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Monitoring, prediction and control of injection molding quality based on tie-bar elongation
Journal of Manufacturing Processes 46 (2019) 159–169
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Monitoring, prediction and control of injection molding quality based on tiebar elongation Jian-Yu Chena, Jia-Xiang Zhuangb, Ming-Shyan Huangb, a b
T
⁎
Bachelor’s Program of Precision System Design, Feng Chia University, 100, Wenhwa Rd., Seatwen Dist., Taichung City 40724, Taiwan Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, 1 University Road, Yanchao Dist., Kaohsiung City 824, Taiwan
ARTICLE INFO
ABSTRACT
Keywords: Clamping force increment Quality monitoring and control Tie bar elongation Injection molding Recycled plastic material
Quality consistency is essential in the injection molding process for maximizing the yield rate and minimizing the production cost, and this issue is more concerned proceeding with recycled material. Effective methods are therefore required for performing the real-time monitoring and control of the injection molded part quality and tuning the injection molding process parameters. Accordingly, the present study proposes a real-time quality monitoring and control system based on the clamping force increment characteristic, as determined by the measured tie-bar elongation. Notably, the proposed method is applicable to both virgin raw materials and recycled plastic materials. The effectiveness of the proposed approach is evaluated for three different processing parameters, namely the barrel temperature, the back pressure and the V/P switchover point. It is shown that the three processing parameters all have a significant effect on the molded part weight and thickness. Moreover, for each parameter, the part weight and thickness are strongly correlated. The clamping force increment is also highly correlated with the molded part weight. It is hence inferred that the clamping force increment provides an appropriate quality index for monitoring the injection molding quality. Furthermore, through the integration with a V/P switchover point calibration model, the clamping force increment index can be used to control the molded part weight to within a small tolerance on a shot-by-shot basis. When performing the injection molding process with virgin raw material, the tensile strength of the molded part increases with an increasing barrel temperature due to an increasing part weight if the proposed quality monitoring and control system is not applied. However, when the proposed system is employed, the tensile strength reduces slightly at a higher barrel temperature. When using recycled material in the injection molding process, the tensile strength remains approximately constant as the barrel temperature increases, irrespective of whether or not the proposed quality control system is applied. The difference in tendencies of the two materials can be attributed to a difference in their viscosities (i.e., molecular weights).
1. Introduction Injection molding is a long-established technique for the mass production of plastic parts. With its advantages of low cost, high efficiency, and ability to produce precise and complex components, injection molding is widely used in many fields nowadays, including consumer electronics, sports goods, automobiles, medical devices, and optical lenses. Thereby, polymers are used in various aspects of everyday life. However, plastic components can take hundreds, if not thousands, of years to decompose, and hence the recycling of plastic waste is a critical environmental concern. In response to this concern, the feasibility for performing injection molding using recycled plastic materials has attracted growing attention in recent years. Due to increasing demand for injection molding quality, a proper setting of main processing ⁎
parameters such as injection speed and pressure, melt and mold temperature, holding pressure and time as well as the cooling rate dominate the results. Particularly, the switching time from mold filling to molding holding, also called V/P switchover point in this research, is crucial in determining part quality. Notably, the control strategy of an injection molding machine to proceed to mold filling and mold holding is distinct. For mold filling, molten polymer is compressed to fill cavities with a constant flow rate beneath a limited injection pressure to protect machine and mold. In contrast, in mold holding, molten polymer fills mold cavities with a constant holding pressure while the holding speed is limited. As for the injection molding of virgin raw materials, the problem of achieving a consistent molded part quality for recycled raw materials poses a significant challenge. Accordingly, an effective online quality monitoring and control method to ensure the
Corresponding author. E-mail addresses: [email protected] (J.-Y. Chen), [email protected] (J.-X. Zhuang), [email protected] (M.-S. Huang).
https://doi.org/10.1016/j.jmapro.2019.09.005 Received 3 December 2018; Received in revised form 24 August 2019; Accepted 3 September 2019 Available online 10 September 2019 1526-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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consistency is urgent. Theoretically, modern all-electric driven injection molding machines provide the ability to achieve the highly-precise and reproducible mass production of plastic components. However, in practice, the injection molding quality inevitably varies from shot-toshot due to intrinsic fluctuations in the melt quality. This attributes to a precise machine control merely assures the stability, repeatability, and in time responses process parameter settings, which is incapable of improve the varying melt quality. Furthermore, current inspection methods for injection molded components tend to focus almost exclusively on the geometrical dimensions and surface quality of the molded part and are performed manually offline using a batch-sampling approach. Hence, the inspection process is not only expensive and timeconsuming, but also lacks the facility to tune the processing parameters adaptively in response to changes in the part quality. Consequently, the problem of developing effective techniques for performing the online monitoring, prediction and control of the injection molding quality has attracted significant attention in the literature in recent years.
ensuring the quality consistency of the molded components still represents a major challenge since, as the proportion of additives added to the recycled material increases, the quality variability of the molded part also increases [5]. Among the various polymers in common use nowadays, acrylonitrile butadiene styrene (ABS) is one of the most widely used. The block made by LEGO is also one of the famous toy manufactured by ABS injection molding. However, the impact strength of ABS reduces with an increasing number of recycling times, or with the use of different blends although some properties insignificantly changes such as young’s modulus, yield stress, tensile strength, bending strength, etc. [6–10]. Notably, the melt quality of recycled plastics is affected not only by the melt temperature, but also by the shear rate [11]. However, the relationship between the mechanical properties of recycled plastic components and the rheological properties of the polymer melt is still unclear. Furthermore, the literature contains scant information on the development of online quality monitoring and control systems for the injection molding of recycled plastic materials. In general, the melt quality is related to the flow behavior of the molten polymer in the barrel and mold cavity, which is difficult to predict in the plasticizing and filling stages of the injection molding process. In the plasticizing stage, the polymer is melted under the combined effects of the energy provided by the barrel heater and the shear effect caused by the rotating screw in the barrel. Hence, the quality of the polymer melt is correlated with both the amount of heat energy absorbed in the compression and feeding zones of the barrel and the shear heat in the metering zone [12–15]. The shear heat depends in turn on the back pressure applied during the plasticizing stage and the screw rotational speed. Modern all-electric driven injection molding machines provide a highly precise and stable motion control. Moreover, they often employ grey prediction techniques to control the V/P switchover point in such a way as to enhance the quality consistency [16]. However, as described above, the melt quality is both variable and intangible and even minor changes in the melt consistency may result in significant variations in the quality of the molded component. In other words, the ability to precisely control the motion of the injection molding machine is insufficient in itself to accommodate the minor changes in the melt quality which may occur even from shot to shot [17]. As a result, it is necessary to develop more effective means of monitoring the melt quality in real time during the molding process
2. Literature review Injection molding process is a complex thermomechanical cycle, and the quality of injection molded parts is strongly dependent on the melt quality (i.e., the viscosity of the molten polymer), where this depends in turn on many and varied factors, as shown in Fig. 1. The problem of quality inconsistency is particularly apparent and more serious in injection molding processes performed using recycled plastic materials since recycled polymer is generally processed at least two times, and hence the rheological properties in the microscale (e.g., the molecular chain length, molecular weight, and molecular distribution) are quite different from those of virgin polymer materials. As a result, the apparent properties of the recycled polymer, e.g., the viscosity, processability, shrinkage, and so on, are also quite different, and further result in different mechanical properties compared to those for raw material injection molding [1]. Therefore, the stability and mechanical properties of injection molded part quality is difficult to monitor and control during recycled polymer injection molding. Traditionally, various additives or fibers may be added to recycled plastic materials in order to enhance their mechanical properties, including minerals, coupling agents, carbon/glass fibers, and so on [2–4]. However,
Fig. 1. Factors affecting injection-molded part quality. 160
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such that the processing parameters can be adaptively modified as required to meet the specified quality requirements. Many studies have shown that an effective control of the pressurevolume-temperature (pVT) path of the polymer melt is essential in improving the quality and consistency of injection molded parts [18–21]. Of these three parameters, the melt quality is determined principally by the temperature and pressure. However, existing quality monitoring and control methods based on the temperature are difficult to implement since the immediacy of heat conduction is not as quick as that of pressure. In other words, heat transfer needs to take time in response. Consequently, pressure-based signals are generally preferred since their response time is relatively quicker than that of temperature signals [22–26]. The quality and reproducibility of injection molded parts is highly correlated with the flow resistance (i.e., viscosity) of the polymer melt. However, the viscosity is traditionally measured off-line and at a low shear rate. As a result, the measurement process not only incurs an inevitable delay, but also differs from the high shear rate conditions encountered in real-world injection processes. Consequently, the viscosity measurements are of little value in turning the injection molding parameters adaptively in response to subtle changes in the melt quality. To address this problem, Gornik [27] proposed an in-machine melt quality measurement system featuring a special sensor installed in the nozzle and designed to detect the volumetric flow rate of polymer melt passing through the nozzle in 10 min at a peculiar temperature and pressure. The resulting volumetric measurement was then converted into a melt index to quantify the viscosity of the polymer melt. Aho and Syrjälä [28] developed a slit die equipped with pressure sensors to evaluate the polymer melt viscosity based on the ratio of the measured pressure gradient to volumetric flow rate. To further assure the consistence of melt quality, Kruppa et al. [29] presented a feedback control method for injection molding machines and molds in which the apparent viscosity of the polymer melt was calculated based on the detected values of the nozzle pressure and temperature, respectively. Asadizanjani and Gordon [30] developed a multivariate sensor for the online detection of the pressure, temperature and velocity of the polymer melt as a means of quantifying and controlling the melt quality. Instead of employing cavity pressure that is invasive to mold cavity, Lin et al. [31] obtained online estimates of the polymer melt viscosity using a pressure sensor bushing mounted around the nozzle. In addition, they also depict the tensile strength of molded component is significantly decreased with increasing injection speed at low injection speed, and then it is also slightly decreased as injection speed set over 75 mm/s. To further realize online quality monitoring, Chen et al. [32] presented a method for evaluating the melt quality of the polymer resin using four quality indices (namely, the peak pressure, the pressure gradient, the viscosity index and the energy index) based on the pressure measurements obtained by three sensors mounted at the nozzle, runner and cavity, respectively. During the melt filling phase, the molten polymer flows into the cavity under high temperature and pressure. As the melt fills the cavity, it is compressed and produces a sudden rise of the cavity pressure, which leads to a mold separation effect. Chen et al. [23,33] exploited this effect to develop a novel V/P switchover method based on the mold separation detected by a linear displacement transducer mounted on the outside of mold plates to monitor the momentary separation of the core and cavity plates. Yin et al. [34] used a back propagation neural network, genetic algorithm, and mold flow simulation method to determine the mold temperature, melt temperature, holding pressure, holding time and cooling time settings which optimized the warpage and clamping force during plastic injection molding. Huang et al. [35] confirmed that a proper setting of the clamping force is essential to enhance the quality and consistency of injection molded parts. The same group [36] additionally proposed a method for determining the appropriate clamping force based on the measured relationship between the tie-bar elongation and the clamping force setting. Zhou et al.
developed a methodology based on ultrasonic measurements to obtain in situ measurements of the clamping force in a non-invasive and nondestructive manner [37,38]. In summary, current injection molding quality monitoring and control methods generally exploit pressure signals detected in the cavity or near the injection mold. However, while these methods provide an effective approach for estimating the molded part quality and tuning the processing parameters accordingly, they incur an additional time and expense in procuring the sensors and installing them in the molding machine. Moreover, an extremely precise positioning of the sensors is essential to prevent damage to the components during the molding process. Accordingly, this study proposes a quality monitoring and control method based on the clamping force increment characteristic, as determined by the measured tie-bar elongation. The feasibility of the proposed method is evaluated experimentally for various values of the barrel temperature, back pressure and V/P switchover point, respectively. Notably, the investigations consider not only virgin raw materials in the injection molding process, but also recycled plastic materials. 3. Control methodology for injection molding quality based on tie-bar elongation In the quality control methodology proposed in the present study, press-on strain sensors enclosed in stainless protective foil are wrapped around the cylindrical surfaces of the four tie bars of the injection molding system in order to measure the surface strain directly at the mounting location. In general, when a body is stretched in the axial direction, the lateral dimensions contract. Conversely, when the body is compressed, the lateral dimensions expand. During the injection molding process, the tie bars stretch in the axial direction during clamping status. In particular, they further suddenly stretch as the polymer fills the cavity and undergoes compression. Consequently, the cylindrical surfaces of the tie bars contract and produce a change in strain which is detected by the sensors. From Hooke’s Law, the stress and strain produced as the tie bars elongate can be derived as follows: i
=E
Fi =E A i
=
(1)
i
(2)
i
Fi EA
(3) th
where i is the stress in the i tie bar in MPa, E is the Young’s modulus of the tie bar material (i.e., 210,000 N/mm2 for the injection molding machine proceeded in the present study), εi is the strain induced in the ith tie bar in micrometers, and A is the cross-sectional area of the tie bars in squared millimeters (i.e., 3848.451 mm2 in the present case). From Eq. (3), the clamping force can be derived as [35]:
Fi = F =
EA i 106 n i=1
(4)
Fi,
(5) th
where Fi and F are the clamping force in the i tie bar and the total clamping force in all four tie bars (in kN), respectively. Note that n is the total number of tie bars and is equal to four in the present case. Furthermore, the denominator of Eq. (4) is used to give the clamping force in units of kN. In the injection molding process, a mold separation effect typically occurs between the end of the filling stage and the end of the holding stage [35]. The extent of the mold separation effect depends on the cavity size and the particular materials employed. Mold separation is often tolerated within a small range (e.g., 75 μm). However, excessive mold separation not only leads to flash on molded part but also reduces the life of both molding machine and injection tool, and must therefore 161
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lower tolerance limits on the nominal weight are specified as 0.005 g. Fig. 3 presents a schematic overview of the proposed online quality control method. The detailed steps in the proposed method are described in the following. Step 1: Define the nominal clamping force increment at a single tie F , increment @SGa and the corresponding tolerance, bar, F , increment @SGa ± 2 . Step 2: If the detected clamping force increment, F , increment @ SGa , does not satisfy the required quality interval, F , increment @SGa ± 2 , trigger the following quality control logic: (a) If F , increment @ SGa > F , increment @SGa + 2 , advance the V/P switchover screw position by 0.2 mm. (b) If F , increment @ SGa < F , increment @SGa 2 , retard the V/P switchover screw position by 0.2 mm.
Fig. 2. Schematic definition of clamping force increment.
be carefully controlled. As shown in Fig. 2, as the mold cavity reaches the point of maximum filling during the polymer filling/holding phase, the polymer melt undergoes compression. The increase in the cavity pressure force causes the tie bars to undergo an instantaneous extension, which prompts a sudden increase in the clamping force. In the present study, the increment in the clamping force is detected by the strain sensors mounted on the four tie bars and is taken as a force-based index of the injection molding quality. In general, the quality of continuous injection molding processes is usually evaluated by comparing the qualities of successive components. The validity of the proposed force-based quality index was evaluated by examining the correlation between the quality indices obtained at each of the monitoring positions (i.e., clamping force increment at each tie bar and the whole clamping force increment) and the associated part qualities (i.e., weight and thickness of molded component). In other words, the correlation index, rq , was computed as
rq =
(x (x
x¯)(y x¯)2
Step 3: Check if the next molded component, with clamping force increment F , increment @ SGa , now satisfies the quality interval F , increment @SGa ± . If the clamping force increment satisfies the required quality interval, terminate the control logic and continue injection molding using the current V/P switchover screw position. Otherwise, apply a linear interpolation/extrapolation model to determine the clamping force increment which satisfies the quality interval F , increment @SGa ± and adjust the V/P switchover point accordingly. Perform V/P switchover calibration iteratively in this way until the clamping force increment, F , increment @SGa , satisfies the quality requirement. In practical injection molding scenarios, the quality monitoring system runs continuously in background mode and is triggered any time the detected clamping force increment fails to meet the defined quality interval in Step 2. Moreover, in implementing Step 1, the nominal clamping force increment at a single tie bar, F , increment @SGa and corresponding tolerance, F , increment @SGa ± 2 (where is the standard deviation) are obtained statistically based on an observation of ten consecutive injection cycles performed under stable production conditions (i.e., the molded components all satisfy the required quality interval).
y¯) (y
y¯) 2
(6)
where x and y are force-based quality index and molded part quality, respectively. Depending on the value obtained for the correlation index, the degree of correlation between the quality index and the associated investigated molded part quality was classified as “strong”, “medium” or “weak”. In the quality control system proposed in this study, the aim is to adjust the V/P switchover point adaptively in accordance with the detected tie bar elongation in such a way as to maintain a constant part weight from shot to shot. For the molded components considered in the present study, the nominal weight is equal to 4.87 g (as determined from the geometry and dimensions of the mold cavity and the density of the injected polymer material). Moreover, the acceptable upper and
4. Experimental setup Fig. 4 presents a photograph of the experimental setup used in the present study. Fig. 5(a) shows the dual-cavity mold used to produce the dumbbell-shaped specimen. Fig. 5(b) and (c) show the detailed dimensions of the specimen and a photograph of the finished component, respectively. As shown in Fig. 5(b), the specimen has a length of 125 mm, an end width of 19 mm, a central width of 13 mm, and a
Fig. 3. Flowchart of proposed quality control method based on measured clamping force increment. 162
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Fig. 4. Photograph of experimental injection molding machine.
thickness of 1.2 mm. The specimen thus conforms to the ASTM D638 standard for the tensile testing of plastics. The injection experiments were performed using two ABS polymer materials (both produced by Chi-Mei Corporation, Taiwan), namely PA756 and PA756H. Both materials have the same recommended processing conditions. However, the melt flow index (MFI) of PA756 is lower than that of PA756H. In other words, PA756 has a higher viscosity than PA756H under the same injection conditions. To demonstrate the practical feasibility of the proposed quality control method, the injection experiments were performed not only with virgin raw material, but also recycled material. As shown in Table 1, two different recycled materials were used, namely PA756 recycled just once (R1) and PA756 recycled three times (R3). Note that in the table, the notation “100%” indicates that the injection molding experiments were performed using only virgin raw material, or only recycled plastic materials (R1 or R3). That is, the experiments were not performed using a mixture of virgin / recycled plastic pellets. All of the experiments were performed using the all-electric injection molding machine (ROBOSHOT S-2000i100B, FANUC, Japan) shown in Fig. 4. The detailed specification of the molding machine is given in Table 2. Fig. 6 presents a schematic illustration of the injection molding system and experimental measurement setup consisting of four strain gauge sensors (SGa, SGb, SGc, and SGd) mounted on the four tie bars of the
molding machine, respectively (see also Fig. 7), a DAQ interface, and a computer. The specifications of the tie bar strain sensors and DAQ card are listed in Table 3. In performing the experiments, the quality of the molded components was evaluated by measuring both the geometrical dimension on thickness of the molded components at points A1, A2, B1 and B2 (Fig. 7) and the part weight. 5. Results and discussions 5.1. Definition of injection molded part quality A series of injection molding experiments was performed to examine the effects of three main processing parameters (namely the barrel temperature, the back pressure and the V/P switchover point) on the polymer melt quality (as determined by the part weight and thickness). Fig. 8 shows the typical variation in the average part weight and average part thickness over 30 continuous shots performed using virgin PA756 raw material and various values of the barrel temperature, back pressure and V/P switchover point in the ranges of 205∼220 °C, 5∼20 MPa, and 14.4∼15.0 mm, respectively. The correlation index rg can be calculated similarly with Eq. (6), where x and y represent the weight and the thickness of molded part, respectively. As expected, a
Fig. 5. (a) Dual-cavity injection mold, (b) geometrical dimensions of dumb-bell specimen, and (c) photograph of injection molded sample. 163
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Table 1 Coding of raw materials used in injection molding experiments based on number of recycling times. Abbreviation
PA756 Raw material
Raw material 100%R1 100%R3
100
PA756 1st recycled material 100
PA756 3rd recycled material
Recycling number
100
0 1 3
Table 2 Specification of injection molding machine. FANUC Roboshot S-2000i100B
Specification
Unit
Injection unit
Screw diameter (mm) Injection stroke (mm) Injection pressure (MPa) Injection speed (mm/s) Clamping force (kN)
28 95 240 330 1000
Clamping unit
high correlation (r = 0.97) exists between the part thickness and the part weight in every case. Consequently, the part weight is inferred to be a reasonable indicator of the molded part quality. 5.2. Effects of injection molding conditions on molded part quality Tables 4 and 5 show the fixed and variable processing parameters employed in the present study. Fig. 9 shows the experimental results obtained for the clamping force increment and part weight under each of the considered experimental conditions. The injected polymer was PA756. Twenty trials were performed under each experimental condition. As shown, the molded part weight increases with an increasing barrel temperature and back pressure, or with a retarding V/P switchover point (i.e., a longer injection stroke). These findings are reasonable since a higher barrel temperature results in a lower viscosity of the polymer melt, and therefore reduces the pressure required to drive the resin into the cavity. In other words, the flow resistance of the polymer melt during the filling and holding stages of the injection cycle reduces with a higher melt temperature. Consequently, the flow and compressive abilities of the polymer are enhanced, and hence the gate-frozen
Fig. 7. Arrangement of strain gauge sensors on tie bars. Table 3 Specifications of sensors and data acquisition card used in proposed quality monitoring and control system. Sensor
Supplier
Type
Tie-bar strain sensor DAQ card
GEFRAN National Instruments
GE1029 USB-6343
Fig. 6. Schematic illustration of injection molding machine and proposed quality monitoring and control system. (Note that dotted lines represent signal flows).
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Fig. 8. Variation of molded part weight and thickness for different injection parameter settings.
barrel temperature and back pressure and 0.94 for the V/P switchover point) regardless the clamping force increment is calculated by single tie bar or the whole clamping force increment. This finding provides a good foundation force-based quality index is highly correlated with molded part weight and could be further engaged to online monitoring, prediction and control of injection molding quality.
Table 4 Fixed parameter settings in injection molding experiments. Fixed parameters Feeding stroke (mm) Screw rotational speed (rpm) Clamping force(kN) Mold temperature ( °C)
40 100 600 60
Injection speed (mm/s) Holding pressure (MPa) Holding time (s) Cooling time (s)
90 105 5 15
5.3. Verification of monitoring system performance for melt quality fluctuation
time is prolonged. As a result, a greater amount of polymer melt is forced into the cavity, and thus this phenomenon reflects on more significant mold separation and the clamping force increment and product weight increase. Regarding the back pressure, a higher back pressure results in a greater density and viscosity of the polymer melt, and therefore increases the pressure required to drive the resin into the cavity. Although changes in the back pressure do not affect the gatefrozen time, the greater amount of polymer melt entering the cavity during the filling stage increases the compression force during the holding stage. As a result, both the clamping force increment and the part weight increase. Finally, a delayed V/P switchover position results in a greater amount of polymer melt flowing into the cavity. Thus, the clamping force increment and part weight are once again both increased. A detailed analysis of the experimental results shows a strong correlation between the clamping force increment and the part weight for each of the considered processing parameters (i.e., 0.99 for the
To further verify the feasibility of the online quality monitoring system mentioned above, a continuous injection molding experiment was performed using PA756 resin for approximately 50 shots followed by the gradual addition of 20% PA756H resin every 50 shots until a condition of 100% PA756H was achieved (see Table 6). Notably, PA756 and PA756H have similar rheological behaviors, but different melt flow indices (MFIs), as described earlier in Section 4. Fig. 10 presents the experimental results for the shot-by-shot variation of the clamping force increment index. (Note that Fig. 10(a) presents the results for F , increment (i.e., the total force increment), while Fig. 10(b) presents the results for F , increment @SGa (i.e., the force increment measured by SGa)). As the PA756 is gradually replaced with PA756H, the viscosity of the polymer melt gradually decreases. Consequently, the amount of molten resin injected into the cavity increases, and hence the clamping force increment and product weight also increase. The part weight is strongly correlated with both clamping increments, i.e., 0.94 for F , increment @SGa and 0.96 for F , increment . In general, the results presented in Fig. 10
Table 5 Variable parameter settings in injection molding experiments. Shot
Back pressure (MPa)
V/P switching position (mm)
10 10 10 10
15 15 15 15
Back pressure variation 5 210 6 210 7 210 8 210
5 10 15 20
15 15 15 15
V/P switching position variation 9 210 10 210 11 210 12 210
10 10 10 10
14.4 14.6 14.8 15.0
Barrel 1 2 3 4
Barrel temperature ( °C) temperature variation 205 210 215 220
Fig. 9. Correlation between force-based quality index and molded part weight for different injection parameter settings. 165
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Table 7 summarizes the experimental results for the mean weight, range and standard deviation of the molded specimens produced using the three different raw materials with and without the quality control system, respectively. The results show that for all three materials, the range and standard deviation values of the products produced using the proposed quality control system are all lower than those produced without the quality control system with the exception of the parts produced using 100%R3 in shots 72-100. The result attributes to larger melt quality difference for 100% R3 recycled material compared to raw material. In general, the molded part weights for all injection cases are qualified with engaging quality control system as melt quality of molten resin fluctuates with rising barrel temperature. Hence, the revealed methodology based on tie bar elongation characteristic is effective to part weight control in injection molding Figs. 12 and 13 show the maximum tensile strengths and weights of the products produced using the three different raw materials and two barrel temperatures (210 and 215 °C) without and with the proposed quality control system, respectively. For the virgin raw material, the tensile strength increases with an increasing barrel temperature when the control system is not employed due to a corresponding increase in the part weight (i.e., part thickness). However, when the control system is applied, the tensile strength reduces slightly as the barrel temperature is increased despite the minor increase in the part weight. For the recycled raw materials, the product weight increases with an increasing barrel temperature in the uncontrolled condition, but remains approximately the same when the control system is employed. However, in contrast to the virgin raw material, the tensile strength of the molded components is insensitive to both the barrel temperature and the use (or not) of the proposed control system. The difference in tendency between the recycled materials and the virgin material can be attributed to a difference in their respective viscosities, as shown in Fig. 14. In particular, the viscosities of the two recycled materials are lower than that of the virgin raw material over the entire shear rate range for both barrel temperatures (210 °C and 215 °C). Moreover, the viscosities of the two recycled materials are very similar to one another. Hence, the tensile strengths of the products produced using the recycled materials are not only slightly lower than those of the products produced using the virgin raw material, but also similar to one another since a similar amount of molten resin enters the cavity in the filling/holding stage in both cases, irrespective of the barrel temperature and application (or otherwise) of the quality control system. Overall, the results presented in Figs. 12–14 indicate that the tensile strength of the molded components is determined principally by the viscosity when the molded part weight is controlled to within a small tolerance.
Table 6 Process parameter settings used in injection molding experiments with PA756/ PA756H. Fixed parameters Barrel temperature ( °C) 210 screw rotational speed (rpm) 100 Back pressure (MPa) 10 Feeding stroke (mm) 40 Clamping force(kN) 600 Mold temperature ( °C) 60 Varying parameter (Blended materials) % of PA756 / % of PA756H 100/0,
Injection speed (mm/s) V/P switch (mm) Holding pressure (MPa) Holding time (s) Cooling time (s)
90 15 105 5 15
80/20, 60/40, 40/60, 20/80, 0/100
therefore confirm the ability of the proposed force-based quality index method to reveal crude changes in the viscosity of the polymer melt induced by deliberate changes in the raw material, and this result could further verify the feasibility of the online quality monitoring method based on the tie bar elongation characteristic. 5.4. Verification of part weight control and property comparison for virgin and recycled raw materials The product quality fluctuation caused by melt quality inconsistencies is particularly apparent in the injection molding of recycled plastic materials [1]. Accordingly, the feasibility of the quality control method proposed in the present study was further evaluated by means of 100-shot continuous injection experiments performed using both virgin raw material (PA756) and its recycled raw materials (R1 and R3). For each raw material, the injection process was performed using a barrel temperature of 210 °C for the first 50 shots and a temperature of 215 °C for the remaining 50 shots. In addition, the experiments were performed both with and without the proposed quality control system, respectively. Fig. 11 presents the corresponding results for the clamping force increment at SGa and part weight in every case. As discussed previously, a higher barrel temperature enhances the flow ability of the polymer resin in the filling stage. Thus, for all of the raw materials, the molded part weight and clamping force increment increase with a higher barrel temperature due to the greater amount of resin which enters the cavity during the filling stage. For the cases where the proposed quality control system is not employed (i.e., Fig. 11(a), (c), and (e)), the product weight exceeds the acceptable quality range (shown by the orange block); even at the lower barrel temperature of 210 °C. However, when the quality control system is applied (i.e., Figs. 11(b), (d), and (f)), the molded part weight generally falls within the required quality interval, irrespective of the barrel temperature.
Fig. 10. Variation of clamping force increment and molded part weight over 400-shot injection molding process using PA756 gradually replaced with PA756H every 50 shots.
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Fig. 11. Variation of clamping force increment and molded part weight over 100-shot injection molding process performed at two different barrel temperatures (210 °C and 215 °C): (a) virgin raw material with no quality control; (b) virgin raw material with quality control; (c) recycled plastic material (R1) with no quality control; (d) recycled plastic material (R1) with quality control; (e) recycled plastic material (R3) with no quality control; (f) recycled plastic material (R3) with quality control. (Note that thin dashed lines represent the upper and lower limits on the clamping force increment, and orange block represents the required quality interval of the molded components).
Table 7 Comparison of part weights with and without quality control for virgin and recycled injection molded parts. Polymer Shot number With no quality control With quality control
Raw material Avg. of weight Range Standard deviation Avg. of weight Range Standard deviation
1-50 4.8694 0.0081 0.0019 4.8676 0.0050 0.0011
100% R1 61-100 4.8822 0.0051 0.0013 4.8688 0.0023 0.0005
6. Conclusions
1-50 4.8714 0.0102 0.0032 4.8696 0.0080 0.0017
100% R3 61-100 4.8814 0.0098 0.0021 4.8683 0.0067 0.0013
1-50 4.8714 0.0084 0.0019 4.8705 0.0083 0.0017
72-100 4.8832 0.0055 0.0015 4.8700 0.0067 0.0018
Consequently, effective online methods for monitoring the polymer melt viscosity and adjusting the processing parameters adaptively are required to ensure the quality consistency of the final molded products, especially proceeding with recycled materials. This study has therefore proposed a quality monitoring method based on the tie bar elongation detected during the molding cycle by simple press-on strain gauges mounted directly on the tie bars. In addition, the V/P calibration procedure revealed in this study is engaged to control molded part weight according to the tie bar elongation profile. The experimental results support the following main conclusions.
The mechanical and physical properties of injection molded components are critically dependent on the viscosity of the polymer melt. However, the polymer materials used in injection molding have a complex rheological behavior, which varies from cycle to cycle under the effects of many extrinsic factors, such as the temperature, pressure and shear rate during the plasticization, filling and holding stages, and so on. Furthermore, the rheological properties of recycled plastic materials are quite different from those of virgin raw materials. 167
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Fig. 12. Tensile strength and product weight of virgin and recycled injection molded parts with no quality control.
Fig. 13. Tensile strength and product weight of virgin and recycled injection molded parts with quality control.
Fig. 14. Variation of viscosity with shear rate for raw and recycled materials at temperatures of: (a) 210 °C and (b) 215 °C.
(1) The force-based quality index derived from the measured tie bar elongation provides a reliable indicator of the molded part weight. Notably, the proposed method is also effective for the injection molding of recycled materials. (2) The barrel temperature, back pressure and V/P switchover point all have an obvious effect on the molded part weight and thickness. The V/P switchover point is particularly strongly correlated with the clamping force increment and part weight, and thus provides an ideal basis for the implementation of the proposed quality control system. Accordingly, the quality control methodology based on tie bar elongation characteristic revealed in this study is successfully verified with its effectiveness. The part weight injected regardless raw and recycled materials is well controlled.
(3) The proposed quality control system achieves an effective control of the part weight for both virgin raw material and recycled raw materials. However, the tensile strengths of the recycled plastic parts are slightly lower than those of the virgin raw material parts due to their lower viscosity (i.e., molecular weight) under the considered shear rate range. (4) Overall, the results presented in this study confirm that the proposed quality control system provides a simple, reliable and noninvasive technique for accommodating intrinsic changes in the polymer melt quality during the injection molding process and improving the quality consistency of the molded products as a result.
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Acknowledgement [15]
This work was supported in part by the Frontier Mould and Die Research and Development Center of Taiwan under The Featured Areas Research Center Program within the Higher Education Sprout Project of the Ministry of Education (MOE), Taiwan. The additional funding provided by the Ministry of Science and Technology, Taiwan, under Project number 106-2218-E-327-002 -MY2is gratefully acknowledged.
[16] [17] [18] [19]
Declaration of Competing Interest
[20]
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
[21] [22]
References
[23] [24]
[1] Hamad K, Kaseem M, Deri F. Recycling of waste from polymer materials: an overview of the recent works. Polym Degrad Stab 2013;98:2801–12. [2] Yoshida T, Ishiaku US, Okumura H, Baba S, Hamada H. The effect of molecular weight on the interfacial properties of GF/PP injection molded composites. Compos Part A Appl Sci Manuf 2006;37:2300–6. [3] Gu F, Hall P, Miles NJ. Performance evaluation for composites based on recycled polypropylene using principal component analysis and cluster analysis. J Cleaner Prod 2016;115:343–53. [4] Gu F, Hall P, Miles NJ. Development of composites based on recycled polypropylene for injection moulding automobile parts using hierarchical clustering analysis and principal component estimate. J Cleaner Prod 2016;137:632–43. [5] Khademi F, Ma Y, Ayranci C, Choi K, Duke K. Effects of recycling on the mechanical behavior of polypropylene at room temperature through statistical analysis method. Polym Eng Sci 2016;56:1283–90. [6] Bai XJ, Isaac DH, Smith K. Reprocessing acrylonitrile-butadiene-sytrene plastics: structure-property relationships. Polym Eng Sci 2007;47:120–30. [7] Pérez JM, Vilas JL, Laza JM, Arnáiz S, Mijangos F, Bilbao E, et al. Effect of reprocessing and accelerated weathering on ABS properties. J Polym Environ 2010;18:71–8. [8] Chen SC, Liao WH, Hsieh MW, Chien RD, Lin SH. Influence of recycled ABS added to virgin polymers on the physical, mechanical properties and molding characteristics. Polym Plast Technol Eng 2011;50:306–11. [9] Scaffaro R, Botta L, Benedetto GD. Physical properties of virgin-recycled ABS blends: effect of post-consumer content and of reprocessing cycles. Eur Polym J 2012;48:637–48. [10] Hamarat I, Kuram E, Ozcelik B. Investigation the mechanical, rheological, and morphological properties of acrylonitrile butadiene styrene blends with different recycling number content. J Process Mech Eng 2017;232:449–58. [11] Boronat J, Segui VJ, Peydro MA, Reig MJ. Influence of temperature and shear rate on the rheology and processability of reprocessed ABS in injection molding process. J Mater Process Technol 2009;209:2735–45. [12] Amano O, Utsugi S. Temperature measurements of polymer melts in the heating barrel during injection molding, part 1. Temperature distribution along the screw axis in the reservoir. Polym Eng Sci 1988;28:1565–71. [13] Amano O, Utsugi S. Temperature measurements of polymer melts in the heating barrel during injection molding, part 2: three-dimensional temperature distribution in the reservoir. Polym Eng Sci 1989;29:171–7. [14] Amano O, Utsugi S. Temperature measurements of polymer melts in the heating
[25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]
169
barrel during injection molding, part 3: effects of screw geometry. Polym Eng Sci 1990;30:385–93. Zhou X, Zhang Y, Mao T, Zhou H. Monitoring and dynamic control of quality stability for injection molding process. J Mater Process Technol 2017;249:358–66. Huang MS. Cavity pressure based grey prediction of the filling-to-packing switchover point for injection molding. J Mater Process Technol 2007;183:419–24. Chen Z, Turng LS. A review of current developments in process and quality control for injection molding. Adv Polym Technol 2005;24:165–82. Zuidema H, Peters GWM, Meijer HEH. Influence of cooling rate on pVT‐ data of semicrystalline polymers. J Appl Polym Sci 2001;82:1170–86. van der Beek MHE, Peters GWM, Meijer HEH. A dilatometer to measure the influence of cooling rate and melt shearing on specific volume. Int Polym Process J Polym Process Soc 2005;20:111–20. van der Beek MHE, Peters GWM, Meijer HEH. The influence of cooling rate on the specific volume of isotactic poly(propylene) at elevated pressures. Macromol Mater Eng 2005;290:443–55. Wang J, Mao Q. A novel process control methodology based on the PVT behavior of polymer for injection molding. Adv Polym Technol 2013;32:E474–85. Min BH. A study on quality monitoring of injection-molded parts. J Mater Process Technol 2003;136:1–6. Chen Z, Turng LS. Injection molding quality control by integrating weight feedback into a cascade closed‐ loop control system. Polym Eng Sci 2007;47:852–62. Michaeli W, Schreiber A. Online control of the injection molding process based on process variables. Adv Polym Technol 2009;28:65–76. Hopmann C, Reßmann A. Self-optimizing in injection molding and the problem at compensating viscosity fluctuations. Proc SPE-ANTEC 2014;2:1706–10. Las Vegas, NV, USA. Zhang JF, Zhao P, Zhao Y, Huang JY, Xia N, Fu JZ. On-line measurement of cavity pressure during injection molding via ultrasonic investigation of tie bar. Sens Actuators A Phys 2019;285:118–26. Gornik C. Viscosity measuring methods for feedstocks directly on injection molding machines. Mater Sci Forum 2008;591-593:174–8. Aho J, Syrjälä S. Shear viscosity measurements of polymer melts using injection molding machine adjustable slit die. Polym Test 2011;30:595–601. Kruppa S, Schiffers R, Würtele M, Holzinger GP. Integrated process monitoring and process control of injection molding machines and molds. Proc SPE-ANTEC 2013;1:1674–9. Cincinnati, OH, USA. Gordon G, Kazmer DO, Tang X, Fan Z, Gao RX. Quality control using a multivariate injection molding sensor. Inter J Adv Manuf Technol 2015;78:1381–91. Lin CC, Wang WT, Kuo CC, Wu CL. Experimental and theoretical study of melt viscosity in injection process. Inter J Chem Mol Nucl Mater Metall Eng 2014;8:687–91. Chen JY, Yang KJ, Huang MS. Online quality monitoring of molten resin in injection molding. Int J Heat Mass Transf - Theory Appl 2018;122:681–93. Chen Z, Turng LS, Wang KK. Adaptive online quality control for injection‐ molding by monitoring and controlling mold separation. Polym Eng Sci 2006;46:569–80. Yin F, Mao H, Hua L. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Mater Design 2011;32:3457–64. Huang MS, Nian SC, Chen JY, Lin CY. Influence of clamping force on tie-bar elongation, mold separation, and part dimensions in injection molding. Precis Eng 2018;51:647–58. Huang MS, Lin CY. A novel clamping force searching method based on sensing tiebar elongation for injection molding. Int J Heat Mass Transf - Theory Appl 2017;109:223–30. Zhao Y, Zhao P, Zhang JF, Huang JY, Xia N, Fu JZ. On-line measurement of clamping force for injection molding machine using ultrasonic technology. Ultrason 2019;91:170–9. Zhao P, Zhao Y, Zhang JF, Huang JY, Xia N, Fu JZ. Ultrasonic measurement of clamping force for injection molding machine. J Polym Eng 2019;39:388–96.
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Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro
Monitoring, prediction and control of injection molding quality based on tiebar elongation Jian-Yu Chena, Jia-Xiang Zhuangb, Ming-Shyan Huangb, a b
T
⁎
Bachelor’s Program of Precision System Design, Feng Chia University, 100, Wenhwa Rd., Seatwen Dist., Taichung City 40724, Taiwan Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, 1 University Road, Yanchao Dist., Kaohsiung City 824, Taiwan
ARTICLE INFO
ABSTRACT
Keywords: Clamping force increment Quality monitoring and control Tie bar elongation Injection molding Recycled plastic material
Quality consistency is essential in the injection molding process for maximizing the yield rate and minimizing the production cost, and this issue is more concerned proceeding with recycled material. Effective methods are therefore required for performing the real-time monitoring and control of the injection molded part quality and tuning the injection molding process parameters. Accordingly, the present study proposes a real-time quality monitoring and control system based on the clamping force increment characteristic, as determined by the measured tie-bar elongation. Notably, the proposed method is applicable to both virgin raw materials and recycled plastic materials. The effectiveness of the proposed approach is evaluated for three different processing parameters, namely the barrel temperature, the back pressure and the V/P switchover point. It is shown that the three processing parameters all have a significant effect on the molded part weight and thickness. Moreover, for each parameter, the part weight and thickness are strongly correlated. The clamping force increment is also highly correlated with the molded part weight. It is hence inferred that the clamping force increment provides an appropriate quality index for monitoring the injection molding quality. Furthermore, through the integration with a V/P switchover point calibration model, the clamping force increment index can be used to control the molded part weight to within a small tolerance on a shot-by-shot basis. When performing the injection molding process with virgin raw material, the tensile strength of the molded part increases with an increasing barrel temperature due to an increasing part weight if the proposed quality monitoring and control system is not applied. However, when the proposed system is employed, the tensile strength reduces slightly at a higher barrel temperature. When using recycled material in the injection molding process, the tensile strength remains approximately constant as the barrel temperature increases, irrespective of whether or not the proposed quality control system is applied. The difference in tendencies of the two materials can be attributed to a difference in their viscosities (i.e., molecular weights).
1. Introduction Injection molding is a long-established technique for the mass production of plastic parts. With its advantages of low cost, high efficiency, and ability to produce precise and complex components, injection molding is widely used in many fields nowadays, including consumer electronics, sports goods, automobiles, medical devices, and optical lenses. Thereby, polymers are used in various aspects of everyday life. However, plastic components can take hundreds, if not thousands, of years to decompose, and hence the recycling of plastic waste is a critical environmental concern. In response to this concern, the feasibility for performing injection molding using recycled plastic materials has attracted growing attention in recent years. Due to increasing demand for injection molding quality, a proper setting of main processing ⁎
parameters such as injection speed and pressure, melt and mold temperature, holding pressure and time as well as the cooling rate dominate the results. Particularly, the switching time from mold filling to molding holding, also called V/P switchover point in this research, is crucial in determining part quality. Notably, the control strategy of an injection molding machine to proceed to mold filling and mold holding is distinct. For mold filling, molten polymer is compressed to fill cavities with a constant flow rate beneath a limited injection pressure to protect machine and mold. In contrast, in mold holding, molten polymer fills mold cavities with a constant holding pressure while the holding speed is limited. As for the injection molding of virgin raw materials, the problem of achieving a consistent molded part quality for recycled raw materials poses a significant challenge. Accordingly, an effective online quality monitoring and control method to ensure the
Corresponding author. E-mail addresses: [email protected] (J.-Y. Chen), [email protected] (J.-X. Zhuang), [email protected] (M.-S. Huang).
https://doi.org/10.1016/j.jmapro.2019.09.005 Received 3 December 2018; Received in revised form 24 August 2019; Accepted 3 September 2019 Available online 10 September 2019 1526-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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consistency is urgent. Theoretically, modern all-electric driven injection molding machines provide the ability to achieve the highly-precise and reproducible mass production of plastic components. However, in practice, the injection molding quality inevitably varies from shot-toshot due to intrinsic fluctuations in the melt quality. This attributes to a precise machine control merely assures the stability, repeatability, and in time responses process parameter settings, which is incapable of improve the varying melt quality. Furthermore, current inspection methods for injection molded components tend to focus almost exclusively on the geometrical dimensions and surface quality of the molded part and are performed manually offline using a batch-sampling approach. Hence, the inspection process is not only expensive and timeconsuming, but also lacks the facility to tune the processing parameters adaptively in response to changes in the part quality. Consequently, the problem of developing effective techniques for performing the online monitoring, prediction and control of the injection molding quality has attracted significant attention in the literature in recent years.
ensuring the quality consistency of the molded components still represents a major challenge since, as the proportion of additives added to the recycled material increases, the quality variability of the molded part also increases [5]. Among the various polymers in common use nowadays, acrylonitrile butadiene styrene (ABS) is one of the most widely used. The block made by LEGO is also one of the famous toy manufactured by ABS injection molding. However, the impact strength of ABS reduces with an increasing number of recycling times, or with the use of different blends although some properties insignificantly changes such as young’s modulus, yield stress, tensile strength, bending strength, etc. [6–10]. Notably, the melt quality of recycled plastics is affected not only by the melt temperature, but also by the shear rate [11]. However, the relationship between the mechanical properties of recycled plastic components and the rheological properties of the polymer melt is still unclear. Furthermore, the literature contains scant information on the development of online quality monitoring and control systems for the injection molding of recycled plastic materials. In general, the melt quality is related to the flow behavior of the molten polymer in the barrel and mold cavity, which is difficult to predict in the plasticizing and filling stages of the injection molding process. In the plasticizing stage, the polymer is melted under the combined effects of the energy provided by the barrel heater and the shear effect caused by the rotating screw in the barrel. Hence, the quality of the polymer melt is correlated with both the amount of heat energy absorbed in the compression and feeding zones of the barrel and the shear heat in the metering zone [12–15]. The shear heat depends in turn on the back pressure applied during the plasticizing stage and the screw rotational speed. Modern all-electric driven injection molding machines provide a highly precise and stable motion control. Moreover, they often employ grey prediction techniques to control the V/P switchover point in such a way as to enhance the quality consistency [16]. However, as described above, the melt quality is both variable and intangible and even minor changes in the melt consistency may result in significant variations in the quality of the molded component. In other words, the ability to precisely control the motion of the injection molding machine is insufficient in itself to accommodate the minor changes in the melt quality which may occur even from shot to shot [17]. As a result, it is necessary to develop more effective means of monitoring the melt quality in real time during the molding process
2. Literature review Injection molding process is a complex thermomechanical cycle, and the quality of injection molded parts is strongly dependent on the melt quality (i.e., the viscosity of the molten polymer), where this depends in turn on many and varied factors, as shown in Fig. 1. The problem of quality inconsistency is particularly apparent and more serious in injection molding processes performed using recycled plastic materials since recycled polymer is generally processed at least two times, and hence the rheological properties in the microscale (e.g., the molecular chain length, molecular weight, and molecular distribution) are quite different from those of virgin polymer materials. As a result, the apparent properties of the recycled polymer, e.g., the viscosity, processability, shrinkage, and so on, are also quite different, and further result in different mechanical properties compared to those for raw material injection molding [1]. Therefore, the stability and mechanical properties of injection molded part quality is difficult to monitor and control during recycled polymer injection molding. Traditionally, various additives or fibers may be added to recycled plastic materials in order to enhance their mechanical properties, including minerals, coupling agents, carbon/glass fibers, and so on [2–4]. However,
Fig. 1. Factors affecting injection-molded part quality. 160
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such that the processing parameters can be adaptively modified as required to meet the specified quality requirements. Many studies have shown that an effective control of the pressurevolume-temperature (pVT) path of the polymer melt is essential in improving the quality and consistency of injection molded parts [18–21]. Of these three parameters, the melt quality is determined principally by the temperature and pressure. However, existing quality monitoring and control methods based on the temperature are difficult to implement since the immediacy of heat conduction is not as quick as that of pressure. In other words, heat transfer needs to take time in response. Consequently, pressure-based signals are generally preferred since their response time is relatively quicker than that of temperature signals [22–26]. The quality and reproducibility of injection molded parts is highly correlated with the flow resistance (i.e., viscosity) of the polymer melt. However, the viscosity is traditionally measured off-line and at a low shear rate. As a result, the measurement process not only incurs an inevitable delay, but also differs from the high shear rate conditions encountered in real-world injection processes. Consequently, the viscosity measurements are of little value in turning the injection molding parameters adaptively in response to subtle changes in the melt quality. To address this problem, Gornik [27] proposed an in-machine melt quality measurement system featuring a special sensor installed in the nozzle and designed to detect the volumetric flow rate of polymer melt passing through the nozzle in 10 min at a peculiar temperature and pressure. The resulting volumetric measurement was then converted into a melt index to quantify the viscosity of the polymer melt. Aho and Syrjälä [28] developed a slit die equipped with pressure sensors to evaluate the polymer melt viscosity based on the ratio of the measured pressure gradient to volumetric flow rate. To further assure the consistence of melt quality, Kruppa et al. [29] presented a feedback control method for injection molding machines and molds in which the apparent viscosity of the polymer melt was calculated based on the detected values of the nozzle pressure and temperature, respectively. Asadizanjani and Gordon [30] developed a multivariate sensor for the online detection of the pressure, temperature and velocity of the polymer melt as a means of quantifying and controlling the melt quality. Instead of employing cavity pressure that is invasive to mold cavity, Lin et al. [31] obtained online estimates of the polymer melt viscosity using a pressure sensor bushing mounted around the nozzle. In addition, they also depict the tensile strength of molded component is significantly decreased with increasing injection speed at low injection speed, and then it is also slightly decreased as injection speed set over 75 mm/s. To further realize online quality monitoring, Chen et al. [32] presented a method for evaluating the melt quality of the polymer resin using four quality indices (namely, the peak pressure, the pressure gradient, the viscosity index and the energy index) based on the pressure measurements obtained by three sensors mounted at the nozzle, runner and cavity, respectively. During the melt filling phase, the molten polymer flows into the cavity under high temperature and pressure. As the melt fills the cavity, it is compressed and produces a sudden rise of the cavity pressure, which leads to a mold separation effect. Chen et al. [23,33] exploited this effect to develop a novel V/P switchover method based on the mold separation detected by a linear displacement transducer mounted on the outside of mold plates to monitor the momentary separation of the core and cavity plates. Yin et al. [34] used a back propagation neural network, genetic algorithm, and mold flow simulation method to determine the mold temperature, melt temperature, holding pressure, holding time and cooling time settings which optimized the warpage and clamping force during plastic injection molding. Huang et al. [35] confirmed that a proper setting of the clamping force is essential to enhance the quality and consistency of injection molded parts. The same group [36] additionally proposed a method for determining the appropriate clamping force based on the measured relationship between the tie-bar elongation and the clamping force setting. Zhou et al.
developed a methodology based on ultrasonic measurements to obtain in situ measurements of the clamping force in a non-invasive and nondestructive manner [37,38]. In summary, current injection molding quality monitoring and control methods generally exploit pressure signals detected in the cavity or near the injection mold. However, while these methods provide an effective approach for estimating the molded part quality and tuning the processing parameters accordingly, they incur an additional time and expense in procuring the sensors and installing them in the molding machine. Moreover, an extremely precise positioning of the sensors is essential to prevent damage to the components during the molding process. Accordingly, this study proposes a quality monitoring and control method based on the clamping force increment characteristic, as determined by the measured tie-bar elongation. The feasibility of the proposed method is evaluated experimentally for various values of the barrel temperature, back pressure and V/P switchover point, respectively. Notably, the investigations consider not only virgin raw materials in the injection molding process, but also recycled plastic materials. 3. Control methodology for injection molding quality based on tie-bar elongation In the quality control methodology proposed in the present study, press-on strain sensors enclosed in stainless protective foil are wrapped around the cylindrical surfaces of the four tie bars of the injection molding system in order to measure the surface strain directly at the mounting location. In general, when a body is stretched in the axial direction, the lateral dimensions contract. Conversely, when the body is compressed, the lateral dimensions expand. During the injection molding process, the tie bars stretch in the axial direction during clamping status. In particular, they further suddenly stretch as the polymer fills the cavity and undergoes compression. Consequently, the cylindrical surfaces of the tie bars contract and produce a change in strain which is detected by the sensors. From Hooke’s Law, the stress and strain produced as the tie bars elongate can be derived as follows: i
=E
Fi =E A i
=
(1)
i
(2)
i
Fi EA
(3) th
where i is the stress in the i tie bar in MPa, E is the Young’s modulus of the tie bar material (i.e., 210,000 N/mm2 for the injection molding machine proceeded in the present study), εi is the strain induced in the ith tie bar in micrometers, and A is the cross-sectional area of the tie bars in squared millimeters (i.e., 3848.451 mm2 in the present case). From Eq. (3), the clamping force can be derived as [35]:
Fi = F =
EA i 106 n i=1
(4)
Fi,
(5) th
where Fi and F are the clamping force in the i tie bar and the total clamping force in all four tie bars (in kN), respectively. Note that n is the total number of tie bars and is equal to four in the present case. Furthermore, the denominator of Eq. (4) is used to give the clamping force in units of kN. In the injection molding process, a mold separation effect typically occurs between the end of the filling stage and the end of the holding stage [35]. The extent of the mold separation effect depends on the cavity size and the particular materials employed. Mold separation is often tolerated within a small range (e.g., 75 μm). However, excessive mold separation not only leads to flash on molded part but also reduces the life of both molding machine and injection tool, and must therefore 161
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lower tolerance limits on the nominal weight are specified as 0.005 g. Fig. 3 presents a schematic overview of the proposed online quality control method. The detailed steps in the proposed method are described in the following. Step 1: Define the nominal clamping force increment at a single tie F , increment @SGa and the corresponding tolerance, bar, F , increment @SGa ± 2 . Step 2: If the detected clamping force increment, F , increment @ SGa , does not satisfy the required quality interval, F , increment @SGa ± 2 , trigger the following quality control logic: (a) If F , increment @ SGa > F , increment @SGa + 2 , advance the V/P switchover screw position by 0.2 mm. (b) If F , increment @ SGa < F , increment @SGa 2 , retard the V/P switchover screw position by 0.2 mm.
Fig. 2. Schematic definition of clamping force increment.
be carefully controlled. As shown in Fig. 2, as the mold cavity reaches the point of maximum filling during the polymer filling/holding phase, the polymer melt undergoes compression. The increase in the cavity pressure force causes the tie bars to undergo an instantaneous extension, which prompts a sudden increase in the clamping force. In the present study, the increment in the clamping force is detected by the strain sensors mounted on the four tie bars and is taken as a force-based index of the injection molding quality. In general, the quality of continuous injection molding processes is usually evaluated by comparing the qualities of successive components. The validity of the proposed force-based quality index was evaluated by examining the correlation between the quality indices obtained at each of the monitoring positions (i.e., clamping force increment at each tie bar and the whole clamping force increment) and the associated part qualities (i.e., weight and thickness of molded component). In other words, the correlation index, rq , was computed as
rq =
(x (x
x¯)(y x¯)2
Step 3: Check if the next molded component, with clamping force increment F , increment @ SGa , now satisfies the quality interval F , increment @SGa ± . If the clamping force increment satisfies the required quality interval, terminate the control logic and continue injection molding using the current V/P switchover screw position. Otherwise, apply a linear interpolation/extrapolation model to determine the clamping force increment which satisfies the quality interval F , increment @SGa ± and adjust the V/P switchover point accordingly. Perform V/P switchover calibration iteratively in this way until the clamping force increment, F , increment @SGa , satisfies the quality requirement. In practical injection molding scenarios, the quality monitoring system runs continuously in background mode and is triggered any time the detected clamping force increment fails to meet the defined quality interval in Step 2. Moreover, in implementing Step 1, the nominal clamping force increment at a single tie bar, F , increment @SGa and corresponding tolerance, F , increment @SGa ± 2 (where is the standard deviation) are obtained statistically based on an observation of ten consecutive injection cycles performed under stable production conditions (i.e., the molded components all satisfy the required quality interval).
y¯) (y
y¯) 2
(6)
where x and y are force-based quality index and molded part quality, respectively. Depending on the value obtained for the correlation index, the degree of correlation between the quality index and the associated investigated molded part quality was classified as “strong”, “medium” or “weak”. In the quality control system proposed in this study, the aim is to adjust the V/P switchover point adaptively in accordance with the detected tie bar elongation in such a way as to maintain a constant part weight from shot to shot. For the molded components considered in the present study, the nominal weight is equal to 4.87 g (as determined from the geometry and dimensions of the mold cavity and the density of the injected polymer material). Moreover, the acceptable upper and
4. Experimental setup Fig. 4 presents a photograph of the experimental setup used in the present study. Fig. 5(a) shows the dual-cavity mold used to produce the dumbbell-shaped specimen. Fig. 5(b) and (c) show the detailed dimensions of the specimen and a photograph of the finished component, respectively. As shown in Fig. 5(b), the specimen has a length of 125 mm, an end width of 19 mm, a central width of 13 mm, and a
Fig. 3. Flowchart of proposed quality control method based on measured clamping force increment. 162
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Fig. 4. Photograph of experimental injection molding machine.
thickness of 1.2 mm. The specimen thus conforms to the ASTM D638 standard for the tensile testing of plastics. The injection experiments were performed using two ABS polymer materials (both produced by Chi-Mei Corporation, Taiwan), namely PA756 and PA756H. Both materials have the same recommended processing conditions. However, the melt flow index (MFI) of PA756 is lower than that of PA756H. In other words, PA756 has a higher viscosity than PA756H under the same injection conditions. To demonstrate the practical feasibility of the proposed quality control method, the injection experiments were performed not only with virgin raw material, but also recycled material. As shown in Table 1, two different recycled materials were used, namely PA756 recycled just once (R1) and PA756 recycled three times (R3). Note that in the table, the notation “100%” indicates that the injection molding experiments were performed using only virgin raw material, or only recycled plastic materials (R1 or R3). That is, the experiments were not performed using a mixture of virgin / recycled plastic pellets. All of the experiments were performed using the all-electric injection molding machine (ROBOSHOT S-2000i100B, FANUC, Japan) shown in Fig. 4. The detailed specification of the molding machine is given in Table 2. Fig. 6 presents a schematic illustration of the injection molding system and experimental measurement setup consisting of four strain gauge sensors (SGa, SGb, SGc, and SGd) mounted on the four tie bars of the
molding machine, respectively (see also Fig. 7), a DAQ interface, and a computer. The specifications of the tie bar strain sensors and DAQ card are listed in Table 3. In performing the experiments, the quality of the molded components was evaluated by measuring both the geometrical dimension on thickness of the molded components at points A1, A2, B1 and B2 (Fig. 7) and the part weight. 5. Results and discussions 5.1. Definition of injection molded part quality A series of injection molding experiments was performed to examine the effects of three main processing parameters (namely the barrel temperature, the back pressure and the V/P switchover point) on the polymer melt quality (as determined by the part weight and thickness). Fig. 8 shows the typical variation in the average part weight and average part thickness over 30 continuous shots performed using virgin PA756 raw material and various values of the barrel temperature, back pressure and V/P switchover point in the ranges of 205∼220 °C, 5∼20 MPa, and 14.4∼15.0 mm, respectively. The correlation index rg can be calculated similarly with Eq. (6), where x and y represent the weight and the thickness of molded part, respectively. As expected, a
Fig. 5. (a) Dual-cavity injection mold, (b) geometrical dimensions of dumb-bell specimen, and (c) photograph of injection molded sample. 163
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Table 1 Coding of raw materials used in injection molding experiments based on number of recycling times. Abbreviation
PA756 Raw material
Raw material 100%R1 100%R3
100
PA756 1st recycled material 100
PA756 3rd recycled material
Recycling number
100
0 1 3
Table 2 Specification of injection molding machine. FANUC Roboshot S-2000i100B
Specification
Unit
Injection unit
Screw diameter (mm) Injection stroke (mm) Injection pressure (MPa) Injection speed (mm/s) Clamping force (kN)
28 95 240 330 1000
Clamping unit
high correlation (r = 0.97) exists between the part thickness and the part weight in every case. Consequently, the part weight is inferred to be a reasonable indicator of the molded part quality. 5.2. Effects of injection molding conditions on molded part quality Tables 4 and 5 show the fixed and variable processing parameters employed in the present study. Fig. 9 shows the experimental results obtained for the clamping force increment and part weight under each of the considered experimental conditions. The injected polymer was PA756. Twenty trials were performed under each experimental condition. As shown, the molded part weight increases with an increasing barrel temperature and back pressure, or with a retarding V/P switchover point (i.e., a longer injection stroke). These findings are reasonable since a higher barrel temperature results in a lower viscosity of the polymer melt, and therefore reduces the pressure required to drive the resin into the cavity. In other words, the flow resistance of the polymer melt during the filling and holding stages of the injection cycle reduces with a higher melt temperature. Consequently, the flow and compressive abilities of the polymer are enhanced, and hence the gate-frozen
Fig. 7. Arrangement of strain gauge sensors on tie bars. Table 3 Specifications of sensors and data acquisition card used in proposed quality monitoring and control system. Sensor
Supplier
Type
Tie-bar strain sensor DAQ card
GEFRAN National Instruments
GE1029 USB-6343
Fig. 6. Schematic illustration of injection molding machine and proposed quality monitoring and control system. (Note that dotted lines represent signal flows).
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Fig. 8. Variation of molded part weight and thickness for different injection parameter settings.
barrel temperature and back pressure and 0.94 for the V/P switchover point) regardless the clamping force increment is calculated by single tie bar or the whole clamping force increment. This finding provides a good foundation force-based quality index is highly correlated with molded part weight and could be further engaged to online monitoring, prediction and control of injection molding quality.
Table 4 Fixed parameter settings in injection molding experiments. Fixed parameters Feeding stroke (mm) Screw rotational speed (rpm) Clamping force(kN) Mold temperature ( °C)
40 100 600 60
Injection speed (mm/s) Holding pressure (MPa) Holding time (s) Cooling time (s)
90 105 5 15
5.3. Verification of monitoring system performance for melt quality fluctuation
time is prolonged. As a result, a greater amount of polymer melt is forced into the cavity, and thus this phenomenon reflects on more significant mold separation and the clamping force increment and product weight increase. Regarding the back pressure, a higher back pressure results in a greater density and viscosity of the polymer melt, and therefore increases the pressure required to drive the resin into the cavity. Although changes in the back pressure do not affect the gatefrozen time, the greater amount of polymer melt entering the cavity during the filling stage increases the compression force during the holding stage. As a result, both the clamping force increment and the part weight increase. Finally, a delayed V/P switchover position results in a greater amount of polymer melt flowing into the cavity. Thus, the clamping force increment and part weight are once again both increased. A detailed analysis of the experimental results shows a strong correlation between the clamping force increment and the part weight for each of the considered processing parameters (i.e., 0.99 for the
To further verify the feasibility of the online quality monitoring system mentioned above, a continuous injection molding experiment was performed using PA756 resin for approximately 50 shots followed by the gradual addition of 20% PA756H resin every 50 shots until a condition of 100% PA756H was achieved (see Table 6). Notably, PA756 and PA756H have similar rheological behaviors, but different melt flow indices (MFIs), as described earlier in Section 4. Fig. 10 presents the experimental results for the shot-by-shot variation of the clamping force increment index. (Note that Fig. 10(a) presents the results for F , increment (i.e., the total force increment), while Fig. 10(b) presents the results for F , increment @SGa (i.e., the force increment measured by SGa)). As the PA756 is gradually replaced with PA756H, the viscosity of the polymer melt gradually decreases. Consequently, the amount of molten resin injected into the cavity increases, and hence the clamping force increment and product weight also increase. The part weight is strongly correlated with both clamping increments, i.e., 0.94 for F , increment @SGa and 0.96 for F , increment . In general, the results presented in Fig. 10
Table 5 Variable parameter settings in injection molding experiments. Shot
Back pressure (MPa)
V/P switching position (mm)
10 10 10 10
15 15 15 15
Back pressure variation 5 210 6 210 7 210 8 210
5 10 15 20
15 15 15 15
V/P switching position variation 9 210 10 210 11 210 12 210
10 10 10 10
14.4 14.6 14.8 15.0
Barrel 1 2 3 4
Barrel temperature ( °C) temperature variation 205 210 215 220
Fig. 9. Correlation between force-based quality index and molded part weight for different injection parameter settings. 165
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Table 7 summarizes the experimental results for the mean weight, range and standard deviation of the molded specimens produced using the three different raw materials with and without the quality control system, respectively. The results show that for all three materials, the range and standard deviation values of the products produced using the proposed quality control system are all lower than those produced without the quality control system with the exception of the parts produced using 100%R3 in shots 72-100. The result attributes to larger melt quality difference for 100% R3 recycled material compared to raw material. In general, the molded part weights for all injection cases are qualified with engaging quality control system as melt quality of molten resin fluctuates with rising barrel temperature. Hence, the revealed methodology based on tie bar elongation characteristic is effective to part weight control in injection molding Figs. 12 and 13 show the maximum tensile strengths and weights of the products produced using the three different raw materials and two barrel temperatures (210 and 215 °C) without and with the proposed quality control system, respectively. For the virgin raw material, the tensile strength increases with an increasing barrel temperature when the control system is not employed due to a corresponding increase in the part weight (i.e., part thickness). However, when the control system is applied, the tensile strength reduces slightly as the barrel temperature is increased despite the minor increase in the part weight. For the recycled raw materials, the product weight increases with an increasing barrel temperature in the uncontrolled condition, but remains approximately the same when the control system is employed. However, in contrast to the virgin raw material, the tensile strength of the molded components is insensitive to both the barrel temperature and the use (or not) of the proposed control system. The difference in tendency between the recycled materials and the virgin material can be attributed to a difference in their respective viscosities, as shown in Fig. 14. In particular, the viscosities of the two recycled materials are lower than that of the virgin raw material over the entire shear rate range for both barrel temperatures (210 °C and 215 °C). Moreover, the viscosities of the two recycled materials are very similar to one another. Hence, the tensile strengths of the products produced using the recycled materials are not only slightly lower than those of the products produced using the virgin raw material, but also similar to one another since a similar amount of molten resin enters the cavity in the filling/holding stage in both cases, irrespective of the barrel temperature and application (or otherwise) of the quality control system. Overall, the results presented in Figs. 12–14 indicate that the tensile strength of the molded components is determined principally by the viscosity when the molded part weight is controlled to within a small tolerance.
Table 6 Process parameter settings used in injection molding experiments with PA756/ PA756H. Fixed parameters Barrel temperature ( °C) 210 screw rotational speed (rpm) 100 Back pressure (MPa) 10 Feeding stroke (mm) 40 Clamping force(kN) 600 Mold temperature ( °C) 60 Varying parameter (Blended materials) % of PA756 / % of PA756H 100/0,
Injection speed (mm/s) V/P switch (mm) Holding pressure (MPa) Holding time (s) Cooling time (s)
90 15 105 5 15
80/20, 60/40, 40/60, 20/80, 0/100
therefore confirm the ability of the proposed force-based quality index method to reveal crude changes in the viscosity of the polymer melt induced by deliberate changes in the raw material, and this result could further verify the feasibility of the online quality monitoring method based on the tie bar elongation characteristic. 5.4. Verification of part weight control and property comparison for virgin and recycled raw materials The product quality fluctuation caused by melt quality inconsistencies is particularly apparent in the injection molding of recycled plastic materials [1]. Accordingly, the feasibility of the quality control method proposed in the present study was further evaluated by means of 100-shot continuous injection experiments performed using both virgin raw material (PA756) and its recycled raw materials (R1 and R3). For each raw material, the injection process was performed using a barrel temperature of 210 °C for the first 50 shots and a temperature of 215 °C for the remaining 50 shots. In addition, the experiments were performed both with and without the proposed quality control system, respectively. Fig. 11 presents the corresponding results for the clamping force increment at SGa and part weight in every case. As discussed previously, a higher barrel temperature enhances the flow ability of the polymer resin in the filling stage. Thus, for all of the raw materials, the molded part weight and clamping force increment increase with a higher barrel temperature due to the greater amount of resin which enters the cavity during the filling stage. For the cases where the proposed quality control system is not employed (i.e., Fig. 11(a), (c), and (e)), the product weight exceeds the acceptable quality range (shown by the orange block); even at the lower barrel temperature of 210 °C. However, when the quality control system is applied (i.e., Figs. 11(b), (d), and (f)), the molded part weight generally falls within the required quality interval, irrespective of the barrel temperature.
Fig. 10. Variation of clamping force increment and molded part weight over 400-shot injection molding process using PA756 gradually replaced with PA756H every 50 shots.
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Fig. 11. Variation of clamping force increment and molded part weight over 100-shot injection molding process performed at two different barrel temperatures (210 °C and 215 °C): (a) virgin raw material with no quality control; (b) virgin raw material with quality control; (c) recycled plastic material (R1) with no quality control; (d) recycled plastic material (R1) with quality control; (e) recycled plastic material (R3) with no quality control; (f) recycled plastic material (R3) with quality control. (Note that thin dashed lines represent the upper and lower limits on the clamping force increment, and orange block represents the required quality interval of the molded components).
Table 7 Comparison of part weights with and without quality control for virgin and recycled injection molded parts. Polymer Shot number With no quality control With quality control
Raw material Avg. of weight Range Standard deviation Avg. of weight Range Standard deviation
1-50 4.8694 0.0081 0.0019 4.8676 0.0050 0.0011
100% R1 61-100 4.8822 0.0051 0.0013 4.8688 0.0023 0.0005
6. Conclusions
1-50 4.8714 0.0102 0.0032 4.8696 0.0080 0.0017
100% R3 61-100 4.8814 0.0098 0.0021 4.8683 0.0067 0.0013
1-50 4.8714 0.0084 0.0019 4.8705 0.0083 0.0017
72-100 4.8832 0.0055 0.0015 4.8700 0.0067 0.0018
Consequently, effective online methods for monitoring the polymer melt viscosity and adjusting the processing parameters adaptively are required to ensure the quality consistency of the final molded products, especially proceeding with recycled materials. This study has therefore proposed a quality monitoring method based on the tie bar elongation detected during the molding cycle by simple press-on strain gauges mounted directly on the tie bars. In addition, the V/P calibration procedure revealed in this study is engaged to control molded part weight according to the tie bar elongation profile. The experimental results support the following main conclusions.
The mechanical and physical properties of injection molded components are critically dependent on the viscosity of the polymer melt. However, the polymer materials used in injection molding have a complex rheological behavior, which varies from cycle to cycle under the effects of many extrinsic factors, such as the temperature, pressure and shear rate during the plasticization, filling and holding stages, and so on. Furthermore, the rheological properties of recycled plastic materials are quite different from those of virgin raw materials. 167
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Fig. 12. Tensile strength and product weight of virgin and recycled injection molded parts with no quality control.
Fig. 13. Tensile strength and product weight of virgin and recycled injection molded parts with quality control.
Fig. 14. Variation of viscosity with shear rate for raw and recycled materials at temperatures of: (a) 210 °C and (b) 215 °C.
(1) The force-based quality index derived from the measured tie bar elongation provides a reliable indicator of the molded part weight. Notably, the proposed method is also effective for the injection molding of recycled materials. (2) The barrel temperature, back pressure and V/P switchover point all have an obvious effect on the molded part weight and thickness. The V/P switchover point is particularly strongly correlated with the clamping force increment and part weight, and thus provides an ideal basis for the implementation of the proposed quality control system. Accordingly, the quality control methodology based on tie bar elongation characteristic revealed in this study is successfully verified with its effectiveness. The part weight injected regardless raw and recycled materials is well controlled.
(3) The proposed quality control system achieves an effective control of the part weight for both virgin raw material and recycled raw materials. However, the tensile strengths of the recycled plastic parts are slightly lower than those of the virgin raw material parts due to their lower viscosity (i.e., molecular weight) under the considered shear rate range. (4) Overall, the results presented in this study confirm that the proposed quality control system provides a simple, reliable and noninvasive technique for accommodating intrinsic changes in the polymer melt quality during the injection molding process and improving the quality consistency of the molded products as a result.
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Acknowledgement [15]
This work was supported in part by the Frontier Mould and Die Research and Development Center of Taiwan under The Featured Areas Research Center Program within the Higher Education Sprout Project of the Ministry of Education (MOE), Taiwan. The additional funding provided by the Ministry of Science and Technology, Taiwan, under Project number 106-2218-E-327-002 -MY2is gratefully acknowledged.
[16] [17] [18] [19]
Declaration of Competing Interest
[20]
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
[21] [22]
References
[23] [24]
[1] Hamad K, Kaseem M, Deri F. Recycling of waste from polymer materials: an overview of the recent works. Polym Degrad Stab 2013;98:2801–12. [2] Yoshida T, Ishiaku US, Okumura H, Baba S, Hamada H. The effect of molecular weight on the interfacial properties of GF/PP injection molded composites. Compos Part A Appl Sci Manuf 2006;37:2300–6. [3] Gu F, Hall P, Miles NJ. Performance evaluation for composites based on recycled polypropylene using principal component analysis and cluster analysis. J Cleaner Prod 2016;115:343–53. [4] Gu F, Hall P, Miles NJ. Development of composites based on recycled polypropylene for injection moulding automobile parts using hierarchical clustering analysis and principal component estimate. J Cleaner Prod 2016;137:632–43. [5] Khademi F, Ma Y, Ayranci C, Choi K, Duke K. Effects of recycling on the mechanical behavior of polypropylene at room temperature through statistical analysis method. Polym Eng Sci 2016;56:1283–90. [6] Bai XJ, Isaac DH, Smith K. Reprocessing acrylonitrile-butadiene-sytrene plastics: structure-property relationships. Polym Eng Sci 2007;47:120–30. [7] Pérez JM, Vilas JL, Laza JM, Arnáiz S, Mijangos F, Bilbao E, et al. Effect of reprocessing and accelerated weathering on ABS properties. J Polym Environ 2010;18:71–8. [8] Chen SC, Liao WH, Hsieh MW, Chien RD, Lin SH. Influence of recycled ABS added to virgin polymers on the physical, mechanical properties and molding characteristics. Polym Plast Technol Eng 2011;50:306–11. [9] Scaffaro R, Botta L, Benedetto GD. Physical properties of virgin-recycled ABS blends: effect of post-consumer content and of reprocessing cycles. Eur Polym J 2012;48:637–48. [10] Hamarat I, Kuram E, Ozcelik B. Investigation the mechanical, rheological, and morphological properties of acrylonitrile butadiene styrene blends with different recycling number content. J Process Mech Eng 2017;232:449–58. [11] Boronat J, Segui VJ, Peydro MA, Reig MJ. Influence of temperature and shear rate on the rheology and processability of reprocessed ABS in injection molding process. J Mater Process Technol 2009;209:2735–45. [12] Amano O, Utsugi S. Temperature measurements of polymer melts in the heating barrel during injection molding, part 1. Temperature distribution along the screw axis in the reservoir. Polym Eng Sci 1988;28:1565–71. [13] Amano O, Utsugi S. Temperature measurements of polymer melts in the heating barrel during injection molding, part 2: three-dimensional temperature distribution in the reservoir. Polym Eng Sci 1989;29:171–7. [14] Amano O, Utsugi S. Temperature measurements of polymer melts in the heating
[25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]
169
barrel during injection molding, part 3: effects of screw geometry. Polym Eng Sci 1990;30:385–93. Zhou X, Zhang Y, Mao T, Zhou H. Monitoring and dynamic control of quality stability for injection molding process. J Mater Process Technol 2017;249:358–66. Huang MS. Cavity pressure based grey prediction of the filling-to-packing switchover point for injection molding. J Mater Process Technol 2007;183:419–24. Chen Z, Turng LS. A review of current developments in process and quality control for injection molding. Adv Polym Technol 2005;24:165–82. Zuidema H, Peters GWM, Meijer HEH. Influence of cooling rate on pVT‐ data of semicrystalline polymers. J Appl Polym Sci 2001;82:1170–86. van der Beek MHE, Peters GWM, Meijer HEH. A dilatometer to measure the influence of cooling rate and melt shearing on specific volume. Int Polym Process J Polym Process Soc 2005;20:111–20. van der Beek MHE, Peters GWM, Meijer HEH. The influence of cooling rate on the specific volume of isotactic poly(propylene) at elevated pressures. Macromol Mater Eng 2005;290:443–55. Wang J, Mao Q. A novel process control methodology based on the PVT behavior of polymer for injection molding. Adv Polym Technol 2013;32:E474–85. Min BH. A study on quality monitoring of injection-molded parts. J Mater Process Technol 2003;136:1–6. Chen Z, Turng LS. Injection molding quality control by integrating weight feedback into a cascade closed‐ loop control system. Polym Eng Sci 2007;47:852–62. Michaeli W, Schreiber A. Online control of the injection molding process based on process variables. Adv Polym Technol 2009;28:65–76. Hopmann C, Reßmann A. Self-optimizing in injection molding and the problem at compensating viscosity fluctuations. Proc SPE-ANTEC 2014;2:1706–10. Las Vegas, NV, USA. Zhang JF, Zhao P, Zhao Y, Huang JY, Xia N, Fu JZ. On-line measurement of cavity pressure during injection molding via ultrasonic investigation of tie bar. Sens Actuators A Phys 2019;285:118–26. Gornik C. Viscosity measuring methods for feedstocks directly on injection molding machines. Mater Sci Forum 2008;591-593:174–8. Aho J, Syrjälä S. Shear viscosity measurements of polymer melts using injection molding machine adjustable slit die. Polym Test 2011;30:595–601. Kruppa S, Schiffers R, Würtele M, Holzinger GP. Integrated process monitoring and process control of injection molding machines and molds. Proc SPE-ANTEC 2013;1:1674–9. Cincinnati, OH, USA. Gordon G, Kazmer DO, Tang X, Fan Z, Gao RX. Quality control using a multivariate injection molding sensor. Inter J Adv Manuf Technol 2015;78:1381–91. Lin CC, Wang WT, Kuo CC, Wu CL. Experimental and theoretical study of melt viscosity in injection process. Inter J Chem Mol Nucl Mater Metall Eng 2014;8:687–91. Chen JY, Yang KJ, Huang MS. Online quality monitoring of molten resin in injection molding. Int J Heat Mass Transf - Theory Appl 2018;122:681–93. Chen Z, Turng LS, Wang KK. Adaptive online quality control for injection‐ molding by monitoring and controlling mold separation. Polym Eng Sci 2006;46:569–80. Yin F, Mao H, Hua L. A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Mater Design 2011;32:3457–64. Huang MS, Nian SC, Chen JY, Lin CY. Influence of clamping force on tie-bar elongation, mold separation, and part dimensions in injection molding. Precis Eng 2018;51:647–58. Huang MS, Lin CY. A novel clamping force searching method based on sensing tiebar elongation for injection molding. Int J Heat Mass Transf - Theory Appl 2017;109:223–30. Zhao Y, Zhao P, Zhang JF, Huang JY, Xia N, Fu JZ. On-line measurement of clamping force for injection molding machine using ultrasonic technology. Ultrason 2019;91:170–9. Zhao P, Zhao Y, Zhang JF, Huang JY, Xia N, Fu JZ. Ultrasonic measurement of clamping force for injection molding machine. J Polym Eng 2019;39:388–96.