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Process planning strategy for wire-arc additive manufacturing: Thermal behavior considerations
Journal Pre-proof Process planning strategy for wire-arc additive manufacturing: thermal behavior considerations Yun Zhao, Yazhou Jia, Shujun Chen, Junbiao Shi, Fang Li
PII:
S2214-8604(19)31584-2
DOI:
https://doi.org/10.1016/j.addma.2019.100935
Reference:
ADDMA 100935
To appear in:
Additive Manufacturing
Received Date:
11 September 2019
Revised Date:
28 October 2019
Accepted Date:
4 November 2019
Please cite this article as: Zhao Y, Jia Y, Chen S, Shi J, Li F, Process planning strategy for wire-arc additive manufacturing: thermal behavior considerations, Additive Manufacturing (2019), doi: https://doi.org/10.1016/j.addma.2019.100935
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Process planning strategy for wire-arc additive manufacturing: thermal behavior considerations Yun Zhao1,2, Yazhou Jia1,2, Shujun Chen1,2 *, Junbiao Shi 1,2,Fang Li1,2 1. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China; 2. Engineering Research Center of Advanced Manufacturing Technology for Automotive Components-Ministry of Education, Beijing University of Technology, Beijing 100124, China *Corresponding author: Shujun Chen, Ph.D., Professor, E-mail: [email protected]
Abstract
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Wire arc additive manufacturing (WAAM) has become a promising metal 3D printing technology for fabricating large-scale and complex-shaped components. One major problem that limits the application of WAAM is the difficulty in controlling the dimensional accuracy under constantly changing interlayer temperatures. During the deposition process, as the wall height increases, the heat accumulates on the upper layers, which leads to the variation of the layer dimensions. Normal practices such as introducing idle time and actively cooling the workpiece to mitigate such problems lack efficiency and practicality, respectively. A novel process planning strategy is proposed in this paper and aims to achieve a continuous deposition process while ensuring dimensional accuracy. With the aid of a finite element model, the typical thermal transfer cycle of the workpiece was analyzed and then divided into different stages. When depositing material, the interlayer temperature of the subsequent layers can be predicted using the developed algorithm. Hence, the process parameters (e.g., wire feed speed and travel speed) can be varied according to the predicted interlayer temperature using the developed adaptive process model, and this will ensure the uniform layer dimensions. The effectiveness of the proposed technique is verified by a large-scale shell-shaped component with a total of 753 layers. The result shows that such technique succeeds in a continuous fabrication of the component with high accuracy and efficiency.
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Keywords
Wire-arc additive manufacturing; CMT welding; Finite element; Adaptive process planning; Dimensional accuracy;
1. Introduction Wire-arc additive manufacturing (WAAM) has given rise to powerful capabilities
for metal fabrication directly from 3D models [1]. Compared with other techniques (e.g., laser-based or electron beam-based AM technologies, etc.), WAAM employs a welding arc to melt metal wire into shaped parts and has unique superiorities in production efficiency, especially for the large-scale or wall structure parts [2]. For instance, when using the tandem-based WAAM technology, the deposition rate of the aluminum parts can reach up to 9 kg/h [3]. On the contrary, the deposition rate of laserbased AM technologies can only reach to 0.14-0.5 kg/h [4]. On the other hand, the additively manufactured parts using the titanium powder were also proven to have excellent mechanical performance, the tensile strength can reach to 1100 Mpa [5]. Due to such advantages, WAAM is considered to be the most suitable technique for fabricating large-scale components [6, 7].
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However, one major problem that limits the application of WAAM is the difficulties in controlling the dimensional accuracy under constantly changing thermal conditions (e.g., interlayer temperature and cooling rate) [8]. During the deposition process, the heat dissipation conditions become poor as the workpiece ‘grows tall’, which would lead to the variation of the layer dimensions [9]. For instance, a lower interlayer temperature would hasten the solidification of the molten pool, thus the layer appears ‘narrow and tall’. Conversely, a higher interlayer temperature would make the layer ‘wide and short’. Zhao et al. [10] demonstrated that the temperature gradient of the molten pool decreases as the wall height increases as a consequence of the heat accumulated, which prolongs the time required for solidification of the molten pool. They proved that the increase of temperature gradient is helpful to reduce the flow behavior of the molten pool. Wu et al. [11] indicated that the layer dimensions vary in the first few layers and then tend to be steady when the heat input and dissipation have reached a balance. This problem significantly affects the dimensional accuracy of the deposited part. More seriously, this dimensional error will accumulate in the build direction and then change the torch-to-workpiece distance, which would prevent the continuation of the deposition operation at upper layers. Furthermore, the varying layer width makes the machining process more complicated. This will increase cost and time to post process the part.
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Normal practice to mitigate such issues is to introduce idle time between each deposited layer. This enables the workpiece to transfer the excessive heat to the environment until it reaches the pre-set temperature. Lei et al. [12] investigated the influence of interlayer idle time on the thermal behavior during GMAW-based additive manufacturing and described the temperature and temperature gradient variation regularity using finite element analysis. They found that increasing the idle time is helpful to improve the forming accuracy of each layer. In addition, Geng et al. [13] developed a theoretical model to optimize interlayer temperature and heat input during GTAW-based additive manufacturing. Using the calculated interlayer idle time for each layer, the solidification defects were eliminated and the fabricated part shows adequate
formation and quality. Montevecchi et al. [14-16] proposed an innovative approach to schedule the interlayer idle times for WAAM based on finite element analysis, concluding that the variation in idle time contributes to a constant interlayer temperature and ensures a constant molten pool size, which then increases the quality and productivity of the WAAM process. By introducing idle time, the temperature field could be effectively regulated to ensure dimensional accuracy. However, the drawback is that this method impedes the production efficiency. This is because the required idle time would become longer as the height of the workpiece increases due to poor heat dissipation. When large-scale parts are needed, the excessive idle times are unacceptable.
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Another way to deal with this problem is to actively cool the workpiece. Shi et al. [17] and Li et al. [9] developed an in-process active cooling system to eliminate the excessive heat between layers. It was proven that the dimensional errors can be significantly reduced with the aid of this system, and finer microstructures can be obtained due to the increased cooling rate. Even though the system helps a great deal in reducing heat accumulation, it is difficult to consider its use in actual production conditions because of the problem on moving the thermoelectric cooling equipment along with the welding torch [18].
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In summary, a method is urgently needed to juggle the dimensional accuracy and manufacturing efficiency for the use of WAAM. Therefore, this paper proposes a novel process planning strategy, which aims to achieve a continuous deposition process while ensuring the dimensional accuracy. To this end, the thermal behavior for the workpiece was studied based on a numerical simulation method. When depositing parts, the thermal conditions of the subsequent layers can be predicted using the developed algorithm. Hence, the process parameters can be varied using the developed adaptive process model to ensure uniform layer dimensions. Finally, the proposed technique was verified by a typical component with a total of 753 layers.
2. Research method
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2.1. Experimental setup A schematic diagram of the additive manufacturing system is presented in Fig. 1. It is mainly composed of a CMT (cold metal transfer) TPS4000 power source, a KUKA KR16 robot, and a rotary table. In the present study, the experiment was conducted by a ϕ1.2 mm wire of ER4043 aluminum alloy, which is deposited on the substrate, a 5A06 aluminum plate with dimensions of 130×100×5 mm. The composition of the deposition wire and substrate are listed in Table 1. The shielding gas for the CMT torch was Ar (99.99%) gas with a constant flow rate of 15 L/min. During the deposition, the
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wire was melted by the arc heat source and then solidified on the top surface of the previous layer, resulting in a layer-by-layer deposition of the part.
Fig. 1 Additive manufacturing platform and fabricated component
Element
Si
Fe
Cu
ER4043
4.5-6.0
0.8
5A06
0.4
0.4
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Table 1 Composition of ER4043 wire and 5A06 plate (wt%) Mn
Mg
Zn
Ti
Al
0.3
0.05
0.05
0.1
0.2
Rem
0.1
0.5-0.8
5.5-6.8
0.2
0.02-0.1
Rem
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2.2. Experimental procedure
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The cylinder component is used as the typical model to study the thermal transfer behavior during the deposition process. As shown in Fig. 2(a), the dimensions of the test model were depicted. A total of 40 layers were deposited in sequence, from the bottom up. When a layer was deposited, the welding torch lifted and repeated the previous trajectory. It should be emphasized that the arc is kept burning and the welding torch is kept moving throughout the entire deposition period. This allows for the production of a continuously varied temperature field which shows a strong regularity and, therefore, makes the interlayer temperature of the following layers predictable. The K type thermocouple made with nickel-chromium and nickel-silicon materials is used to measure the temperature of the point A (seen in Fig. 2(a)) to provide confidence in the accuracy of the finite element model. The probing end of the wire was buried into the substrate plate. The sampling rate of the thermocouple was 10 Hz and the measurement range of temperatures was between 0 and 1300℃. This system was depicted in detail in our previously published studies [19, 20].
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Fig. 2(b) shows the fabricated component with fine surface quality. However, the dimensional accuracy can not be guaranteed. As shown in Fig. 2(c), the layer width continues to widen due to the heat accumulation, especially for the last several layers. Fig. 3 shows the wall width variation from the 1th to the 40th layer. It can be seen that the wall width dramatically increased from 4.51 mm to 5.72mm (approximately 26.8% increased), which means that the redundant materials would be post machined and, therefore, result in lower production efficiency and higher cost. Thus, a controlled technique should be developed to deal with this problem, and the finite element model is first established to study the thermal behavior on the additively manufactured component.
Fig. 2 Model description and problem statement (a) dimensions of the test model (b) fabricated
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component (c) widening deposited layer due to heat accumulation
Fig. 3 Wall width variation using constant welding parameters
2.3. FE model description 2.3.1. Dimension and mesh
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The Simufact.welding software (version 6.0.0) is used for thermal simulations. According to our previous study [21], it can provide the thermal distribution and the distortion prediction for the welding process with high accuracy. The geometric size of the FEM model is shown in Fig. 2(a). The wall thickness was set at 4.5 mm and the layer height was 1.5 mm. Fig. 4 shows the three-dimensional finite element mesh for the cylinder model. In the present model, linear brick elements with eight-node hexahedrons are used for thermal simulation. In addition, the software can automatically refine the elements for the area near the deposited layer from level 0 to 10. All the elements of the deposited layer were deactivated before deposition, and then activated sequentially following the heat source.
Fig. 4 Three-dimensional finite element mesh for forty-layer cylinder part
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2.3.2. Heat source
The Goldak double ellipsoidal heat source was used to apply the heat to the additive manufactured deposits [22]. Ding et al. [23] proved that this heat source model is suitable for the simulation of a CMT heat source. The power density distribution of the region in front of the arc center and the region behind the arc center are described as follows:
qf
qr
x 2 y 2 z 2 exp 3 2 2 2 b c a f bc a f
(1)
x 2 y 2 z 2 6 3qf r exp 3 2 2 2 b c ar bc ar
(2)
6 3qf f
f f fr 2
(3)
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where af is the front ellipsoidal semi-axes length and ar is the rear ellipsoidal semi-axes length; b is the width of the heat source; c is the depth of the heat source; q is the energy input while considering the factor of efficiency and can be calculated as follows:
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q IU
(4)
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where I is the welding current; U is the welding voltage; ƞ is the thermal efficiency coefficient which ranges from 0 to 1 based on the different welding processes. The value of ƞ is set to 0.9 in this paper.
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According to the Ref [22] and [23], the welding parameters and the corresponding heat source properties are listed in Table 2. The method of determining the parameters (af, ar, b, c) of CMT process can be consulted in Ref [22] and [23]. The data was also verified by cutting out the cross-section of the weld bead. The voltage and amperage is obtained by measuring the waveform during deposition using Hall current and voltage sensor. The material properties were temperature-dependent which were obtained from Ref. [24]. A uniform coefficient of convection on all surfaces equal to 10 W (m2K)-1 and a radiation emissivity of 0.22 were assumed.
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Table 2 welding parameters and the corresponding heat source properties
I
U
af
ar
b
c
[m/min]
[A]
[V]
[mm]
[mm]
[mm]
[mm]
3.2
45
12.6
2.6
4.1
3.2
1.27
3.8
56
12.9
2.8
4.4
3.3
1.43
4.3
65
13.0
3.1
4.8
3.5
1.51
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WFS
2.3.3. Validation of the FE model Experimental validation was performed to prove the accuracy of the numerical simulation. It was verified by comparing the measured temperature cycling curves of point A (seen in Fig. 2(a)) with the corresponding simulated curves. The results are
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shown in Fig. 5, the dashed lines and solid lines are the experimental results and the simulated results, respectively. It is found that the trend in fluctuations of the simulated results approximately agrees with the actual measurements, indicating the effectiveness of the FE model. However, there are errors between the two curves, which are mainly due to the different heat dissipation conditions. In actual conditions, the heat prone to dissipation through the worktable is not considered in the FE model and, therefore, results in a lower actual temperature. The maximum peak temperature error is 19.3 ℃, which occurred on the first layer. This error tends to decrease layer by layer. The minimum peak temperature error that appeared on the 20th layer is 10.6 ℃. The average error of the simulated and experimental curves is 4.4 ℃. Although the simulation results lack some accuracy, they can provide the temperature variation trends for the deposition process. This still allows the thermal transfer behavior to be studied on the manufactured part as well as for the development of the dimensional control technique.
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Fig. 5 Comparison of the simulated and experimental thermal cycling curves
3. Typical thermal transfer cycle
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Fig. 6 depicts the temperature distribution on the cylinder part with the number of layers at 1, 5, 10, 20, 30, and 40, respectively. The welding current was 60 A, welding voltage was 12.9 V, and the travel speed was 8 mm/s. It was shown that both the maximum temperature and the high-temperature region increase along with the layer number, and the temperature gradient also shows a downward trend. This is expected because the heat dissipation condition becomes poorer with the increase in layers. When the workpiece height is relatively low, the heat is prone to dissipation through the substrate plate (seen in Fig.6 (a), (b) and (c)), and as a consequence the hightemperature area on the substrate plate becomes larger [25-27]. As the workpiece was ‘growing tall’, the heat accumulated on the workpiece (seen in Fig.6 (d), (e) and (f)),
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and the high-temperature area on the substrate plate became smaller until it dropped to room temperature.
Fig. 6 Temperature distribution of the cylinder component when the heat source moves to the midpoint
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of (a) 1st layer (b) 5th layer (c) 10th layer (d) 20th layer (e) 30th layer (f) 40th layer.
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The simulation results of a 40-layer cylinder component gives a clear understanding of the overall thermal transfer behavior during deposition process [28]. To analyze the thermal behavior for planning process parameters, the thermal cycle should be divided into different stages and discussed separately [29-30]. Fig. 7 shows the peak temperature and interlayer temperature of the midpoint in the different layers. According to the characteristics of the curve, a typical thermal cycle can be divided into three stages as follows:
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(1) Boost heating stage: At the beginning of the deposition process (first six layers), the peak temperature drastically increased from 430 ℃ to 620 ℃. However, the interlayer temperature varied within a narrow range around 61 ℃. This is mainly due to the strong heat dissipation effect of the substrate plate, which is also known as the ‘heat sink effect’. Such an effect results in a high cooling rate on the first deposited layers and makes them prematurely solidified and therefore strongly affects their dimensions. It is hard to ensure the dimensional accuracy in this stage due to the violent fluctuations of the peak temperature. However, the duration and amplitude of this stage can be effectively shortened by preheating the substrate plate or using high arc power
(2) Quasi-steady stage: In this stage, the mode of heat dissipation changes gradually
from heat conduction to heat radiation. Both the rising trend of the peak temperature and interlayer temperature starts to become even and gentle, which indicates that this stage is controllable. Therefore, the adaptive process parameters can be planned according to different temperature values to ensure uniform dimensions.
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(3) Steady stage: In this stage, the molten pool reaches the steady state, so that the heat flux of heat inflow and outflow from the molten pool remains unchanged. This stage is considered as the best case for WAAM because it enables the deposition of uniform layers without changing the deposition variables. In the present case, the steady stage could appear when the heat dissipation mode completely transfers to radiation from the deposited wall to the surrounding air, which means that the temperature of the substrate plate would drop to room temperature. As shown in Fig. 8, four temperature tracking points were inserted to monitor the temperature history of the first layer. It can be observed that the temperature is still maintained at approximately 100 ℃ even after 40 layers were deposited. Apparently, quite a few layers need to be deposited to reach the steady stage, indicating the difficulties in reaching such stage.
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Fig. 7 Peak temperature and minimum temperature for the midpoint of each layer
Fig. 8 Thermal history of the tracking points B, C, D, and E on the first layer
It is believed that a different process planning strategy should be used according to the characteristics within different stages. For instance, even the most undesirable boost heating stage could be moderated by the preheating method. Even though the steady stage is the most desired case, it is not easy to obtain and prone to being disturbed by various deposition variables such as arc power, substrate plate condition, and geometric shape of the model. Therefore, the proposed technique would mainly focus on the quasi-steady stage because it not only occupies the majority of the cycle but also shows regular and controllable characteristics.
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4. Proposed process planning technique
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In order to deal with the problem of the dimensional error caused by heat accumulation, this paper proposes a novel technique to generate the adaptive process parameters for the fabrication of the large-scale component. The basic idea of the proposed technique is to monitor the temperature of the current layer and predict the next one and then generate the adaptive process parameters that correspond to the predicted temperature to ensure the uniform layer dimensions. Therefore, two key parameters are listed as follows: An interlayer temperature monitoring method and prediction algorithm
Adaptive process modeling and matching
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In contrast to the traditional process planning method, the proposed technique allows the continuous deposition operation without the interlayer idle time or heat eliminating equipment, which is particularly beneficial for increasing production efficiency.
4.1. Interlayer temperature prediction
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4.1.1. Control node
As shown in Fig. 9, two critical control nodes are selected in each layer to perform the control strategy, which is defined as follows:
The process-varying node (PV node) is defined as the starting point of each layer. Every time the welding torch comes back to the starting point, the process parameters should be varied to match the predicted temperature of the next layer.
The temperature-monitoring node (TM node), located at the middle point of each layer, can show the real-time temperature which is measured by an infrared thermometer. Such temperature values are only read and substituted into the
Fig. 9 Scheme diagram of the control nodes
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temperature prediction equations when the welding torch comes back to the starting point, then the interlayer temperature of the next layer can be calculated.
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The surface temperature of the control nodes is captured using the thermal infrared camera (Flir SC620). In the present study, the temperature range was set in 0500 ℃. The spatial resolution was set to 0.66 mm/pixel. The frame rate was set to 15 Hz. The emissivity of the aluminum material is set to 0.22. The IR camera was directly facing the TM node during deposition. Once the welding torch moves to PV node, the acquisition signal will be triggered and the real-time temperature data will be transferred to the industrial computer. With the aid of the MATLAB, the temperature data was processed using the Eq. (5), (6) and (9) to generate the new welding parameters (wire feed speed and travel speed). With the aid of Robot Art online programming software, the new parameters would be activated to perform the next layer.
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4.1.2. Temperature calculation
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According to the analysis in section 3, the quasi-steady stage is believed to be regular and predictable. As shown in Fig. 10, when reaching the quasi-steady state, each layer number corresponds to a different temperature. Assuming Ln is the current layer, Ln-1 and Ln+1 are the former layer and the predicted layer, respectively. In order to predict the interlayer temperature of the next layer (Tn+1*), the heat transfer coefficient λ is defined to describe the rising temperature trend of the former two layers, which can be calculated as follows:
Tn Tn 1
(5)
where Tn-1 and Tn is the actual temperature of the Ln-1 and Ln, respectively, which were
measured by an infrared thermometer. Such heat transfer coefficient λ could be passed on to the next layer. Therefore, the Tn+1* could be calculated as follows:
Tn1 * Tn
(6)
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When obtaining the Tn+1*, a new group of adaptive process parameters would be generated simultaneously at the PV node. Analogously, Tn+2 could also be obtained by substituting Tn and Tn+1 into the Eq. (5) and (6). Note that there is an inevitable error between the predicted T* and measured T, which is mainly caused by the variable path length in each layer. For instance, as shown in Fig. 1, the closed path length increases gradually along with the diameter, which results in the longer cooling time of the TM node and, therefore, the gradually diminishing temperature difference. However, such error is insignificant because the slope of the temperature curve in the quasi-steady stage is much smaller compared with that in the boost heating stage, and this will also be verified through the experimental case in section 5.
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Fig. 10 Interlayer temperature prediction procedure
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4.2. Adaptive process model
4.2.1. Fixed Slayer planning strategy
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The dimensional problem can be presented by the shape variation of the crosssection of the single layer. As shown in Fig. 11, the basic idea of the adaptive process model is to deposit layers with a fixed cross-sectional area (Slayer) and control their shapes.
Fig. 11 Schematic representation of the cross-section of the deposited wall
d 2WFS
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Slayer
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The Slayer can be calculated as follows [20]:
4TS
(7)
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where d is the diameter of the welding wire, WFS is the wire feed speed, and TS is the travel speed of the welding torch. It is obvious that, in order to obtain the fixed Slayer, the ratio of the wire feed speed to travel speed (RWT) should be kept unchanged. In other words, the fixed Slayer could be obtained by increasing or decreasing WFS and TS at the same time.
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However, the geometric shapes associated with different WFS or TS under constant RWT are quite different. As shown in Table 3, three case parameter groups were chosen to illustrate the dimensional differences and their effects on thermal behavior. The welding current and voltage output from the CMT source were recorded and the lineenergy Q was calculated as follows:
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Q
TS
(8)
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The results show that the geometric shape turns from ‘narrow and tall’ to ‘wide and short’ with the increase of the WFS, this is because the higher line-energy Q would result in a wider molten pool and, therefore, wider layer width [19,20]. Table 3 Three case parameter groups with the same RWT
WFS
TS
I
U
Q
Layer width
Layer height
[m/min]
[mm/s]
[A]
[V]
[J]
[mm]
[mm]
3.2
8.00
45
12.6
510.3
4.41
1.71
3.8
9.50
56
12.9
650.2
4.83
1.56
4.3
10.75
65
13.0
760.5
5.31
1.42
4.2.2. Effect of line-energy on thermal behavior
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The three case parameter groups also have a significant effect on the temperature behavior for the workpiece. As shown in Fig. 12, the peak temperature significantly increased with the increase of WFS, indicating that the line-energy Q directly determines the available layer width. On the contrary, the interlayer temperature increased slightly along with the WFS, this is mainly due to the excellent heat dissipation in aluminum alloy. It is worth noting that, the line-energy Q has no significant effect on the number of layers required to reach the quasi-steady state, and the slope of the curves of the peak temperature is almost parallel. Therefore, it can be concluded that the adaptive process model is able to control dimensional accuracy and adjust heat input at the same time.
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Fig. 12 Temperature comparison using different line-energy
4.2.3. Dimensional prediction model
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In order to adapt the process parameters into the sliced path associated with different interlayer temperature, the central composite rotatable design (CCRD) method is applied to establish the dimensional prediction model. The RWT is set constant at 0.4, the wire feed speed (WFS) and interlayer temperature (T) were chosen as the input variables, and the layer width (Wlayer) is regarded as the output variable. Their upper and lower limits and coding are given in Table 4. It should be emphasized that the parameters should be selected within certain range to ensure the good layer formation. The parameter range was designed by performing experimental trails which can be referred in our previous literature [20, 31]. Totally 20-layered thin-wall part was deposited layer by layer for each experiment
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groups. To measure the effective wall width, cross sections were intercepted from the middle of the thin-wall specimen. After the process of grinding, polishing and corroding, the morphology photos were taken by OLMPUS laser scanning confocal microscopy. The schematic representation of the cross-section measurement system was shown in Fig. 13.
Fig. 13 Schematic representation of cross-section
Factor level
WFS
T
(m·min-1)
(℃)
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Table 4 Coding for factor and level
2.8
20
-1
3.3
46
0
4.5
110
1
5.7
174
1.41421
6.2
200
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-1.41421
For two factors chosen with five levels, the required number of experiments is 13 according to CCRD. Using the software of design expert, the experimental samples can be designed, and then the experiment was performed using the setups presented in section 2. The detailed calculation method can be referred to our previous literature [20, 31]. The final regression model was obtained as follows:
Wlayer 0.2476 1.2415WFS 0.0147T 1.5032 10-3WFST
(9)
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The corresponding 2D layer width plot is shown in Fig. 14, it can be seen that the adaptive WFS can maintain a target layer width under different interlayer temperature and, therefore, keep a constant layer height. This prediction model will be used to adjust the WFS and TS at the PV node to ensure the uniform layer dimensions.
5. Experimental verification
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Fig. 14 2D layer width plot
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The proposed planning technique is verified by fabricating a large-scale model. Fig. 15 shows the dimensions of the typical model, it is featured by the continuously changing diameter from the bottom up, and the wall thickness is set at 6 mm. As shown in Fig. 16, a rotary table helps to keep the building direction oriented vertically and rotated during deposition. Thus, the welding torch only needs to lift when a layer is finished.
Fig. 15 Dimensional description of the test model
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Fig. 16 Scheme diagram of the deposition mode
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The process parameters generated for the first 100 layers were listed in Table 5. Starting from the 3th layer, the interlayer temperature was predicted according to the actually measured temperature at the TM node of the former two layers using the Eq. (5) and (6). Then, the process parameter was generated by substituting Wlayer = 6 and the predicted T into the Eq. (9). Therefore, a new group of WFS and TS can be generated and activated at the PV node. Analogously, the subsequent layers can be planned one by one following the steps above with the aid of RobotArt online programming software.
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The final manufactured part is shown in Fig. 1. In regard to the former 100 layers, the welding parameters changed significantly with the increase of the interlayer temperature. It can be seen from Table 5 that the proposed technique has high accuracy in predicting the interlayer temperature and the layer dimensions. And, as expected, the macrostructure (seen in Fig. 16) shows that the deposited wall has uniform dimensions. The final manufactured part is shown in Fig. 1, it indicates that the proposed planning technique is effective and practicable. Table 5 Deposition variables and forming dimensions PV node
TM node
Actual dimensions
Layer
WFS
TS
Predicted
Measured
Layer width
Layer height
number
[m/min]
[mm/s]
temperature [℃]
temperature [℃]
[mm]
[mm]
5.4
8
-
105.3
6.51
1.95
10
4.4
11
90.7
90.8
6.03
1.25
20
4.3
10.75
96.7
98.3
5.94
1.27
30
4.2
10.5
106.9
108.4
6.08
1.24
40
4.2
10.5
117.1
118.9
5.91
1.28
50
4.1
10.25
126.8
129.2
5.97
1.26
60
4.1
10.25
137.5
140.3
6.04
1.25
70
4.0
10
146.6
148.7
5.95
1.27
80
4.0
10
151.4
152.1
6.01
1.25
90
4.0
10
153.5
153.8
100
4.0
10
153.2
153.2
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1
1.24
6.07
1.24
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6.09
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The layer width of the former 20 layers is shown in Fig. 17. In order to show the advantages of the proposed technique, the experiment was also performed using the constant parameters (WFS=4.0 m/min, TS=10 mm/s) and 1 min interval time. It can be seen that the proposed technique has better dimensional accuracy comparing with others. The variance of the optimized layer width is 0.1178 while the others are 0.6399 and 0.4868, respectively. In addition, the production time of the optimized method is 20 min less than the normal method with 1 min idle time. It is confirmed that the proposed technique succeeds in continuously fabricating the large-scale part with a total of 753 layers. The part also has high dimensional accuracy and manufacturing efficiency.
Fig. 17 Comparison of the layer width using different deposition strategy
6. Conclusion This paper proposes a novel process planning strategy for WAAM through the generation of variable process parameters in accordance with the different thermal conditions. This ensures dimensional accuracy and improved manufacturing efficiency. With the aid of the finite element model, the typical thermal transfer cycle of the workpiece was analyzed and then divided into three stages (e.g., boost heating stage, quasi-steady stage, and steady stage). Among them, the quasi-steady stage occupies the majority of the cycle and was found to be regular and controllable.
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When depositing parts, the thermal condition of the subsequent layers would be predicted with the aid of the control nodes and developed algorithm, and then the process parameters (e.g., wire feed speed and travel speed) could be varied according to the predicted interlayer temperature using the developed adaptive process model to ensure the uniform layer dimensions. The effectiveness of the proposed technique is verified by a large-scale shell-shaped component with a total of 753 layers. The result shows that the proposed technique has high accuracy in predicting the interlayer temperature as well as the uniform dimensions. Moreover, the manufacturing efficiency is significantly improved because no idle time is introduced.
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Conflict of interest statement
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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of the manuscript entitled ‘Dimensional control of wire-arc additive manufacturing for large-scale shell-shaped component: A process planning strategy considering thermal behavior’.
Acknowledgment This work was supported by the National Natural Science Foundation of China (no. 51805013) and Foundation Research Fund of Beijing University of Technology (no. 001000546318526).
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Appl. Sci. 7 (2017), 1233.
PII:
S2214-8604(19)31584-2
DOI:
https://doi.org/10.1016/j.addma.2019.100935
Reference:
ADDMA 100935
To appear in:
Additive Manufacturing
Received Date:
11 September 2019
Revised Date:
28 October 2019
Accepted Date:
4 November 2019
Please cite this article as: Zhao Y, Jia Y, Chen S, Shi J, Li F, Process planning strategy for wire-arc additive manufacturing: thermal behavior considerations, Additive Manufacturing (2019), doi: https://doi.org/10.1016/j.addma.2019.100935
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Process planning strategy for wire-arc additive manufacturing: thermal behavior considerations Yun Zhao1,2, Yazhou Jia1,2, Shujun Chen1,2 *, Junbiao Shi 1,2,Fang Li1,2 1. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China; 2. Engineering Research Center of Advanced Manufacturing Technology for Automotive Components-Ministry of Education, Beijing University of Technology, Beijing 100124, China *Corresponding author: Shujun Chen, Ph.D., Professor, E-mail: [email protected]
Abstract
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Wire arc additive manufacturing (WAAM) has become a promising metal 3D printing technology for fabricating large-scale and complex-shaped components. One major problem that limits the application of WAAM is the difficulty in controlling the dimensional accuracy under constantly changing interlayer temperatures. During the deposition process, as the wall height increases, the heat accumulates on the upper layers, which leads to the variation of the layer dimensions. Normal practices such as introducing idle time and actively cooling the workpiece to mitigate such problems lack efficiency and practicality, respectively. A novel process planning strategy is proposed in this paper and aims to achieve a continuous deposition process while ensuring dimensional accuracy. With the aid of a finite element model, the typical thermal transfer cycle of the workpiece was analyzed and then divided into different stages. When depositing material, the interlayer temperature of the subsequent layers can be predicted using the developed algorithm. Hence, the process parameters (e.g., wire feed speed and travel speed) can be varied according to the predicted interlayer temperature using the developed adaptive process model, and this will ensure the uniform layer dimensions. The effectiveness of the proposed technique is verified by a large-scale shell-shaped component with a total of 753 layers. The result shows that such technique succeeds in a continuous fabrication of the component with high accuracy and efficiency.
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Keywords
Wire-arc additive manufacturing; CMT welding; Finite element; Adaptive process planning; Dimensional accuracy;
1. Introduction Wire-arc additive manufacturing (WAAM) has given rise to powerful capabilities
for metal fabrication directly from 3D models [1]. Compared with other techniques (e.g., laser-based or electron beam-based AM technologies, etc.), WAAM employs a welding arc to melt metal wire into shaped parts and has unique superiorities in production efficiency, especially for the large-scale or wall structure parts [2]. For instance, when using the tandem-based WAAM technology, the deposition rate of the aluminum parts can reach up to 9 kg/h [3]. On the contrary, the deposition rate of laserbased AM technologies can only reach to 0.14-0.5 kg/h [4]. On the other hand, the additively manufactured parts using the titanium powder were also proven to have excellent mechanical performance, the tensile strength can reach to 1100 Mpa [5]. Due to such advantages, WAAM is considered to be the most suitable technique for fabricating large-scale components [6, 7].
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However, one major problem that limits the application of WAAM is the difficulties in controlling the dimensional accuracy under constantly changing thermal conditions (e.g., interlayer temperature and cooling rate) [8]. During the deposition process, the heat dissipation conditions become poor as the workpiece ‘grows tall’, which would lead to the variation of the layer dimensions [9]. For instance, a lower interlayer temperature would hasten the solidification of the molten pool, thus the layer appears ‘narrow and tall’. Conversely, a higher interlayer temperature would make the layer ‘wide and short’. Zhao et al. [10] demonstrated that the temperature gradient of the molten pool decreases as the wall height increases as a consequence of the heat accumulated, which prolongs the time required for solidification of the molten pool. They proved that the increase of temperature gradient is helpful to reduce the flow behavior of the molten pool. Wu et al. [11] indicated that the layer dimensions vary in the first few layers and then tend to be steady when the heat input and dissipation have reached a balance. This problem significantly affects the dimensional accuracy of the deposited part. More seriously, this dimensional error will accumulate in the build direction and then change the torch-to-workpiece distance, which would prevent the continuation of the deposition operation at upper layers. Furthermore, the varying layer width makes the machining process more complicated. This will increase cost and time to post process the part.
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Normal practice to mitigate such issues is to introduce idle time between each deposited layer. This enables the workpiece to transfer the excessive heat to the environment until it reaches the pre-set temperature. Lei et al. [12] investigated the influence of interlayer idle time on the thermal behavior during GMAW-based additive manufacturing and described the temperature and temperature gradient variation regularity using finite element analysis. They found that increasing the idle time is helpful to improve the forming accuracy of each layer. In addition, Geng et al. [13] developed a theoretical model to optimize interlayer temperature and heat input during GTAW-based additive manufacturing. Using the calculated interlayer idle time for each layer, the solidification defects were eliminated and the fabricated part shows adequate
formation and quality. Montevecchi et al. [14-16] proposed an innovative approach to schedule the interlayer idle times for WAAM based on finite element analysis, concluding that the variation in idle time contributes to a constant interlayer temperature and ensures a constant molten pool size, which then increases the quality and productivity of the WAAM process. By introducing idle time, the temperature field could be effectively regulated to ensure dimensional accuracy. However, the drawback is that this method impedes the production efficiency. This is because the required idle time would become longer as the height of the workpiece increases due to poor heat dissipation. When large-scale parts are needed, the excessive idle times are unacceptable.
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Another way to deal with this problem is to actively cool the workpiece. Shi et al. [17] and Li et al. [9] developed an in-process active cooling system to eliminate the excessive heat between layers. It was proven that the dimensional errors can be significantly reduced with the aid of this system, and finer microstructures can be obtained due to the increased cooling rate. Even though the system helps a great deal in reducing heat accumulation, it is difficult to consider its use in actual production conditions because of the problem on moving the thermoelectric cooling equipment along with the welding torch [18].
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In summary, a method is urgently needed to juggle the dimensional accuracy and manufacturing efficiency for the use of WAAM. Therefore, this paper proposes a novel process planning strategy, which aims to achieve a continuous deposition process while ensuring the dimensional accuracy. To this end, the thermal behavior for the workpiece was studied based on a numerical simulation method. When depositing parts, the thermal conditions of the subsequent layers can be predicted using the developed algorithm. Hence, the process parameters can be varied using the developed adaptive process model to ensure uniform layer dimensions. Finally, the proposed technique was verified by a typical component with a total of 753 layers.
2. Research method
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2.1. Experimental setup A schematic diagram of the additive manufacturing system is presented in Fig. 1. It is mainly composed of a CMT (cold metal transfer) TPS4000 power source, a KUKA KR16 robot, and a rotary table. In the present study, the experiment was conducted by a ϕ1.2 mm wire of ER4043 aluminum alloy, which is deposited on the substrate, a 5A06 aluminum plate with dimensions of 130×100×5 mm. The composition of the deposition wire and substrate are listed in Table 1. The shielding gas for the CMT torch was Ar (99.99%) gas with a constant flow rate of 15 L/min. During the deposition, the
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wire was melted by the arc heat source and then solidified on the top surface of the previous layer, resulting in a layer-by-layer deposition of the part.
Fig. 1 Additive manufacturing platform and fabricated component
Element
Si
Fe
Cu
ER4043
4.5-6.0
0.8
5A06
0.4
0.4
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Table 1 Composition of ER4043 wire and 5A06 plate (wt%) Mn
Mg
Zn
Ti
Al
0.3
0.05
0.05
0.1
0.2
Rem
0.1
0.5-0.8
5.5-6.8
0.2
0.02-0.1
Rem
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2.2. Experimental procedure
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The cylinder component is used as the typical model to study the thermal transfer behavior during the deposition process. As shown in Fig. 2(a), the dimensions of the test model were depicted. A total of 40 layers were deposited in sequence, from the bottom up. When a layer was deposited, the welding torch lifted and repeated the previous trajectory. It should be emphasized that the arc is kept burning and the welding torch is kept moving throughout the entire deposition period. This allows for the production of a continuously varied temperature field which shows a strong regularity and, therefore, makes the interlayer temperature of the following layers predictable. The K type thermocouple made with nickel-chromium and nickel-silicon materials is used to measure the temperature of the point A (seen in Fig. 2(a)) to provide confidence in the accuracy of the finite element model. The probing end of the wire was buried into the substrate plate. The sampling rate of the thermocouple was 10 Hz and the measurement range of temperatures was between 0 and 1300℃. This system was depicted in detail in our previously published studies [19, 20].
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Fig. 2(b) shows the fabricated component with fine surface quality. However, the dimensional accuracy can not be guaranteed. As shown in Fig. 2(c), the layer width continues to widen due to the heat accumulation, especially for the last several layers. Fig. 3 shows the wall width variation from the 1th to the 40th layer. It can be seen that the wall width dramatically increased from 4.51 mm to 5.72mm (approximately 26.8% increased), which means that the redundant materials would be post machined and, therefore, result in lower production efficiency and higher cost. Thus, a controlled technique should be developed to deal with this problem, and the finite element model is first established to study the thermal behavior on the additively manufactured component.
Fig. 2 Model description and problem statement (a) dimensions of the test model (b) fabricated
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component (c) widening deposited layer due to heat accumulation
Fig. 3 Wall width variation using constant welding parameters
2.3. FE model description 2.3.1. Dimension and mesh
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The Simufact.welding software (version 6.0.0) is used for thermal simulations. According to our previous study [21], it can provide the thermal distribution and the distortion prediction for the welding process with high accuracy. The geometric size of the FEM model is shown in Fig. 2(a). The wall thickness was set at 4.5 mm and the layer height was 1.5 mm. Fig. 4 shows the three-dimensional finite element mesh for the cylinder model. In the present model, linear brick elements with eight-node hexahedrons are used for thermal simulation. In addition, the software can automatically refine the elements for the area near the deposited layer from level 0 to 10. All the elements of the deposited layer were deactivated before deposition, and then activated sequentially following the heat source.
Fig. 4 Three-dimensional finite element mesh for forty-layer cylinder part
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2.3.2. Heat source
The Goldak double ellipsoidal heat source was used to apply the heat to the additive manufactured deposits [22]. Ding et al. [23] proved that this heat source model is suitable for the simulation of a CMT heat source. The power density distribution of the region in front of the arc center and the region behind the arc center are described as follows:
qf
qr
x 2 y 2 z 2 exp 3 2 2 2 b c a f bc a f
(1)
x 2 y 2 z 2 6 3qf r exp 3 2 2 2 b c ar bc ar
(2)
6 3qf f
f f fr 2
(3)
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where af is the front ellipsoidal semi-axes length and ar is the rear ellipsoidal semi-axes length; b is the width of the heat source; c is the depth of the heat source; q is the energy input while considering the factor of efficiency and can be calculated as follows:
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q IU
(4)
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where I is the welding current; U is the welding voltage; ƞ is the thermal efficiency coefficient which ranges from 0 to 1 based on the different welding processes. The value of ƞ is set to 0.9 in this paper.
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According to the Ref [22] and [23], the welding parameters and the corresponding heat source properties are listed in Table 2. The method of determining the parameters (af, ar, b, c) of CMT process can be consulted in Ref [22] and [23]. The data was also verified by cutting out the cross-section of the weld bead. The voltage and amperage is obtained by measuring the waveform during deposition using Hall current and voltage sensor. The material properties were temperature-dependent which were obtained from Ref. [24]. A uniform coefficient of convection on all surfaces equal to 10 W (m2K)-1 and a radiation emissivity of 0.22 were assumed.
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Table 2 welding parameters and the corresponding heat source properties
I
U
af
ar
b
c
[m/min]
[A]
[V]
[mm]
[mm]
[mm]
[mm]
3.2
45
12.6
2.6
4.1
3.2
1.27
3.8
56
12.9
2.8
4.4
3.3
1.43
4.3
65
13.0
3.1
4.8
3.5
1.51
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WFS
2.3.3. Validation of the FE model Experimental validation was performed to prove the accuracy of the numerical simulation. It was verified by comparing the measured temperature cycling curves of point A (seen in Fig. 2(a)) with the corresponding simulated curves. The results are
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shown in Fig. 5, the dashed lines and solid lines are the experimental results and the simulated results, respectively. It is found that the trend in fluctuations of the simulated results approximately agrees with the actual measurements, indicating the effectiveness of the FE model. However, there are errors between the two curves, which are mainly due to the different heat dissipation conditions. In actual conditions, the heat prone to dissipation through the worktable is not considered in the FE model and, therefore, results in a lower actual temperature. The maximum peak temperature error is 19.3 ℃, which occurred on the first layer. This error tends to decrease layer by layer. The minimum peak temperature error that appeared on the 20th layer is 10.6 ℃. The average error of the simulated and experimental curves is 4.4 ℃. Although the simulation results lack some accuracy, they can provide the temperature variation trends for the deposition process. This still allows the thermal transfer behavior to be studied on the manufactured part as well as for the development of the dimensional control technique.
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Fig. 5 Comparison of the simulated and experimental thermal cycling curves
3. Typical thermal transfer cycle
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Fig. 6 depicts the temperature distribution on the cylinder part with the number of layers at 1, 5, 10, 20, 30, and 40, respectively. The welding current was 60 A, welding voltage was 12.9 V, and the travel speed was 8 mm/s. It was shown that both the maximum temperature and the high-temperature region increase along with the layer number, and the temperature gradient also shows a downward trend. This is expected because the heat dissipation condition becomes poorer with the increase in layers. When the workpiece height is relatively low, the heat is prone to dissipation through the substrate plate (seen in Fig.6 (a), (b) and (c)), and as a consequence the hightemperature area on the substrate plate becomes larger [25-27]. As the workpiece was ‘growing tall’, the heat accumulated on the workpiece (seen in Fig.6 (d), (e) and (f)),
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and the high-temperature area on the substrate plate became smaller until it dropped to room temperature.
Fig. 6 Temperature distribution of the cylinder component when the heat source moves to the midpoint
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of (a) 1st layer (b) 5th layer (c) 10th layer (d) 20th layer (e) 30th layer (f) 40th layer.
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The simulation results of a 40-layer cylinder component gives a clear understanding of the overall thermal transfer behavior during deposition process [28]. To analyze the thermal behavior for planning process parameters, the thermal cycle should be divided into different stages and discussed separately [29-30]. Fig. 7 shows the peak temperature and interlayer temperature of the midpoint in the different layers. According to the characteristics of the curve, a typical thermal cycle can be divided into three stages as follows:
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(1) Boost heating stage: At the beginning of the deposition process (first six layers), the peak temperature drastically increased from 430 ℃ to 620 ℃. However, the interlayer temperature varied within a narrow range around 61 ℃. This is mainly due to the strong heat dissipation effect of the substrate plate, which is also known as the ‘heat sink effect’. Such an effect results in a high cooling rate on the first deposited layers and makes them prematurely solidified and therefore strongly affects their dimensions. It is hard to ensure the dimensional accuracy in this stage due to the violent fluctuations of the peak temperature. However, the duration and amplitude of this stage can be effectively shortened by preheating the substrate plate or using high arc power
(2) Quasi-steady stage: In this stage, the mode of heat dissipation changes gradually
from heat conduction to heat radiation. Both the rising trend of the peak temperature and interlayer temperature starts to become even and gentle, which indicates that this stage is controllable. Therefore, the adaptive process parameters can be planned according to different temperature values to ensure uniform dimensions.
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(3) Steady stage: In this stage, the molten pool reaches the steady state, so that the heat flux of heat inflow and outflow from the molten pool remains unchanged. This stage is considered as the best case for WAAM because it enables the deposition of uniform layers without changing the deposition variables. In the present case, the steady stage could appear when the heat dissipation mode completely transfers to radiation from the deposited wall to the surrounding air, which means that the temperature of the substrate plate would drop to room temperature. As shown in Fig. 8, four temperature tracking points were inserted to monitor the temperature history of the first layer. It can be observed that the temperature is still maintained at approximately 100 ℃ even after 40 layers were deposited. Apparently, quite a few layers need to be deposited to reach the steady stage, indicating the difficulties in reaching such stage.
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Fig. 7 Peak temperature and minimum temperature for the midpoint of each layer
Fig. 8 Thermal history of the tracking points B, C, D, and E on the first layer
It is believed that a different process planning strategy should be used according to the characteristics within different stages. For instance, even the most undesirable boost heating stage could be moderated by the preheating method. Even though the steady stage is the most desired case, it is not easy to obtain and prone to being disturbed by various deposition variables such as arc power, substrate plate condition, and geometric shape of the model. Therefore, the proposed technique would mainly focus on the quasi-steady stage because it not only occupies the majority of the cycle but also shows regular and controllable characteristics.
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4. Proposed process planning technique
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In order to deal with the problem of the dimensional error caused by heat accumulation, this paper proposes a novel technique to generate the adaptive process parameters for the fabrication of the large-scale component. The basic idea of the proposed technique is to monitor the temperature of the current layer and predict the next one and then generate the adaptive process parameters that correspond to the predicted temperature to ensure the uniform layer dimensions. Therefore, two key parameters are listed as follows: An interlayer temperature monitoring method and prediction algorithm
Adaptive process modeling and matching
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In contrast to the traditional process planning method, the proposed technique allows the continuous deposition operation without the interlayer idle time or heat eliminating equipment, which is particularly beneficial for increasing production efficiency.
4.1. Interlayer temperature prediction
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4.1.1. Control node
As shown in Fig. 9, two critical control nodes are selected in each layer to perform the control strategy, which is defined as follows:
The process-varying node (PV node) is defined as the starting point of each layer. Every time the welding torch comes back to the starting point, the process parameters should be varied to match the predicted temperature of the next layer.
The temperature-monitoring node (TM node), located at the middle point of each layer, can show the real-time temperature which is measured by an infrared thermometer. Such temperature values are only read and substituted into the
Fig. 9 Scheme diagram of the control nodes
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temperature prediction equations when the welding torch comes back to the starting point, then the interlayer temperature of the next layer can be calculated.
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The surface temperature of the control nodes is captured using the thermal infrared camera (Flir SC620). In the present study, the temperature range was set in 0500 ℃. The spatial resolution was set to 0.66 mm/pixel. The frame rate was set to 15 Hz. The emissivity of the aluminum material is set to 0.22. The IR camera was directly facing the TM node during deposition. Once the welding torch moves to PV node, the acquisition signal will be triggered and the real-time temperature data will be transferred to the industrial computer. With the aid of the MATLAB, the temperature data was processed using the Eq. (5), (6) and (9) to generate the new welding parameters (wire feed speed and travel speed). With the aid of Robot Art online programming software, the new parameters would be activated to perform the next layer.
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4.1.2. Temperature calculation
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According to the analysis in section 3, the quasi-steady stage is believed to be regular and predictable. As shown in Fig. 10, when reaching the quasi-steady state, each layer number corresponds to a different temperature. Assuming Ln is the current layer, Ln-1 and Ln+1 are the former layer and the predicted layer, respectively. In order to predict the interlayer temperature of the next layer (Tn+1*), the heat transfer coefficient λ is defined to describe the rising temperature trend of the former two layers, which can be calculated as follows:
Tn Tn 1
(5)
where Tn-1 and Tn is the actual temperature of the Ln-1 and Ln, respectively, which were
measured by an infrared thermometer. Such heat transfer coefficient λ could be passed on to the next layer. Therefore, the Tn+1* could be calculated as follows:
Tn1 * Tn
(6)
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When obtaining the Tn+1*, a new group of adaptive process parameters would be generated simultaneously at the PV node. Analogously, Tn+2 could also be obtained by substituting Tn and Tn+1 into the Eq. (5) and (6). Note that there is an inevitable error between the predicted T* and measured T, which is mainly caused by the variable path length in each layer. For instance, as shown in Fig. 1, the closed path length increases gradually along with the diameter, which results in the longer cooling time of the TM node and, therefore, the gradually diminishing temperature difference. However, such error is insignificant because the slope of the temperature curve in the quasi-steady stage is much smaller compared with that in the boost heating stage, and this will also be verified through the experimental case in section 5.
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Fig. 10 Interlayer temperature prediction procedure
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4.2. Adaptive process model
4.2.1. Fixed Slayer planning strategy
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The dimensional problem can be presented by the shape variation of the crosssection of the single layer. As shown in Fig. 11, the basic idea of the adaptive process model is to deposit layers with a fixed cross-sectional area (Slayer) and control their shapes.
Fig. 11 Schematic representation of the cross-section of the deposited wall
d 2WFS
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Slayer
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The Slayer can be calculated as follows [20]:
4TS
(7)
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where d is the diameter of the welding wire, WFS is the wire feed speed, and TS is the travel speed of the welding torch. It is obvious that, in order to obtain the fixed Slayer, the ratio of the wire feed speed to travel speed (RWT) should be kept unchanged. In other words, the fixed Slayer could be obtained by increasing or decreasing WFS and TS at the same time.
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However, the geometric shapes associated with different WFS or TS under constant RWT are quite different. As shown in Table 3, three case parameter groups were chosen to illustrate the dimensional differences and their effects on thermal behavior. The welding current and voltage output from the CMT source were recorded and the lineenergy Q was calculated as follows:
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Q
TS
(8)
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The results show that the geometric shape turns from ‘narrow and tall’ to ‘wide and short’ with the increase of the WFS, this is because the higher line-energy Q would result in a wider molten pool and, therefore, wider layer width [19,20]. Table 3 Three case parameter groups with the same RWT
WFS
TS
I
U
Q
Layer width
Layer height
[m/min]
[mm/s]
[A]
[V]
[J]
[mm]
[mm]
3.2
8.00
45
12.6
510.3
4.41
1.71
3.8
9.50
56
12.9
650.2
4.83
1.56
4.3
10.75
65
13.0
760.5
5.31
1.42
4.2.2. Effect of line-energy on thermal behavior
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The three case parameter groups also have a significant effect on the temperature behavior for the workpiece. As shown in Fig. 12, the peak temperature significantly increased with the increase of WFS, indicating that the line-energy Q directly determines the available layer width. On the contrary, the interlayer temperature increased slightly along with the WFS, this is mainly due to the excellent heat dissipation in aluminum alloy. It is worth noting that, the line-energy Q has no significant effect on the number of layers required to reach the quasi-steady state, and the slope of the curves of the peak temperature is almost parallel. Therefore, it can be concluded that the adaptive process model is able to control dimensional accuracy and adjust heat input at the same time.
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Fig. 12 Temperature comparison using different line-energy
4.2.3. Dimensional prediction model
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In order to adapt the process parameters into the sliced path associated with different interlayer temperature, the central composite rotatable design (CCRD) method is applied to establish the dimensional prediction model. The RWT is set constant at 0.4, the wire feed speed (WFS) and interlayer temperature (T) were chosen as the input variables, and the layer width (Wlayer) is regarded as the output variable. Their upper and lower limits and coding are given in Table 4. It should be emphasized that the parameters should be selected within certain range to ensure the good layer formation. The parameter range was designed by performing experimental trails which can be referred in our previous literature [20, 31]. Totally 20-layered thin-wall part was deposited layer by layer for each experiment
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groups. To measure the effective wall width, cross sections were intercepted from the middle of the thin-wall specimen. After the process of grinding, polishing and corroding, the morphology photos were taken by OLMPUS laser scanning confocal microscopy. The schematic representation of the cross-section measurement system was shown in Fig. 13.
Fig. 13 Schematic representation of cross-section
Factor level
WFS
T
(m·min-1)
(℃)
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Unit
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Table 4 Coding for factor and level
2.8
20
-1
3.3
46
0
4.5
110
1
5.7
174
1.41421
6.2
200
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-1.41421
For two factors chosen with five levels, the required number of experiments is 13 according to CCRD. Using the software of design expert, the experimental samples can be designed, and then the experiment was performed using the setups presented in section 2. The detailed calculation method can be referred to our previous literature [20, 31]. The final regression model was obtained as follows:
Wlayer 0.2476 1.2415WFS 0.0147T 1.5032 10-3WFST
(9)
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The corresponding 2D layer width plot is shown in Fig. 14, it can be seen that the adaptive WFS can maintain a target layer width under different interlayer temperature and, therefore, keep a constant layer height. This prediction model will be used to adjust the WFS and TS at the PV node to ensure the uniform layer dimensions.
5. Experimental verification
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Fig. 14 2D layer width plot
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The proposed planning technique is verified by fabricating a large-scale model. Fig. 15 shows the dimensions of the typical model, it is featured by the continuously changing diameter from the bottom up, and the wall thickness is set at 6 mm. As shown in Fig. 16, a rotary table helps to keep the building direction oriented vertically and rotated during deposition. Thus, the welding torch only needs to lift when a layer is finished.
Fig. 15 Dimensional description of the test model
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Fig. 16 Scheme diagram of the deposition mode
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The process parameters generated for the first 100 layers were listed in Table 5. Starting from the 3th layer, the interlayer temperature was predicted according to the actually measured temperature at the TM node of the former two layers using the Eq. (5) and (6). Then, the process parameter was generated by substituting Wlayer = 6 and the predicted T into the Eq. (9). Therefore, a new group of WFS and TS can be generated and activated at the PV node. Analogously, the subsequent layers can be planned one by one following the steps above with the aid of RobotArt online programming software.
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The final manufactured part is shown in Fig. 1. In regard to the former 100 layers, the welding parameters changed significantly with the increase of the interlayer temperature. It can be seen from Table 5 that the proposed technique has high accuracy in predicting the interlayer temperature and the layer dimensions. And, as expected, the macrostructure (seen in Fig. 16) shows that the deposited wall has uniform dimensions. The final manufactured part is shown in Fig. 1, it indicates that the proposed planning technique is effective and practicable. Table 5 Deposition variables and forming dimensions PV node
TM node
Actual dimensions
Layer
WFS
TS
Predicted
Measured
Layer width
Layer height
number
[m/min]
[mm/s]
temperature [℃]
temperature [℃]
[mm]
[mm]
5.4
8
-
105.3
6.51
1.95
10
4.4
11
90.7
90.8
6.03
1.25
20
4.3
10.75
96.7
98.3
5.94
1.27
30
4.2
10.5
106.9
108.4
6.08
1.24
40
4.2
10.5
117.1
118.9
5.91
1.28
50
4.1
10.25
126.8
129.2
5.97
1.26
60
4.1
10.25
137.5
140.3
6.04
1.25
70
4.0
10
146.6
148.7
5.95
1.27
80
4.0
10
151.4
152.1
6.01
1.25
90
4.0
10
153.5
153.8
100
4.0
10
153.2
153.2
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1
1.24
6.07
1.24
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6.09
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The layer width of the former 20 layers is shown in Fig. 17. In order to show the advantages of the proposed technique, the experiment was also performed using the constant parameters (WFS=4.0 m/min, TS=10 mm/s) and 1 min interval time. It can be seen that the proposed technique has better dimensional accuracy comparing with others. The variance of the optimized layer width is 0.1178 while the others are 0.6399 and 0.4868, respectively. In addition, the production time of the optimized method is 20 min less than the normal method with 1 min idle time. It is confirmed that the proposed technique succeeds in continuously fabricating the large-scale part with a total of 753 layers. The part also has high dimensional accuracy and manufacturing efficiency.
Fig. 17 Comparison of the layer width using different deposition strategy
6. Conclusion This paper proposes a novel process planning strategy for WAAM through the generation of variable process parameters in accordance with the different thermal conditions. This ensures dimensional accuracy and improved manufacturing efficiency. With the aid of the finite element model, the typical thermal transfer cycle of the workpiece was analyzed and then divided into three stages (e.g., boost heating stage, quasi-steady stage, and steady stage). Among them, the quasi-steady stage occupies the majority of the cycle and was found to be regular and controllable.
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When depositing parts, the thermal condition of the subsequent layers would be predicted with the aid of the control nodes and developed algorithm, and then the process parameters (e.g., wire feed speed and travel speed) could be varied according to the predicted interlayer temperature using the developed adaptive process model to ensure the uniform layer dimensions. The effectiveness of the proposed technique is verified by a large-scale shell-shaped component with a total of 753 layers. The result shows that the proposed technique has high accuracy in predicting the interlayer temperature as well as the uniform dimensions. Moreover, the manufacturing efficiency is significantly improved because no idle time is introduced.
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Conflict of interest statement
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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of the manuscript entitled ‘Dimensional control of wire-arc additive manufacturing for large-scale shell-shaped component: A process planning strategy considering thermal behavior’.
Acknowledgment This work was supported by the National Natural Science Foundation of China (no. 51805013) and Foundation Research Fund of Beijing University of Technology (no. 001000546318526).
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