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Development of portable power monitoring system and grinding analytical tool
Journal of Manufacturing Processes 27 (2017) 188–197
Contents lists available at ScienceDirect
Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro
Development of portable power monitoring system and grinding analytical tool Y.B. Tian a,b,∗ , F. Liu b , Y. Wang b , H. Wu b a b
School of Mechanical Engineering, Shandong University of Technology, 266 Xinchun West Road, Zhangdian District, Zibo, 255049, PR China Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, 638075, Singapore
a r t i c l e
i n f o
Article history: Received 16 December 2016 Received in revised form 9 April 2017 Accepted 3 May 2017 Keywords: Precision grinding Monitoring system Analytical tool Process optimization Condition monitoring Energy efficiency grinding
a b s t r a c t Grinding is one of the most critical surface finishing processes to meet the demand of tight tolerance, good surface integrity, high productivity and low cost in manufacturing industries. However, grinding is considered to be a complex and highly non-stationary process owing to a huge number of irregular cutting edges in abrasive tools. The abrasive tool and process condition monitoring is a well-recognized approach to track process change and analyze tool condition. In this work, we developed a portable power monitoring system with specially designed grinding analytical software. The hardware architecture and software modules were introduced in detail. The main functions in the software module comprised of signals acquisition, feature extraction and data calculation, and data analytical toolkit. Knowledge-based analytical tool was established through the correlation between grinding power/energy and grinding conditions. The actual application of the portable power monitoring system for grinding process was further demonstrated with some case studies. The developed portable power monitoring system is easy and convenient to implement in production conditions for improvement and optimization of grinding process. © 2017 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.
1. Introduction Precision grinding is widely used to efficiently attain good surface integrity with high accuracy in manufacturing industries. The applications of grinding technology can be found in most industrial sectors such as automotive, aerospace, marine, nuclear, medical devices, optics, electronics and semiconductor engineering [1–4]. Nowadays, grinding becomes the most critical surface finishing process. It was reported that grinding accounted for ∼70% within the spectrum of precision machining [2,5]. However, the grinding process is characterized by a huge number of irregular abrasive grains undergoing non-uniform wear. It is recognized as a complex and highly non-stationary process [2]. Moreover, abrasive grains in grinding tools are high negative rake angle geometry. Hence, the grinding process requires large energy per unit volume of material removed as compared with other mechanical machining processes. Grinding specific energy consumption is typically higher than the energy required for melting the materials. For example,
∗ Corresponding author at: School of Mechanical Engineering, Shandong University of Technology, 266 Xinchun West Road, Zhangdian District, Zibo, 255049, PR China. E-mail addresses: [email protected], [email protected] (Y.B. Tian).
melting iron or nickel consumes roughly less than the energy of 10 J/mm3 . However, the specific grinding energy consumption is typically 30–50 J/mm3 in the steels grinding with conventional aluminum-oxide abrasive wheels [5]. High grinding energy consumption indicate large friction thermal and high machining force, which leads to fast wear of the grinding wheel/abrasive tool as well as potential surface and subsurface damage into the workpiece such as grinding burn, white layer, and residual stresses. As abrasive tool and tool conditions change with the grinding operation progressing, the grinding quality and energy/power consumption will be affected. Close monitoring and comprehensive analytical tool development is very important for the grinding process to reduce cycle time and wheel wear, and improve grinding quality as well as energy consumption. Great efforts have been made in the development of a sensorbased grinding monitoring to improve the grinding efficiency and quality. Force/pressure monitoring, temperature/heat monitoring, power monitoring, and acoustic emission were well reported in many applications of grinding [1,2,5,6]. Toshoff et al. (2002) reviewed the measurement approaches and sensors used to monitoring the micro- and macro-topography of the active layer of the grinding wheel [7]. K. Wegener et al. (2011) summarized the recent macro-wear and micro-wear monitoring technique for grinding wheels [2]. In many laboratory grinding tests, force mea-
http://dx.doi.org/10.1016/j.jmapro.2017.05.002 1526-6125/© 2017 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.
Y.B. Tian et al. / Journal of Manufacturing Processes 27 (2017) 188–197
surements are able to give valuable information on the grinding process. Grinding force monitoring and result analysis has been widely adopted in the academic grinding researches [1,2]. However, it has seldom performed on the shop floor owing to relatively high cost (relatively expensive for a dynamometer), cumbersome in real production operation, and change of machine setup and alteration of stiffness of the grinding system. Power monitoring, however, is an easy and convenient way to obtain useful information for the grinding process at relative low cost. In particular, there is no special design of complex fixturing and modification of machine tool. Power monitoring cells do not encroach inside the workspace of the machine tool and have no negative effects on the grinding process. A power cell is capable of measuring the spindle or axis driven motor power directly for alternating current (A.C.), direct current (D.C.) or variable frequency. The sensor is easy to connect to the electrical cabinet on both new and existing grinding machine. Utilization of three balanced Hall Effect sensors is able to eliminate large phase shift errors [1]. Effective power measurement has the advantage over simple current measurement as current is not a sensitive indicator of power at low loads in three-phase motors [3,4]. Power is linear relationship with motor load. A change in motor load reflects a change in motor power (horsepower or kilowatts). Current doesn’t vary significantly until the motor reaches 50% of capacity. At light loads, power (horsepower or kilowatts) is 10 times more sensitive than current (amps). It has been approved that small cutting tools can also be monitored using an additional logarithmic signal amplifier in machining process [1–4]. Therefore, on-line power monitoring has been assisted for the grinding analysis. Grinding power is even recognized as one of important grinding index, like grinding force and G-ratio, to characterize the grinding process and abrasive tool performance [2,7–9,11–16]. K. Subramanian et al. [12,13] proposed system approach in precision production grinding of ceramics and established six microscopic interaction models in the grinding contact zone. The six microscopic interactions in the grinding interface between abrasive tool and work material are able to be decomposed and linked with grinding power information. A.P. Walsh [14], W. Tian [15], and R. Vairamuthu [16] reported performance analysis of cylindrical grinding process through monitored power signals. In industrial aspect, several grinding-wheel manufacturers have developed their own power meters. For examples, Norton (now Saint-Gobain) owned Field Instrumentation System (F.I.S.). Slip Naxos/Winterthur developed NaxoTech. Camel/C.G.W. called Grinding Knowledge Systems (G.K.S). Nevertheless, grinding power monitoring is not fully explored. The current existing systems are still focused on measurement of grinding power and output of a plot of power versus process time from the data-logger. Grinding analysis method is only based on the comparison of the different grinding power pattern and magnitude under different abrasive tools and operating parameters. However, for complex grinding process, the current power monitoring work is actually limited. The further target is therefore motivated to extract the useful power (also grinding energy) signals recorded and to develop an analytical toolkit to comprehensively link the grinding inputs (machine tool, abrasive tool, workpiece, coolant, operating factors) and outputs (technical and economic outcome), as illustrated in Fig. 1. In this work, a portable power monitoring system with specially designed grinding analytical software was developed. The main software modules included signals acquisition, feature extraction and data calculation (grinding energy consumption and proportion included), and data analytical toolkit. Knowledge-based analytical tool was established through the correlation between grinding power/energy and grinding conditions. The actual application was further demonstrated with some case studies for grinding cycle reduction, energy consumption saving, grinding wheel assess-
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Fig. 1. Illustration of portable power monitoring system and analytical tool.
ment/selection, wheel (dressing/cleaning) condition monitoring, grinding quality improvement, and grinding burn avoidance. 2. Portable power monitoring system development 2.1. Hardware architecture A real-time portable power monitoring system was developed with integration of a portable power meter (PPC-3), a data acquisition system (A/D converter cDAQ 9171 and NI 9125) and a computing system with specially designed analytical software. The hardware architecture is illustrated in Fig. 2. It is typical sensorbased monitoring system for the grinding process. Fig. 3 shows the core hardware devices of the actual portable power monitoring system (Note that computing system and analytical tool/software are not shown). The power cell is able to measure the main spindle or axis drive power for single phase, three phases, DC, or variable frequency drive. The true power in kW or HP is given by measuring, rectifying and calculating from current with three clamp-on Hall-Effect transducers and voltage with three clip-on voltage sensors. The power cell possesses two isolated analog outputs i.e. 0–10 V DC minimum impedance 2,000 and 4–20 mA DC maximum impedance 500 . The measurement response time of the selected power sensor is 0.015–10 s. Frequency range is DC 0–1000 Hz. Power range is maximum 100 kW (or 130 HP) with six full scale ranges of 3 kW, 5 kW, 10 kW, 25 kW, 50 kW and 100 kW (or 4 HP, 10 HP, 20 HP, 50 HP, 100 HP, and 130 HP) to achieve maximum sensitivity. The case size of the power cell is 470 mm × 370 mm × 145 mm, which is light and portable. The power output of analog signals are discretized and acquired with a USB-based data acquisition system (DAQ). The voltage range is −10 V – +10 V. It has 4 channels with 16 bits resolutions and the sampling rate is 250 kS/s. 2.2. Software modules The power/energy monitoring software modules were specially designed and programmed with LabVIEW system design software (National Instruments). The designed functions consist of signals acquisition, feature extraction and data calculation, and data analytical toolkit. Fig. 4(a)–(c) shows the overall software interface for the three modules. After it was programmed and designed, the modules were built up and isolated to run all of the functions without the other commercial software. In the signal acquisition module, the real-time power data of main spindle motor were automatically collected (also be used for other axis drive motors). As shown in Fig. 4(a), power gears were specially designed for six full scale power ranges of 3 kW, 5 kW, 10 kW, 25 kW, 50 kW and 100 kW. This is used to adjust maximum sensitivity and obtain true power reading for the power cell. The response time was designed as seven options i.e. 0.015 s, 0.075 s, 0.15 s, 0.5 s, 1.0 s, 5.0 s and
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Fig. 2. Hardware architecture of monitoring system.
10.0 s. The power data storage files and file path were easily created and edited in the module. The corresponding channel of the data acquisition system (DAQ) connected is able to be rectified. Three control buttons, i.e. “Start”, “Stop” and “Pause”, were designed for the monitoring control. Real-time true power signals were monitored in a designed display window, which were also recorded in the storage file for further data extraction, calculation and analysis. Grinding peak power was plotted in a digital indicator, in which pre-warning peak power limit was manually set. Alert information (or alarm) will be generated when the grinding peak power exceeds the limit. This is able to prevent overload of the main spindle and detect collisions during grinding operations. Fig. 4(b) shows the module of feature extraction and data calculation. The stored raw power data can be opened and displayed in the formats of both measurement data file (.lvm) and binary file (.tdms). There is a function designed to merge different storage files with same format into one single file. This is convenient to realize the analysis and comparison of recorded data in one display window. Low pass filter was programmed with nineteen window options and selectable low cutoff frequency. The measured raw data of grinding power could be filtered and smoothened by operation of the low pass filter
buttons. The filtered data is also able to be converted into MS-Excel format file via the “Export” button. As shown in Fig. 4(b), the raw power data are plotted in the display window, which is able to be captured (zoon in or out) any interested feature of recorded power patterns. After the power feature with selectable error index (0–1, close to 1 resemble the defined feature) is defined, it will automatically identify the grinding power pattern. Manual identification of power pattern is also optional for some difficult-to-identify power patterns and analysis of short grinding cycles. After starting time, ending time, idling power and cutoff power are further set up in the display window, approaching time, approaching energy, actual grinding time, actual grinding energy, peak grinding power, average approaching power, average grinding power, and summary the proportion of grinding energy and approaching energy, will be automatically calculated and reported in this module. In order to further link the grinding inputs (machine tool, abrasive tool, workpiece, coolant, operating factors) and outputs (technical and economic output), the six microscopic interactions (which reflect overall grinding inputs/loads) in the grinding interface between abrasive tool and work material proposed by K. Subramanian et al.
Fig. 3. Portable power monitoring system.
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Fig. 4. Interface of software modules.
[12,13] are adopted in the grinding analytical toolkit development (see Fig. 4(c)). As explained in Fig. 5, the grinding power consumption (P(t)) versus material remove rate was decomposed and correlated with different grinding power i.e. initial threshold power (abrasive grain
sliding, plowing and bond/work sliding) (Pth (0)), effective cutting power (abrasive grain/work cutting) (Pc ), loading (chip/bond sliding, chip/work sliding) caused friction power Pf (t), glazing (abrasive grain plowing, sliding, and bond/work sliding) caused friction (Pth (t)) via curve fitting into a straight line at two individ-
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an important index to evaluate and compare the performance of the different grinding wheels. Three sub-modules i.e. “wheel comparison”, “wheel conditioning”, and “summary”, were developed. The sub-modules of “wheel comparison” and “wheel conditioning” have the functions of comparison of grinding power pattern, correlation graphs between average power and material removal rate, correlation graphs between specific grinding energy and material removal rate, grinding power/energy graphs with grinding conditions such as maximum undeformed chip thickness. In the sub-module of “summary”, the consumptions of time and energy under different grinding and approaching stages, and their proportions are clearly tabulated for process improvement and operation management.
3. Application in grinding process 3.1. Grinding strategy optimization
Fig. 5. Decomposition of grinding power consumption [12–15].
ual grinding cycles [12–15]. Grinding power consumption can be expressed accordingly as below. P(t) = Pth (0) + Pth (t) + Pc + Pf (t)
(1)
In the data analytical toolkit module in the designed software shown in Fig. 4(c), a build-in algorithm was developed to extract initial threshold power Pth (0), time dependent threshold power Pth (t), ideal cutting power Pc , time dependent friction power Pf (t) from the total measured power at a given material removal rate (Qw ). The slop rate of grinding power (power increase rate) against material removal rate can be also automatically deduced, which is
A serial of grinding experiments were conducted on a precision surface grinding machine (Okamoto ACC-63DXNC), in order to demonstrate the actual application of the developed portable power monitoring system in grinding process. Block workpiece made of typical hard-to-grind material, Ni718, was employed. The initial block dimension was 50 mm × 50 mm × 10 mm. An electroplated CBN wheel was used. The wheel diameter and width were 204 mm and 8 mm, respectively. Mesh size was 100/120#. Average abrasive grain diameter is equivalent to 149 m. Grinding speed was selected as 35 m/s (i.e. wheel ration speed was 3360 rpm). Feed rate was set as 200 mm/min. Total material removal was 30 m in workpiece height. In initial grinding scenario called Scenario #1, three stages i.e. rough grinding, semi-finishing, and final-finishing, were arranged. Total material removal required was 30 m in each grinding cycle. The depths of cut in the three stages were set as
Fig. 6. Power pattern under grinding scenario #1.
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Fig. 7. Power pattern under grinding scenario #2.
Table 1 Grinding parameters. Grinding parameters
Values
Wheel diameter (mm) Wheel width (mm) Wheel speed (m/s) Table speed (mm/min) Depth of cut (m) (Scenario #1) Depth of cut (m) (Scenario #2)
204 8 35 200 5, 10, 15 10, 20
15 m, 10 m, and 5 m, respectively. The grinding parameters are summarized in Table 1. Fig. 6 shows the power profile recorded versus process time in one grinding cycle. The power distributions of approaching, rough grinding, semi-finishing, and final-finishing were clearly exhibited. After the starting time, ending time, idling power and cutoff power were set up in the designed software, the power, energy consumptions and energy proportions in each stage were directly obtained, as summarized in Table 2. It is seen that the most energy (70.57%) was consumed by approaching while for actual material removal, the total of rough grinding, semi-finishing, and final finishing, occupied less than 30%. In order to reduce cycle time and save energy consumption, another grinding strategy (scenario #2) were proposed and adopted. As most of time and energy was consumed during the approaching stages, the total of approaching time was therefore reduced to 55.3 s with the consideration of the safety concerns (e.g. reasonable air-cut setting). Meanwhile, two stages i.e. rough grinding and final-finishing was reconfigured. The depths of cut of the rough grinding and final finishing were set to be 20 m and 10 m, respectively in the new grinding scenario #2. The requirement of total material removal of 30 m was still met. Fig. 7 exhibits the new power pattern recorded versus process time for the implementation of the new grinding strategy.
It can be seen that the total cycle time was reduced from initial 179.6 s to 87.5 s. The total cycle time was improved 51.28%. The absolute energy consumed by approaching decreased from 41.98 kJ to 17.7 kJ (energy saving is up to 57.84%). Total energy consumption (sum of approaching and grinding) decreased from intial 59.49 kJ to19.58 kJ. The total energy saving is up to 67.09% with the optimization of grinding strategy. The details of grinding power/energy consumption are shown in Table 3. Hence, with the assistance of the developed power monitoring system and analytic toolkits, it is confirmed that it is an effective tool to optimize grinding strategy, reduce grinding cycle time and save machining energy.
3.2. Operating parameters optimization Except for the output of a plot of power versus process time, it is convenient to link the operating parameters with the recorded grinding power and energy in the grinding analytic tool. Power is linear relationship with motor load. During grinding, external load can be characterized by specific material removal rate (related to feed rate, depth of cut and wheel width), maximum undeformed chip thickness (i.e. abrasive grain depth of cut, overall grinding parameter), and equivalent grinding thickness (related to feed rate, depth of cut and wheel speed). With the grinding outcome of power and energy under the grinding conditions (scenario #1) shown in Table 1, the correlation of the average power with specific material removal rate, maximum undeformed chip thickness and equivalent grinding thickness, and the correlation between specific grinding energy and specific material removal rate were plotted in Fig. 8(a)–(d). The effects of the important operating parameters such as feed rate, depth of cut and wheel speed could be directly linked with grinding power and energy. The prediction and analysis of grinding power and energy was easily realized for process improvement and energy-efficiency machining under the differ-
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Fig. 8. Average power and specific grinding energy versus grinding conditions.
Table 2 Grinding power/energy consumption under grinding scinario #1.
Time (s) Average power (kW) Energy consumption (kJ) Proportion of energy (%)
Approaching
Rough grinding
Semi-finishing
Final-finishing
Total
131.20 0.320 41.98 70.57
16.50 0.375 6.19 10.40
15.80 0.363 5.74 9.64
16.10 0.347 5.59 9.39
179.60 – 59.49 –
ent grinding conditions with assistance of the developed analytical tool.
oped power (energy) monitoring system and this model allows for the prediction of grinding regimes in which undesired burning can be avoided based on energy considerations. Furthermore, critical grinding conditions can be deduced to avoid grinding burns.
3.3. Avoidance of grinding burn In the previous publications reported by Malkin, Guo and Brinksmeier [5,10], the critical specific energy was defined as the onset of grinding burn, which followed a straight line if plotted versus deq 1/4 ae −3/4 vw −1/2 in the unit of mm−1 s1/2 , as shown in Fig. 9. deq is equivalent wheel diameter. ae is grinding depth of cut. vw is feed rate in surface grinding. The application of the devel-
3.4. Grinding wheel assessment In the data analytical toolkit module in the designed software, a build-in algorithm was developed to extract initial threshold power Pth (0), ideal cutting power Pc , and slop rate (power increasing rate) for the purpose of wheel comparison, as shown in Fig. 10. In order
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Fig. 9. Correlation between grinding energy and process conditions for the occurrence of grinding burn (according to publications by Malkin, Guo, Brinksmeier [5,10]).
Fig. 10. Comparasion of average grinding power and specific grinding energy for three different grinding wheels.
to demonstrate the function of wheel comparison, grinding experiments were conducted with three different wheels under same grinding parameters. The average grinding power for the three individual grinding wheels (Green, White and Red lines in Fig. 10) were compared against material removal rate. In grinding process, three microscopic interactions occur between abrasive tool/wheel and workpiece, i.e. sliding (rubbing), ploughing, and cutting. The initial threshold power Pth (0) indicated the initial sliding and ploughing power. The material removal by abrasive tool/wheel is needed to exceed the threshold power Pth (0). Less the initial threshold power Pth (0) is, easier the material removal is (or shaper the abrasive tool/wheel is). The initial threshold power Pth (0) of the wheel in red line is only 0.0063 kW which is 17% of the wheel in green line
which is indicated as 0.0369 kW, as shown in Fig. 10. Therefore, the cutting capability of wheel in red line (shaper, less energy consumption) is better than that of the wheel in green line at the beginning stage of the grinding process. The initial threshold power Pth (0) of the wheel in white line is the middle of red line and green line, so is the cutting capability at the beginning of grinding. Specific grinding energy, SGE (kJ/mm3 ), is energy consumption per unit of material removal. Large SGE indicates more energy consumption of material removal and worse performance of grinding wheel (like the grinding wheel shown in green line in Fig. 10). Grinding wheel with higher SGE usually results in grinding burn (or high grinding temperature) and small G-ratio. It is an indicator to distinguish wheel performance. In this case, the wheel in red line is the best
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Fig. 11. Comparasion of average grinding power under different wheel wearing stages.
performance, followed by white line while the wheel in green line performed the worst in the actual grinding. 3.5. Grinding wheel condition monitoring and analyzing For the purpose of comparison of wheel wearing and conditioning, a build-in algorithm was developed to extract time dependent threshold power Pth (t), ideal cutting power Pc , time dependent friction power Pf (t), and slop rate (power increasing rate) in the data analytical toolkit module in the designed software. As shown in Fig. 11, three different wearing (similar application in wheel conditioning using the analytical toolkit) stages of a same grinding wheel were compared which was plotted in Red, Green, and Purple lines. The time dependent threshold power Pth (t) is an indicator of grinding wheel condition (degree of sharpness). Pth (t) is glazing (abrasive grain plowing, sliding, and bond/work sliding) caused friction power. Large Pth (t) value indicated that wheel became dull and needed to be dressed. For example, the time dependent threshold power Pth (t) at the beginning is 0.0172 kW. After three days, the power increased 0.0065 kW. Another three days later, the value increased up to 0.0183 kW, which was 2.06 times of that at the beginning. The specific grinding energy was 2.06 times increased. With the prediction of grinding regimes of undesired burn (see Fig. 9), the increase of the threshold power Pth (t) is able to be used to determine the wheel dressing frequency to avoid grinding burn. The time dependent friction power, Pf (t), is caused by the friction of chip/bond sliding and chip/work sliding, which is an indicator of wheel loading level. The time dependent friction power, Pf (t), can be used to evaluate the cooling performance and coolant assessment. Table 3 Grinding power/energy consumption under grinding scinario #2.
3.6. Other applications Equipment health with different stiffness can be monitored and compared using power profiles. The power signal can be used to help identify a problem (such as truing problem) and troubleshooting. For example, when grinding wheel is needed to be trued to produce the required macroscopic wheel shape, the grinding power patterns will become strange and different from those recorded at normal stages. The power monitoring can help to find the source of grinding chatter, optimize dressing parameters, and evaluate grindability of different materials, cooling performance of different coolant and consistency of grinding wheel quality. 4. Summary This paper presented a real-time portable power monitoring system, which was integrated with a portable power meter, a data acquisition system and a computing system with specially designed analytical software. The main software modules is comprised of signals acquisition, feature extraction and data calculation (approaching and grinding energy consumption and their proportions included), and knowledge-based data analytical toolkit. The developed portable power monitoring system is easy and convenient to implement in production conditions. Grinding case studies were demonstrated the actual application with the developed portable power monitoring system for grinding cycle reduction, energy consumption saving, grinding wheel assessment/selection, wheel (dressing/cleaning) condition monitoring, grinding quality improvement, and grinding burn avoidance. In the future, the grinding analytical tool can be further upgraded to add the function of intelligent grinding expert database. With the new intelligent analytical tool, process window is able to be designed and optimized according to process requirements.
Approaching Rough grinding Final-finishing Total Time (s) Average power (kW) Energy consumption (kJ) Propotion of energy (%)
55.3 0.320 17.70 90.38
16.0 0.073 1.17 5.97
16.2 0.044 0.71 3.64
87.5 – 19.58 –
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Contents lists available at ScienceDirect
Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro
Development of portable power monitoring system and grinding analytical tool Y.B. Tian a,b,∗ , F. Liu b , Y. Wang b , H. Wu b a b
School of Mechanical Engineering, Shandong University of Technology, 266 Xinchun West Road, Zhangdian District, Zibo, 255049, PR China Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, 638075, Singapore
a r t i c l e
i n f o
Article history: Received 16 December 2016 Received in revised form 9 April 2017 Accepted 3 May 2017 Keywords: Precision grinding Monitoring system Analytical tool Process optimization Condition monitoring Energy efficiency grinding
a b s t r a c t Grinding is one of the most critical surface finishing processes to meet the demand of tight tolerance, good surface integrity, high productivity and low cost in manufacturing industries. However, grinding is considered to be a complex and highly non-stationary process owing to a huge number of irregular cutting edges in abrasive tools. The abrasive tool and process condition monitoring is a well-recognized approach to track process change and analyze tool condition. In this work, we developed a portable power monitoring system with specially designed grinding analytical software. The hardware architecture and software modules were introduced in detail. The main functions in the software module comprised of signals acquisition, feature extraction and data calculation, and data analytical toolkit. Knowledge-based analytical tool was established through the correlation between grinding power/energy and grinding conditions. The actual application of the portable power monitoring system for grinding process was further demonstrated with some case studies. The developed portable power monitoring system is easy and convenient to implement in production conditions for improvement and optimization of grinding process. © 2017 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.
1. Introduction Precision grinding is widely used to efficiently attain good surface integrity with high accuracy in manufacturing industries. The applications of grinding technology can be found in most industrial sectors such as automotive, aerospace, marine, nuclear, medical devices, optics, electronics and semiconductor engineering [1–4]. Nowadays, grinding becomes the most critical surface finishing process. It was reported that grinding accounted for ∼70% within the spectrum of precision machining [2,5]. However, the grinding process is characterized by a huge number of irregular abrasive grains undergoing non-uniform wear. It is recognized as a complex and highly non-stationary process [2]. Moreover, abrasive grains in grinding tools are high negative rake angle geometry. Hence, the grinding process requires large energy per unit volume of material removed as compared with other mechanical machining processes. Grinding specific energy consumption is typically higher than the energy required for melting the materials. For example,
∗ Corresponding author at: School of Mechanical Engineering, Shandong University of Technology, 266 Xinchun West Road, Zhangdian District, Zibo, 255049, PR China. E-mail addresses: [email protected], [email protected] (Y.B. Tian).
melting iron or nickel consumes roughly less than the energy of 10 J/mm3 . However, the specific grinding energy consumption is typically 30–50 J/mm3 in the steels grinding with conventional aluminum-oxide abrasive wheels [5]. High grinding energy consumption indicate large friction thermal and high machining force, which leads to fast wear of the grinding wheel/abrasive tool as well as potential surface and subsurface damage into the workpiece such as grinding burn, white layer, and residual stresses. As abrasive tool and tool conditions change with the grinding operation progressing, the grinding quality and energy/power consumption will be affected. Close monitoring and comprehensive analytical tool development is very important for the grinding process to reduce cycle time and wheel wear, and improve grinding quality as well as energy consumption. Great efforts have been made in the development of a sensorbased grinding monitoring to improve the grinding efficiency and quality. Force/pressure monitoring, temperature/heat monitoring, power monitoring, and acoustic emission were well reported in many applications of grinding [1,2,5,6]. Toshoff et al. (2002) reviewed the measurement approaches and sensors used to monitoring the micro- and macro-topography of the active layer of the grinding wheel [7]. K. Wegener et al. (2011) summarized the recent macro-wear and micro-wear monitoring technique for grinding wheels [2]. In many laboratory grinding tests, force mea-
http://dx.doi.org/10.1016/j.jmapro.2017.05.002 1526-6125/© 2017 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.
Y.B. Tian et al. / Journal of Manufacturing Processes 27 (2017) 188–197
surements are able to give valuable information on the grinding process. Grinding force monitoring and result analysis has been widely adopted in the academic grinding researches [1,2]. However, it has seldom performed on the shop floor owing to relatively high cost (relatively expensive for a dynamometer), cumbersome in real production operation, and change of machine setup and alteration of stiffness of the grinding system. Power monitoring, however, is an easy and convenient way to obtain useful information for the grinding process at relative low cost. In particular, there is no special design of complex fixturing and modification of machine tool. Power monitoring cells do not encroach inside the workspace of the machine tool and have no negative effects on the grinding process. A power cell is capable of measuring the spindle or axis driven motor power directly for alternating current (A.C.), direct current (D.C.) or variable frequency. The sensor is easy to connect to the electrical cabinet on both new and existing grinding machine. Utilization of three balanced Hall Effect sensors is able to eliminate large phase shift errors [1]. Effective power measurement has the advantage over simple current measurement as current is not a sensitive indicator of power at low loads in three-phase motors [3,4]. Power is linear relationship with motor load. A change in motor load reflects a change in motor power (horsepower or kilowatts). Current doesn’t vary significantly until the motor reaches 50% of capacity. At light loads, power (horsepower or kilowatts) is 10 times more sensitive than current (amps). It has been approved that small cutting tools can also be monitored using an additional logarithmic signal amplifier in machining process [1–4]. Therefore, on-line power monitoring has been assisted for the grinding analysis. Grinding power is even recognized as one of important grinding index, like grinding force and G-ratio, to characterize the grinding process and abrasive tool performance [2,7–9,11–16]. K. Subramanian et al. [12,13] proposed system approach in precision production grinding of ceramics and established six microscopic interaction models in the grinding contact zone. The six microscopic interactions in the grinding interface between abrasive tool and work material are able to be decomposed and linked with grinding power information. A.P. Walsh [14], W. Tian [15], and R. Vairamuthu [16] reported performance analysis of cylindrical grinding process through monitored power signals. In industrial aspect, several grinding-wheel manufacturers have developed their own power meters. For examples, Norton (now Saint-Gobain) owned Field Instrumentation System (F.I.S.). Slip Naxos/Winterthur developed NaxoTech. Camel/C.G.W. called Grinding Knowledge Systems (G.K.S). Nevertheless, grinding power monitoring is not fully explored. The current existing systems are still focused on measurement of grinding power and output of a plot of power versus process time from the data-logger. Grinding analysis method is only based on the comparison of the different grinding power pattern and magnitude under different abrasive tools and operating parameters. However, for complex grinding process, the current power monitoring work is actually limited. The further target is therefore motivated to extract the useful power (also grinding energy) signals recorded and to develop an analytical toolkit to comprehensively link the grinding inputs (machine tool, abrasive tool, workpiece, coolant, operating factors) and outputs (technical and economic outcome), as illustrated in Fig. 1. In this work, a portable power monitoring system with specially designed grinding analytical software was developed. The main software modules included signals acquisition, feature extraction and data calculation (grinding energy consumption and proportion included), and data analytical toolkit. Knowledge-based analytical tool was established through the correlation between grinding power/energy and grinding conditions. The actual application was further demonstrated with some case studies for grinding cycle reduction, energy consumption saving, grinding wheel assess-
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Fig. 1. Illustration of portable power monitoring system and analytical tool.
ment/selection, wheel (dressing/cleaning) condition monitoring, grinding quality improvement, and grinding burn avoidance. 2. Portable power monitoring system development 2.1. Hardware architecture A real-time portable power monitoring system was developed with integration of a portable power meter (PPC-3), a data acquisition system (A/D converter cDAQ 9171 and NI 9125) and a computing system with specially designed analytical software. The hardware architecture is illustrated in Fig. 2. It is typical sensorbased monitoring system for the grinding process. Fig. 3 shows the core hardware devices of the actual portable power monitoring system (Note that computing system and analytical tool/software are not shown). The power cell is able to measure the main spindle or axis drive power for single phase, three phases, DC, or variable frequency drive. The true power in kW or HP is given by measuring, rectifying and calculating from current with three clamp-on Hall-Effect transducers and voltage with three clip-on voltage sensors. The power cell possesses two isolated analog outputs i.e. 0–10 V DC minimum impedance 2,000 and 4–20 mA DC maximum impedance 500 . The measurement response time of the selected power sensor is 0.015–10 s. Frequency range is DC 0–1000 Hz. Power range is maximum 100 kW (or 130 HP) with six full scale ranges of 3 kW, 5 kW, 10 kW, 25 kW, 50 kW and 100 kW (or 4 HP, 10 HP, 20 HP, 50 HP, 100 HP, and 130 HP) to achieve maximum sensitivity. The case size of the power cell is 470 mm × 370 mm × 145 mm, which is light and portable. The power output of analog signals are discretized and acquired with a USB-based data acquisition system (DAQ). The voltage range is −10 V – +10 V. It has 4 channels with 16 bits resolutions and the sampling rate is 250 kS/s. 2.2. Software modules The power/energy monitoring software modules were specially designed and programmed with LabVIEW system design software (National Instruments). The designed functions consist of signals acquisition, feature extraction and data calculation, and data analytical toolkit. Fig. 4(a)–(c) shows the overall software interface for the three modules. After it was programmed and designed, the modules were built up and isolated to run all of the functions without the other commercial software. In the signal acquisition module, the real-time power data of main spindle motor were automatically collected (also be used for other axis drive motors). As shown in Fig. 4(a), power gears were specially designed for six full scale power ranges of 3 kW, 5 kW, 10 kW, 25 kW, 50 kW and 100 kW. This is used to adjust maximum sensitivity and obtain true power reading for the power cell. The response time was designed as seven options i.e. 0.015 s, 0.075 s, 0.15 s, 0.5 s, 1.0 s, 5.0 s and
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Fig. 2. Hardware architecture of monitoring system.
10.0 s. The power data storage files and file path were easily created and edited in the module. The corresponding channel of the data acquisition system (DAQ) connected is able to be rectified. Three control buttons, i.e. “Start”, “Stop” and “Pause”, were designed for the monitoring control. Real-time true power signals were monitored in a designed display window, which were also recorded in the storage file for further data extraction, calculation and analysis. Grinding peak power was plotted in a digital indicator, in which pre-warning peak power limit was manually set. Alert information (or alarm) will be generated when the grinding peak power exceeds the limit. This is able to prevent overload of the main spindle and detect collisions during grinding operations. Fig. 4(b) shows the module of feature extraction and data calculation. The stored raw power data can be opened and displayed in the formats of both measurement data file (.lvm) and binary file (.tdms). There is a function designed to merge different storage files with same format into one single file. This is convenient to realize the analysis and comparison of recorded data in one display window. Low pass filter was programmed with nineteen window options and selectable low cutoff frequency. The measured raw data of grinding power could be filtered and smoothened by operation of the low pass filter
buttons. The filtered data is also able to be converted into MS-Excel format file via the “Export” button. As shown in Fig. 4(b), the raw power data are plotted in the display window, which is able to be captured (zoon in or out) any interested feature of recorded power patterns. After the power feature with selectable error index (0–1, close to 1 resemble the defined feature) is defined, it will automatically identify the grinding power pattern. Manual identification of power pattern is also optional for some difficult-to-identify power patterns and analysis of short grinding cycles. After starting time, ending time, idling power and cutoff power are further set up in the display window, approaching time, approaching energy, actual grinding time, actual grinding energy, peak grinding power, average approaching power, average grinding power, and summary the proportion of grinding energy and approaching energy, will be automatically calculated and reported in this module. In order to further link the grinding inputs (machine tool, abrasive tool, workpiece, coolant, operating factors) and outputs (technical and economic output), the six microscopic interactions (which reflect overall grinding inputs/loads) in the grinding interface between abrasive tool and work material proposed by K. Subramanian et al.
Fig. 3. Portable power monitoring system.
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Fig. 4. Interface of software modules.
[12,13] are adopted in the grinding analytical toolkit development (see Fig. 4(c)). As explained in Fig. 5, the grinding power consumption (P(t)) versus material remove rate was decomposed and correlated with different grinding power i.e. initial threshold power (abrasive grain
sliding, plowing and bond/work sliding) (Pth (0)), effective cutting power (abrasive grain/work cutting) (Pc ), loading (chip/bond sliding, chip/work sliding) caused friction power Pf (t), glazing (abrasive grain plowing, sliding, and bond/work sliding) caused friction (Pth (t)) via curve fitting into a straight line at two individ-
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an important index to evaluate and compare the performance of the different grinding wheels. Three sub-modules i.e. “wheel comparison”, “wheel conditioning”, and “summary”, were developed. The sub-modules of “wheel comparison” and “wheel conditioning” have the functions of comparison of grinding power pattern, correlation graphs between average power and material removal rate, correlation graphs between specific grinding energy and material removal rate, grinding power/energy graphs with grinding conditions such as maximum undeformed chip thickness. In the sub-module of “summary”, the consumptions of time and energy under different grinding and approaching stages, and their proportions are clearly tabulated for process improvement and operation management.
3. Application in grinding process 3.1. Grinding strategy optimization
Fig. 5. Decomposition of grinding power consumption [12–15].
ual grinding cycles [12–15]. Grinding power consumption can be expressed accordingly as below. P(t) = Pth (0) + Pth (t) + Pc + Pf (t)
(1)
In the data analytical toolkit module in the designed software shown in Fig. 4(c), a build-in algorithm was developed to extract initial threshold power Pth (0), time dependent threshold power Pth (t), ideal cutting power Pc , time dependent friction power Pf (t) from the total measured power at a given material removal rate (Qw ). The slop rate of grinding power (power increase rate) against material removal rate can be also automatically deduced, which is
A serial of grinding experiments were conducted on a precision surface grinding machine (Okamoto ACC-63DXNC), in order to demonstrate the actual application of the developed portable power monitoring system in grinding process. Block workpiece made of typical hard-to-grind material, Ni718, was employed. The initial block dimension was 50 mm × 50 mm × 10 mm. An electroplated CBN wheel was used. The wheel diameter and width were 204 mm and 8 mm, respectively. Mesh size was 100/120#. Average abrasive grain diameter is equivalent to 149 m. Grinding speed was selected as 35 m/s (i.e. wheel ration speed was 3360 rpm). Feed rate was set as 200 mm/min. Total material removal was 30 m in workpiece height. In initial grinding scenario called Scenario #1, three stages i.e. rough grinding, semi-finishing, and final-finishing, were arranged. Total material removal required was 30 m in each grinding cycle. The depths of cut in the three stages were set as
Fig. 6. Power pattern under grinding scenario #1.
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Fig. 7. Power pattern under grinding scenario #2.
Table 1 Grinding parameters. Grinding parameters
Values
Wheel diameter (mm) Wheel width (mm) Wheel speed (m/s) Table speed (mm/min) Depth of cut (m) (Scenario #1) Depth of cut (m) (Scenario #2)
204 8 35 200 5, 10, 15 10, 20
15 m, 10 m, and 5 m, respectively. The grinding parameters are summarized in Table 1. Fig. 6 shows the power profile recorded versus process time in one grinding cycle. The power distributions of approaching, rough grinding, semi-finishing, and final-finishing were clearly exhibited. After the starting time, ending time, idling power and cutoff power were set up in the designed software, the power, energy consumptions and energy proportions in each stage were directly obtained, as summarized in Table 2. It is seen that the most energy (70.57%) was consumed by approaching while for actual material removal, the total of rough grinding, semi-finishing, and final finishing, occupied less than 30%. In order to reduce cycle time and save energy consumption, another grinding strategy (scenario #2) were proposed and adopted. As most of time and energy was consumed during the approaching stages, the total of approaching time was therefore reduced to 55.3 s with the consideration of the safety concerns (e.g. reasonable air-cut setting). Meanwhile, two stages i.e. rough grinding and final-finishing was reconfigured. The depths of cut of the rough grinding and final finishing were set to be 20 m and 10 m, respectively in the new grinding scenario #2. The requirement of total material removal of 30 m was still met. Fig. 7 exhibits the new power pattern recorded versus process time for the implementation of the new grinding strategy.
It can be seen that the total cycle time was reduced from initial 179.6 s to 87.5 s. The total cycle time was improved 51.28%. The absolute energy consumed by approaching decreased from 41.98 kJ to 17.7 kJ (energy saving is up to 57.84%). Total energy consumption (sum of approaching and grinding) decreased from intial 59.49 kJ to19.58 kJ. The total energy saving is up to 67.09% with the optimization of grinding strategy. The details of grinding power/energy consumption are shown in Table 3. Hence, with the assistance of the developed power monitoring system and analytic toolkits, it is confirmed that it is an effective tool to optimize grinding strategy, reduce grinding cycle time and save machining energy.
3.2. Operating parameters optimization Except for the output of a plot of power versus process time, it is convenient to link the operating parameters with the recorded grinding power and energy in the grinding analytic tool. Power is linear relationship with motor load. During grinding, external load can be characterized by specific material removal rate (related to feed rate, depth of cut and wheel width), maximum undeformed chip thickness (i.e. abrasive grain depth of cut, overall grinding parameter), and equivalent grinding thickness (related to feed rate, depth of cut and wheel speed). With the grinding outcome of power and energy under the grinding conditions (scenario #1) shown in Table 1, the correlation of the average power with specific material removal rate, maximum undeformed chip thickness and equivalent grinding thickness, and the correlation between specific grinding energy and specific material removal rate were plotted in Fig. 8(a)–(d). The effects of the important operating parameters such as feed rate, depth of cut and wheel speed could be directly linked with grinding power and energy. The prediction and analysis of grinding power and energy was easily realized for process improvement and energy-efficiency machining under the differ-
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Fig. 8. Average power and specific grinding energy versus grinding conditions.
Table 2 Grinding power/energy consumption under grinding scinario #1.
Time (s) Average power (kW) Energy consumption (kJ) Proportion of energy (%)
Approaching
Rough grinding
Semi-finishing
Final-finishing
Total
131.20 0.320 41.98 70.57
16.50 0.375 6.19 10.40
15.80 0.363 5.74 9.64
16.10 0.347 5.59 9.39
179.60 – 59.49 –
ent grinding conditions with assistance of the developed analytical tool.
oped power (energy) monitoring system and this model allows for the prediction of grinding regimes in which undesired burning can be avoided based on energy considerations. Furthermore, critical grinding conditions can be deduced to avoid grinding burns.
3.3. Avoidance of grinding burn In the previous publications reported by Malkin, Guo and Brinksmeier [5,10], the critical specific energy was defined as the onset of grinding burn, which followed a straight line if plotted versus deq 1/4 ae −3/4 vw −1/2 in the unit of mm−1 s1/2 , as shown in Fig. 9. deq is equivalent wheel diameter. ae is grinding depth of cut. vw is feed rate in surface grinding. The application of the devel-
3.4. Grinding wheel assessment In the data analytical toolkit module in the designed software, a build-in algorithm was developed to extract initial threshold power Pth (0), ideal cutting power Pc , and slop rate (power increasing rate) for the purpose of wheel comparison, as shown in Fig. 10. In order
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Fig. 9. Correlation between grinding energy and process conditions for the occurrence of grinding burn (according to publications by Malkin, Guo, Brinksmeier [5,10]).
Fig. 10. Comparasion of average grinding power and specific grinding energy for three different grinding wheels.
to demonstrate the function of wheel comparison, grinding experiments were conducted with three different wheels under same grinding parameters. The average grinding power for the three individual grinding wheels (Green, White and Red lines in Fig. 10) were compared against material removal rate. In grinding process, three microscopic interactions occur between abrasive tool/wheel and workpiece, i.e. sliding (rubbing), ploughing, and cutting. The initial threshold power Pth (0) indicated the initial sliding and ploughing power. The material removal by abrasive tool/wheel is needed to exceed the threshold power Pth (0). Less the initial threshold power Pth (0) is, easier the material removal is (or shaper the abrasive tool/wheel is). The initial threshold power Pth (0) of the wheel in red line is only 0.0063 kW which is 17% of the wheel in green line
which is indicated as 0.0369 kW, as shown in Fig. 10. Therefore, the cutting capability of wheel in red line (shaper, less energy consumption) is better than that of the wheel in green line at the beginning stage of the grinding process. The initial threshold power Pth (0) of the wheel in white line is the middle of red line and green line, so is the cutting capability at the beginning of grinding. Specific grinding energy, SGE (kJ/mm3 ), is energy consumption per unit of material removal. Large SGE indicates more energy consumption of material removal and worse performance of grinding wheel (like the grinding wheel shown in green line in Fig. 10). Grinding wheel with higher SGE usually results in grinding burn (or high grinding temperature) and small G-ratio. It is an indicator to distinguish wheel performance. In this case, the wheel in red line is the best
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Fig. 11. Comparasion of average grinding power under different wheel wearing stages.
performance, followed by white line while the wheel in green line performed the worst in the actual grinding. 3.5. Grinding wheel condition monitoring and analyzing For the purpose of comparison of wheel wearing and conditioning, a build-in algorithm was developed to extract time dependent threshold power Pth (t), ideal cutting power Pc , time dependent friction power Pf (t), and slop rate (power increasing rate) in the data analytical toolkit module in the designed software. As shown in Fig. 11, three different wearing (similar application in wheel conditioning using the analytical toolkit) stages of a same grinding wheel were compared which was plotted in Red, Green, and Purple lines. The time dependent threshold power Pth (t) is an indicator of grinding wheel condition (degree of sharpness). Pth (t) is glazing (abrasive grain plowing, sliding, and bond/work sliding) caused friction power. Large Pth (t) value indicated that wheel became dull and needed to be dressed. For example, the time dependent threshold power Pth (t) at the beginning is 0.0172 kW. After three days, the power increased 0.0065 kW. Another three days later, the value increased up to 0.0183 kW, which was 2.06 times of that at the beginning. The specific grinding energy was 2.06 times increased. With the prediction of grinding regimes of undesired burn (see Fig. 9), the increase of the threshold power Pth (t) is able to be used to determine the wheel dressing frequency to avoid grinding burn. The time dependent friction power, Pf (t), is caused by the friction of chip/bond sliding and chip/work sliding, which is an indicator of wheel loading level. The time dependent friction power, Pf (t), can be used to evaluate the cooling performance and coolant assessment. Table 3 Grinding power/energy consumption under grinding scinario #2.
3.6. Other applications Equipment health with different stiffness can be monitored and compared using power profiles. The power signal can be used to help identify a problem (such as truing problem) and troubleshooting. For example, when grinding wheel is needed to be trued to produce the required macroscopic wheel shape, the grinding power patterns will become strange and different from those recorded at normal stages. The power monitoring can help to find the source of grinding chatter, optimize dressing parameters, and evaluate grindability of different materials, cooling performance of different coolant and consistency of grinding wheel quality. 4. Summary This paper presented a real-time portable power monitoring system, which was integrated with a portable power meter, a data acquisition system and a computing system with specially designed analytical software. The main software modules is comprised of signals acquisition, feature extraction and data calculation (approaching and grinding energy consumption and their proportions included), and knowledge-based data analytical toolkit. The developed portable power monitoring system is easy and convenient to implement in production conditions. Grinding case studies were demonstrated the actual application with the developed portable power monitoring system for grinding cycle reduction, energy consumption saving, grinding wheel assessment/selection, wheel (dressing/cleaning) condition monitoring, grinding quality improvement, and grinding burn avoidance. In the future, the grinding analytical tool can be further upgraded to add the function of intelligent grinding expert database. With the new intelligent analytical tool, process window is able to be designed and optimized according to process requirements.
Approaching Rough grinding Final-finishing Total Time (s) Average power (kW) Energy consumption (kJ) Propotion of energy (%)
55.3 0.320 17.70 90.38
16.0 0.073 1.17 5.97
16.2 0.044 0.71 3.64
87.5 – 19.58 –
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