Response surface methodology approach in achieving multi-response setup optimization in the machining process
DOI:
https://doi.org/10.56294/saludcyt2022190Keywords:
Machining Parameter, Multi Response Optimisation, Tool Wear, Dimension DeviationAbstract
A machining cost is constructed on many factors. All aspects potentially raise the additional charges resulting from not achieving dimension due to tool wear level. The accuracy of parameters determines the effectiveness of the machining process. However, these parameters are sensitive, so the different machines may not provide the same performance. The specific machining parameters become less suitable for others. This experimental approach is proposed to obtain the parameter used on other machines without reducing the performance. This multi-response study used a response surface methodology by selecting the material removal area, feed rate, spindle speed, and the number of repetitions as input have a dominant influence on the tool wear and the dimension deviation. A comprehensive range with the specified target is obtained by applying different weights. Testing on 11 units of machines from 3 other countries provides the same performance and contributes to saving 15 % of machining time
References
1. Schoenfeldt TI. A Practical Application of Supply Chain Management Principles. ASQ Quality Press; 2008.
2. Manalo RG, Manalo MV. Quality, Cost and Delivery performance indicators and Activity-Based Costing. In: 2010 IEEE International Conference on Management of Innovation & Technology [Internet]. Singapore, Singapore: IEEE; 2010 [cited 2022 Aug 29]. p. 869–74. Available from: http://ieeexplore.ieee.org/document/5492805/
3. Oprea R. Survival Versus Profit Maximization in a Dynamic Stochastic Experiment. Econometrica. 2014;82(6):2225–55.
4. Wojcicki J. Energy Efficiency of Machine Tools [Doctoral Thesis]. Politecnico di Milano; 2017.
5. Pavanaskar S. Improving Energy Efficiency in CNC Machining [Internet] [Doctoral Thesis]. University of California; 2014. Available from: https://books.google.co.id/books?id=VDArngAACAAJ
6. Wang SM, Lee CY, Gunawan H, Yeh CC. An Accuracy-Efficiency-Power Consumption Hybrid Optimization Method for CNC Milling Process. Applied Sciences. 2019 Apr 10;9(7):1495.
7. Singh G, Aggarwal V, Singh S. Critical review on ecological, economical and technological aspects of minimum quantity lubrication towards sustainable machining. Journal of Cleaner Production. 2020 Oct;271:122185.
8. Ming W, Shen F, Zhang G, Liu G, Du J, Chen Z. Green machining: A framework for optimization of cutting parameters to minimize energy consumption and exhaust emissions during electrical discharge machining of Al 6061 and SKD 11. Journal of Cleaner Production. 2021 Feb;285:124889.
9. Myers RH, Montgomery DC, Cook CMA. Response Surface Methodology: Process and Product Optimization using Designed Experiments. 3rd ed. Vol. 39. New Jersey: JOhn Wiley & Sons; 2009.
10. Zahraee SM, Rohani JM, Wong KY. Application of computer simulation experiment and response surface methodology for productivity improvement in a continuous production line: Case study. Journal of King Saud University - Engineering Sciences. 2018 Jul;30(3):207–17.
11. Dengiz B, Belgin O. Simulation optimization of a multi-stage multi-product paint shop line with Response Surface Methodology. SIMULATION. 2014 Mar;90(3):265–74.
12. Bhushan RK. Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production. 2013 Jan;39:242–54.
13. Camposeco-Negrete C. Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. Journal of Cleaner Production. 2015 Mar;91:109–17.
14. Lv J, Tang R, Jia S, Liu Y. Experimental study on energy consumption of computer numerical control machine tools. Journal of Cleaner Production. 2016 Jan;112:3864–74.
15. Zhong Q, Tang R, Lv J, Jia S, Jin M. Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process. Int J Adv Manuf Technol. 2016 Feb;82(9–12):2087–99.
16. Wibowo YT, Baskoro SY, Manurung VAT. Toolpath Strategy and Optimum Combination of Machining Parameter during Pocket Mill Process of Plastic Mold Steels Material. In: Materials Science and Engineering Conference Series. 2018. p. 012137. (Materials Science and Engineering Conference Series; vol. 306).
17. Hu L. Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed. Energy. 2017;12.
18. Li, Lingling, Li C, Tang Y, Li L. An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining. Journal of Cleaner Production. 2017 Sep;162:458–73.
19. Wibowo YT, Manurung V, Kosasih K. Experimental Study on Plastic Mold Steel Reaming Process using Taguchi Method. In: 2018 International Conference on Applied Science and Technology (iCAST). 2018. p. 559–62.
20. Lee Y, Resiga A, Yi S, Wern C. The Optimization of Machining Parameters for Milling Operations by Using the Nelder–Mead Simplex Method. JMMP. 2020 Jul 5;4(3):66.
21. Zhang X, Yu T, Dai Y, Qu S, Zhao J. Energy consumption considering tool wear and optimization of cutting parameters in micro milling process. International Journal of Mechanical Sciences. 2020 Jul;178:105628.
22. Hu L, Tang R, Liu Y, Yanlong Cao, Tiwari A. Optimising the machining time, deviation and energy consumption through a multi-objective feature sequencing approach. Energy Conversion and Management. 2018;15.
23. Jiang Z, Gao D, Lu Y, Kong L, Shang Z. Electrical energy consumption of CNC machine tools based on empirical modeling. The International Journal of Advanced Manufacturing Technology [Internet]. 2018; Available from: https://doi.org/10.1007/s00170-018-2808-x
24. Shi KN. A Novel Energy Consumption Model for Milling Process Considering Tool Wear Progression. Journal of Cleaner Production. 2018;179:22.
25. Lv J, Tang R, Tang W, Liu Y, Zhang Y, Jia S. An investigation into reducing the spindle acceleration energy consumption of machine tools. Journal of Cleaner Production. 2016;1–10.
26. Öztürk B, Uğur L, Yildiz A. Investigation of effect on energy consumption of surface roughness in X-axis and spindle servo motors in slot milling operation. Measurement. 2019 Jun;139:92–102.
27. Hu L, Tang R, Cai W, Feng Y, Ma X. Optimisation of cutting parameters for improving energy efficiency in machining process. Robotics and Computer-Integrated Manufacturing. 2019 Oct;59:406–16.
28. Hassan M, Sadek A, Attia MH. Novel sensor-based tool wear monitoring approach for seamless implementation in high speed milling applications. CIRP Annals. 2021;70(1):87–90.
29. Shankar S, Mohanraj T, Rajasekar R. Prediction of cutting tool wear during milling process using artificial intelligence techniques. International Journal of Computer Integrated Manufacturing. 2018 Nov 26;
30. Zhao G, Li C, Lv Z, Cheng X, Zheng G. Specific energy consumption prediction model of CNC machine tools based on tool wear. International Journal of Computer Integrated Manufacturing. 2020 Feb 1;33(2):159–68.
31. Akçay H, Anagün AS. Multi Response Optimization Application on a Manufacturing Factory. MCA. 2013 Dec 1;18(3):531–8.
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Copyright (c) 2022 Yohanes T. Wibowo, Nurhadi Siswanto, Mokh Suef (Author)
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