Optimization of milling process parameters for energy saving and surface roughness
Email:
trungthanhk21@mta.edu.vn
Từ khóa:
Milling, energy, surface roughness, parameter, desirability approach, optimization.
Tóm tắt
Improving the technical parameters of the machining process is an effective solution to save manufacturing costs. The purpose of this work is to decrease energy consumption (EC) and average surface roughness(ASR) for the milling process of AISI H13 steel. The spindle speed (S), depth of cut (a), and feed rate (f) were the processing inputs. The milling runs were performed using the experimental plan generated by the Box-Behnken method approach. The relationships between inputs and outputs were established using the response surface models (RSM). The desirability approach (DA) was used to observe the optimal values. The results showed that the reductions of EC and ASR are approximately 33.75% and 40.58%, respectively, as compared to the initial parameter setting. In addition, a hybrid approach using RSM and DA can be considered as a powerful solution to model the milling process and obtain a reliable optimal solution.Tài liệu tham khảo
[1] B.Bhardwaj, R.Kumar, P.K. Singh, Effect of machining parameters on surface roughness in end milling of AISI 1019 steel, Proc. Inst. Mech. Eng. B., 228 (2014) 704-714. https://doi.org/10.1177/0954405413506417
[2] B.Bhardwaj, R.Kumar, P.K. Singh, An improved surface roughness prediction model using Box-Cox transformation with RSM in end milling of EN 353, J. Mech. Sci. Technol., 28 (2014) 5149-5157. DOI: 10.1007/s12206-014-0837-4.
[3] M.R. Policena, C. Devitte, G. Fronza, R.F. Garcia, A.J. Souza, Surface roughness analysis in finishing end-milling of duplex stainless steel UNS S32205, Int. J. Adv. Manuf. Technol., 98 (2018) 1617-1625. https://doi.org/10.1007/s00170-018-2356-4
[4] F.Montevecchi, N.Grossi, H.Takagi, A.Scippa, H.Sasahara, G. Campatelli, Cutting Forces Analysis in Additive Manufactured AISI H13 Alloy, Procedia CIRP, 46 (2016) 476-479. https://doi.org/10.1016/j.procir.2016.04.034
[5] K. Gok, H. Sari, A. Gok, S. Neseli, E. Turkes, S. Yaldiz, Three-dimensional finite element modeling of effect on the cutting forces of rake angle and approach angle in milling, Proc. Inst. Mech. Eng. E., 231 (2017) 83-88. https://doi.org/10.1177/0954408915576698
[6] M.T.Prado, A.Pereira, J.A.Pérez, T.G.Mathia, Methodology for tool wear analysis by a simple procedure during milling of AISI H13 and its impact on surface morphology, Procedia Manuf., 13 (2017) 348- 355. https://doi.org/10.1016/j.promfg.2017.09.090
[7] M.T.Prado, A.Pereira, J.A.Pérez, T.G.Mathia, Methodology for tool wear analysis by electrical measuring during milling of AISI H13 and its impact on surface morphology, Procedia Manuf., 13 (2017) 356-363. https://doi.org/10.1016/j.promfg.2017.09.017
[8] N.S. Narayanan, N.Baskar, M.Ganesan, Multi Objective Optimization of machining parameters for Hard Turning OHNS/AISI H13 material Using Genetic Algorithm, Mater. Today Proc., 5 (2018) 6897-6905. https://doi.org/10.1016/j.matpr.2017.11.351.
[9] L. C. S. Rocha, A. P. de Paiva, P. R. Junior, P. P. Balestrassi, P. H. Campos, Robust multiple criteria decision making applied to optimization of AISI H13 hardened steel turning with PCBN wiper tool, Int. J. Adv. Manuf. Technol., 89 (2017) 2251-2268. DOI: 10.1007/s00170-016-9250-8.
[10] L. C. S. Rocha, A. P. de Paiva, P. R. Junior, P. P. Balestrassi, P. H. Campos, Robust weighting applied to optimization of AISI H13 hardened steel turning process with ceramic wiper tool: A diversity-based approach, Precis. Eng., 50 (2017) 235-247. https://doi.org/10.1016/j.precisioneng.2017.05.011
[11] Q. Zhang, S. Zhang, L. Jianfeng, Three Dimensional Finite Element Simulation of Cutting Forces and Cutting Temperature in Hard Milling of AISI H13 Steel, Procedia Manuf., 10 (2017) 37-47. https://doi.org/10.1016/j.promfg.2017.07.018
[12] E. Kuram, Nose radius and cutting speed effects during milling of AISI 304 material, Mater. Manuf. Process, 32 (2017) 185-192. https://doi.org/10.1080/10426914.2016.1198019
[13] H. S. Park, T.T.Nguyen, X.P. Dang, Multi-objective optimization of turning process of hardened material for energy efficiency, Int. J. Pr. Eng. Man., 17 (2016) 1623-1631. DOI: 10.1007/s12541-016-0188-4.
[14] A. Mehrvar, A. Basti, A. Jamali, Optimization of electrochemical machining process parameters: Combining response surface methodology and differential evolution algorithm, Proc. Inst. Mech. Eng. E., 231 (2017) 1114-1126. https://doi.org/10.1177/0954408916656387
[15] M. M. Liman, K. Hossein, Modeling and multi response optimization of cutting parameters in SPDT of a rigid contact lens polymer using RSM and desirability function, Int. J. Adv. Manuf. Technol., 102 (2019) 1443-1465. https://doi.org/10.1007/s00170-018-3169-1
[16] R. Kumar, P. S. Bilga, S. Singh, Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation, J. Clean. Prod., 164 (2017) 45-57. https://doi.org/10.1016/j.jclepro.2017.06.077.
[2] B.Bhardwaj, R.Kumar, P.K. Singh, An improved surface roughness prediction model using Box-Cox transformation with RSM in end milling of EN 353, J. Mech. Sci. Technol., 28 (2014) 5149-5157. DOI: 10.1007/s12206-014-0837-4.
[3] M.R. Policena, C. Devitte, G. Fronza, R.F. Garcia, A.J. Souza, Surface roughness analysis in finishing end-milling of duplex stainless steel UNS S32205, Int. J. Adv. Manuf. Technol., 98 (2018) 1617-1625. https://doi.org/10.1007/s00170-018-2356-4
[4] F.Montevecchi, N.Grossi, H.Takagi, A.Scippa, H.Sasahara, G. Campatelli, Cutting Forces Analysis in Additive Manufactured AISI H13 Alloy, Procedia CIRP, 46 (2016) 476-479. https://doi.org/10.1016/j.procir.2016.04.034
[5] K. Gok, H. Sari, A. Gok, S. Neseli, E. Turkes, S. Yaldiz, Three-dimensional finite element modeling of effect on the cutting forces of rake angle and approach angle in milling, Proc. Inst. Mech. Eng. E., 231 (2017) 83-88. https://doi.org/10.1177/0954408915576698
[6] M.T.Prado, A.Pereira, J.A.Pérez, T.G.Mathia, Methodology for tool wear analysis by a simple procedure during milling of AISI H13 and its impact on surface morphology, Procedia Manuf., 13 (2017) 348- 355. https://doi.org/10.1016/j.promfg.2017.09.090
[7] M.T.Prado, A.Pereira, J.A.Pérez, T.G.Mathia, Methodology for tool wear analysis by electrical measuring during milling of AISI H13 and its impact on surface morphology, Procedia Manuf., 13 (2017) 356-363. https://doi.org/10.1016/j.promfg.2017.09.017
[8] N.S. Narayanan, N.Baskar, M.Ganesan, Multi Objective Optimization of machining parameters for Hard Turning OHNS/AISI H13 material Using Genetic Algorithm, Mater. Today Proc., 5 (2018) 6897-6905. https://doi.org/10.1016/j.matpr.2017.11.351.
[9] L. C. S. Rocha, A. P. de Paiva, P. R. Junior, P. P. Balestrassi, P. H. Campos, Robust multiple criteria decision making applied to optimization of AISI H13 hardened steel turning with PCBN wiper tool, Int. J. Adv. Manuf. Technol., 89 (2017) 2251-2268. DOI: 10.1007/s00170-016-9250-8.
[10] L. C. S. Rocha, A. P. de Paiva, P. R. Junior, P. P. Balestrassi, P. H. Campos, Robust weighting applied to optimization of AISI H13 hardened steel turning process with ceramic wiper tool: A diversity-based approach, Precis. Eng., 50 (2017) 235-247. https://doi.org/10.1016/j.precisioneng.2017.05.011
[11] Q. Zhang, S. Zhang, L. Jianfeng, Three Dimensional Finite Element Simulation of Cutting Forces and Cutting Temperature in Hard Milling of AISI H13 Steel, Procedia Manuf., 10 (2017) 37-47. https://doi.org/10.1016/j.promfg.2017.07.018
[12] E. Kuram, Nose radius and cutting speed effects during milling of AISI 304 material, Mater. Manuf. Process, 32 (2017) 185-192. https://doi.org/10.1080/10426914.2016.1198019
[13] H. S. Park, T.T.Nguyen, X.P. Dang, Multi-objective optimization of turning process of hardened material for energy efficiency, Int. J. Pr. Eng. Man., 17 (2016) 1623-1631. DOI: 10.1007/s12541-016-0188-4.
[14] A. Mehrvar, A. Basti, A. Jamali, Optimization of electrochemical machining process parameters: Combining response surface methodology and differential evolution algorithm, Proc. Inst. Mech. Eng. E., 231 (2017) 1114-1126. https://doi.org/10.1177/0954408916656387
[15] M. M. Liman, K. Hossein, Modeling and multi response optimization of cutting parameters in SPDT of a rigid contact lens polymer using RSM and desirability function, Int. J. Adv. Manuf. Technol., 102 (2019) 1443-1465. https://doi.org/10.1007/s00170-018-3169-1
[16] R. Kumar, P. S. Bilga, S. Singh, Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation, J. Clean. Prod., 164 (2017) 45-57. https://doi.org/10.1016/j.jclepro.2017.06.077.
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
12/07/2019
Nhận bài sửa
14/08/2019
Chấp nhận đăng
26/08/2019
Xuất bản
15/11/2019
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Quoc-Hoang, P., Xuan-Phuong, D., Tat-Khoa, D., Xuan-Hung, L., Lan-Huong Luong, T., & Trung-Thanh, N. (1573750800). Optimization of milling process parameters for energy saving and surface roughness. Tạp Chí Khoa Học Giao Thông Vận Tải, 70(3), 173-183. https://doi.org/10.25073/tcsj.70.3.25
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