Abstract
In the past, milling operations have been mainly considered from the economic and technological perspectives, while the environmental consideration has been becoming highly imperative nowadays. In this study, a systemic optimization approach is presented to identify the Pareto-optimal values of some key process parameters for low-carbon milling operation. The approach consists of the following stages. Firstly, regression models are established to characterize the relationship between milling parameters and several important performance indicators, i.e., material removal rate, carbon emission, and surface roughness. Then, a multi-objective optimization model is further constructed for identifying the optimal process parameters, and a hybrid NSGA-II algorithm is proposed to obtain the Pareto frontier of the non-dominated solutions. Based on the Taguchi design method, dry milling experiments on aluminum are performed to verify the proposed regression and optimization models. The experimental results show that a higher spindle speed and feed rate are more advantageous for achieving the performance indicators, and the depth of cut is the most critical process parameter because the increase of the depth of cut results in the decrease of the specific carbon emission but the increase of the material removal rate and surface roughness. Finally, based on the regression models and the optimization approach, an online platform is developed to obtain in-process information of energy consumption and carbon emission for real-time decision making, and a simulation case is conducted in three different scenarios to verify the proposed approach.
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EIA U., Annual Energy Review. (2011). U.S. Energy Information Admin: Washington DC, USA, pp. 3–4.
Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., et al. (2012). Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Annals—Manufacturing Technology, 61(2), 587–609.
Bruzzone, A., Anghinolfi, D., Paolucci, M., & Tonelli, F. (2012). Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops. CIRP Annals—Manufacturing Technology, 61(1), 459–462.
Newman, S. T., Nassehi, A., Imani-Asrai, R., & Dhokia, V. (2012). Energy efficient process planning for CNC machining. CIRP Journal of Manufacturing Science and Technology, 5(2), 127–136.
Gopalsamy, B. M., Mondal, B., & Ghosh, S. (2009). Taguchi method and ANOVA: An approach for process parameters optimization of hard machining while machining hardened steel. Journal of Scientific & Industrial Research, 68(8), 686–695.
Somashekhar, K. P., Mathew, J., & Ramachandran, N. (2011). Multi-objective optimization of micro wire electric discharge machining parameters using grey relational analysis with Taguchi method. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 225(7), 1742–1753.
Campanelli, S. L., Casalino, G., & Contuzzi, N. (2013). Multi-objective optimization of laser milling of 5754 aluminum alloy. Optics & Laser Technology, 52(1), 48–56.
Yan, J., & Li, L. (2013). Multi-objective optimization of milling parameters—the trade-offs between energy, production rate and cutting quality. Journal of Cleaner Production, 52(1), 462–471.
Vijayaraghavan, A., & Dornfeld, D. (2010). Automated energy monitoring of machine tools. CIRP Annals—Manufacturing Technology, 59(1), 21–24.
Behrendt, T., Zein, A., & Min, S. (2012). Development of an energy consumption monitoring procedure for machine tools. CIRP Annals—Manufacturing Technology, 61(1), 43–46.
Hu, S., Liu, F., He, Y., & Hu, T. (2012). An on-line approach for energy efficiency monitoring of machine tools. Journal of Cleaner Production, 27(1), 133–140.
Kara, S., & Li, W. (2011). Unit process energy consumption models for material removal processes. CIRP Annals—Manufacturing Technology, 60(1), 37–40.
Mori, M., Fujishima, M., Inamasu, Y., & Oda, Y. (2011). A study on energy efficiency improvement for machine tools. CIRP Annals—Manufacturing Technology, 60(1), 145–148.
Arif, M., Stroud, I. A., & Akten, O. (2014). A model to determine the optimal parameters for sustainable-energy machining in a multi-pass turning operation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 228(6), 866–877.
Kant, G., & Sangwan, K. S. (2014). Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production, 83, 151–164.
Choudhury, S. K., & Appa, Rao I. (1999). Optimization of cutting parameters for maximizing tool life. International Journal of Machine Tools and Manufacture, 39(2), 343–353.
Lalwani, D. I., Mehta, N. K., & Jain, P. K. (2008). Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. Journal of Materials Processing Technology, 206(1), 167–179.
Zain, A. M., Haron, H., & Sharif, S. (2010). Prediction of surface roughness in the end milling machining using artificial neural network. Expert Systems with Applications, 37(2), 1755–1768.
Campatelli, G., Lorenzini, L., & Scippa, A. (2013). Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. Journal of Cleaner Production, 66(1), 309–316.
Camposeco-Negrete, C. (2013). Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. Journal of Cleaner Production, 53(1), 195–203.
Bhattacharya, A., Das, S., Majumder, P., & Batish, A. (2009). Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Production Engineering, 3(1), 31–40.
Winter, M., Li, W., Kara, S., & Herrmann, C. (2014). Determining optimal process parameters to increase the eco-efficiency of grinding processes. Journal of Cleaner Production, 66(1), 644–654.
Bhushan, R. K. (2013). Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production, 39(1), 242–254.
Rajemi, M. F., Mativenga, P. T., & Aramcharoen, A. (2010). Sustainable machining: Selection of optimum turning conditions based on minimum energy considerations. Journal of Cleaner Production, 18(10–11), 1059–1065.
Narita, H., Desmira, N., Fujimoto, H., Environmental burden analysis for machining operation using LCA method. In Manufacturing Systems and Technologies for the New Frontier, 2008, Springer, pp. 65–68.
Nalbant, M., Gökkaya, H., & Sur, G. (2007). Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Materials and Design, 28(4), 1379–1385.
Wibowo, A., & Desa, M. I. (2012). Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process. Expert Systems with Applications, 39(14), 11634–11641.
Quiza, Sardiñas R., Rivas, Santana M., & Alfonso, Brindis E. (2006). Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence, 19(2), 127–133.
Pawade, R. S., & Joshi, S. S. (2011). Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA). The International Journal of Advanced Manufacturing Technology., 56(1–4), 47–62.
Kuram, E., Ozcelik, B., Bayramoglu, M., Demirbas, E., & Simsek, B. T. (2013). Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. Journal of Cleaner Production, 42(1), 159–166.
Palanikumar, K., Latha, B., Senthilkumar, V. S., & Karthikeyan, R. (2009). Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II). Metals and Materials International, 15(2), 249–258.
China D.O.M.I. (1997). Mechanical engineering handbook: Machine building technology and equipment (II). 1997. Beijing, China, China Machine Press.
Fang, X. D., & Safi-Jahanshahi, H. (1997). A new algorithm for developing a reference-based model for predicting surface roughness in finish machining of steels. International Journal of Production Research, 35(1), 179–199.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Davoodi, B., & Tazehkandi, A. H. (2013). Experimental investigation and optimization of cutting parameters in dry and wet machining of aluminum alloy 5083 in order to remove cutting fluid. Journal of Cleaner Production, 68(1), 234–242.
Yang, L., Deuse, J., & Jiang, P. (2013). Multi-objective optimization of facility planning for energy intensive companies. Journal of Intelligent Manufacturing, 24(6), 1095–1109.
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Zhang, C.Y., Li, W.D., Jiang, P.Y., Gu, P.H. (2019). Experimental Investigation and Multi-objective Optimization Approach for Low-Carbon Milling Operation of Aluminum. In: Li, W., Wang, S. (eds) Sustainable Manufacturing and Remanufacturing Management. Springer, Cham. https://doi.org/10.1007/978-3-319-73488-0_5
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DOI: https://doi.org/10.1007/978-3-319-73488-0_5
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