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Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network

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Abstract

Determination of optimal cutting parameters is one of the most essential tasks in process planning of sculptured parts to reduce machining cost and increase surface quality. This paper presents a multi-objective optimization approach, based on neural network, to optimize the cutting parameters in sculptured parts machining. An optimization mathematical model is first presented with spindle speed, feed rate, depth of cut and path spacing as the process parameters and machining time, energy consumption and surface roughness as objectives. Then a Back propagation neural network (BPNN) model is developed to predict cutting parameter, and experiments are designed to train and test the validation of developed BPNN model. Finally, an application case is given and its results demonstrate the ability of our method through comparing with the traditional approach.

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References

  • Ahilan, C., Kumanan, S., Sivakumaran, N., & Dhas Edwin Raja, J. (2013). Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Applied Soft Computing, 13(3), 1543–1551.

    Article  Google Scholar 

  • Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23(4), 1001–1014.

    Article  Google Scholar 

  • Amiolemhen, E., & Ibhadode, A. O. A. (2004). Application of genetic algorithms-determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile. International Journal of Machine Tools and Manufacture, 44(12–13), 1403–1412.

    Article  Google Scholar 

  • Burke, L., & Ignizio, J. P. (1997). A practical overview of neural networks. Journal of Intelligent Manufacturing, 8(3), 157–165.

    Article  Google Scholar 

  • Chien, W. T., & Tsai, C. S. (2003). The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17–4PH stainless steel. Journal of Material Processing Technology, 140(1–3), 340–345.

    Article  Google Scholar 

  • Chinchanikar, S., & Choudhury, S. K. (2013). Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: An optimization approach. Measurement, 46(4), 1572–1584.

    Article  Google Scholar 

  • Chua, M. S., Loh, H. T., Wong, Y. S., & Rahman, M. (1991). Optimization of cutting conditions for multi-pass turning operations using sequential quadratic programming. Journal of Materials Processing Technology, 28(1–2), 253–262.

    Article  Google Scholar 

  • Çiçek, A., Kıvak, T., & Ekici, E. (2013). Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills. Journal of Intelligent Manufacturing, (In press).

  • Cus, F., & Balic, J. (2003). Optimization of cutting process by GA approach. Robotics and Computer Integrated Manufacturing, 19(1–2), 113–121.

    Article  Google Scholar 

  • Farahnakian, M., Razfar, M. R., Moghri, M., & Asadnia, M. (2011). The selection of milling parameters by the PSO-based neural network modeling method. International of Advanced Manufacturing Technology, 57(1–4), 49–60.

    Article  Google Scholar 

  • Huang, J., Süer, G. A., & Urs, S. B. R. (2012). Genetic algorithm for rotary machine scheduling with dependent processing times. Journal of Intelligent Manufacturing, 23(5), 1931–1948.

    Article  Google Scholar 

  • Joo, J., Yi, G.-R., Cho, H., & Choi, Y.-S. (2001). Dynamic planning model for determining cutting parameters using neural networks in feature-based process planning. Journal of Intelligent Manufacturing, 12(1), 13–29.

    Article  Google Scholar 

  • Krimpenis, A., & Vosniakos, G.-C. (2009). Rough milling optimization for parts with sculptured surfaces using genetic algorithms in a Stackelberg game. Journal of Intelligent Manufacturing, 20(4), 447–461.

    Article  Google Scholar 

  • Lee, B. Y., & Tarng, Y. S. (2000). Cutting-parameter selection for maximizing production rate or minimizing production cost in multistage turning operations. Journal of Materials Processing Technology, 105(1–2), 61–66.

    Article  Google Scholar 

  • Li, L., Liu, F., Li, C. B., & Chen, G. A. (2011). Realistic wrinkle generation for 3D face modeling based on automatically extracted curves and improved shape control functions. Computers & Graphics-UK, 35(1), 175–184.

    Article  Google Scholar 

  • Li, L., Liu, F., Peng, C. H., & Gao, B. L. (2013). Computer-aided 3D human modeling for portrait-based product development using point- and curve-based deformation. Computer-Aided Design, 45(2), 134–143.

    Article  Google Scholar 

  • Makadia, A. J., & Nanavati, J. I. (2013). Optimisation of machining parameters for turning operations based on response surface methodology. Measurement, 46(4), 1521–1529.

    Article  Google Scholar 

  • Malakooti, B., & Raman, V. (2000). An interactive multi-objective artificial neural network approach for machine setup optimization. Journal of Intelligent Manufacturing, 11(1), 41–50.

    Google Scholar 

  • Manav, C., Bank, H. S., & Lazoglu, I. (2013). Intelligent toolpath selection via multi-criteria optimization in complex sculptured surface milling. Journal of Intelligent Manufacturing, 24(2), 349–355.

    Article  Google Scholar 

  • Molinari, A., & Nouari, M. (2002). Modeling of tool wear by diffusion in metal cutting. Wear, 252(1–2), 135–149.

    Article  Google Scholar 

  • Rituparna D., & Anima M. (2010). Optimization of turning process parameters using multi-objective evolutionary algorithm. In 2010 IEEE congress on evolutionary computation. Barcelona, Spain, 18–23 Jul, 2010.

  • Robert, H.-N. (1988). Theory of the backpropagation neural network. Neural Networks, 1(1), 593–605.

    Google Scholar 

  • Sarac, T., & Ozcelik, F. (2012). A genetic algorithm with proper parameters for manufacturing cell formation problems. Journal of Intelligent Manufacturing, 23(4), 1047–1061.

    Article  Google Scholar 

  • Saravanan, R., Asokan, P., & Vijayakumar, K. (2003). Machining parameters optimization for turning cylindrical stock into a continuous finished profile using genetic algorithm(GA) and simulated annealing(SA). International Journal of Advanced Manufacturing Technology, 21(1), 1–9.

    Article  Google Scholar 

  • Sardinas, R. Q., Santana, M. R., & Brindis, E. A. (2006). Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence, 19(2), 127–133.

    Google Scholar 

  • Sardinas, R. Q., Santana, M. R., & Brindis, E. A. (2006). Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence, 19(2), 127–133.

    Google Scholar 

  • Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. K. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4), 473–483.

    Article  Google Scholar 

  • Soodamani, R., & Liu, Z. Q. (2000). GA-based learning for a model-based object recognition system. International Journal of Approximate Reasoning, 23(2), 85–109.

    Google Scholar 

  • Wang, W., & Zhang, Y. M. (2009). Prediction of cutting parameters based on improved neural networks. In Proceedings of 9th international conference on electronic measurement and instruments, (pp. 3126–3130). Beijing China, 16–19 August 2009.

  • Yildiz, A. R. (2013a). Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Informaiton Sciences, 220, 399–407.

    Google Scholar 

  • Yildiz, A. R. (2013b). A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing, 13(3), 1561–1566.

    Google Scholar 

  • Zuperl, U., & Cus, F. (2003). Optimization of cutting conditions during cutting by using neural networks. Robotics and Computer Integrated Manufacturing, 19(1–2), 189–199.

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially funded by China Postdoctoral Science Foundation (No. 2012M521671), the Chongqing Postdoctoral Science Foundation Funded project (No. XM2012007), and the Fundamental Research Funds for the Central Universities (CDJZR12110076). We are grateful to other people in our team for collaboration and discussion.

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Authors and Affiliations

  1. College of Engineering and Technology, Southwest University, Chongqing, 400715, People’s Republic of China

    Li Li, Fei Liu & Cong Bo Li

  2. Post-doctoral Study Center of Management Science and Engineering, State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400030, People’s Republic of China

    Li Li

  3. Dongfang Electric Machinery Co., Ltd., Deyang, 618000, People’s Republic of China

    Bing Chen

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  1. Li Li
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  2. Fei Liu
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  4. Cong Bo Li
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Correspondence to Li Li.

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Li, L., Liu, F., Chen, B. et al. Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network. J Intell Manuf 26, 891–898 (2015). https://doi.org/10.1007/s10845-013-0809-z

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  • DOI: https://doi.org/10.1007/s10845-013-0809-z

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