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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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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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