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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.
목차
ABSTRACT
1. Introduction
2. Methodology
3. Training Neural Network with Back Propagation
4. Training Neural Network with Particle Swarm Optimization
5. Neural network validation
6. Conclusion
References
참고문헌 (15)
참고문헌 신청
Ahilan / 2013 / Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools / Applied Soft Computing 13:1543~1551
Asiltürk, I. / 2012 / Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression / Int. J. Adv. Manuf. Techno
Benardos, P.G / 2003 / Predicting surface roughness in machining: a review / International Journal of Machine Tools & Manufacture 43:833~844
Bhattacharya / 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 / Prod. Eng. Res. Devel 3:1~40
Che, Z.H / 2010 / PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding / Computers & Industrial Engineering 58:625~637
Asiltürk, I. / 2012 / Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression / Int. J. Adv. Manuf. Techno
Benardos, P.G / 2003 / Predicting surface roughness in machining: a review / International Journal of Machine Tools & Manufacture 43:833~844
Bhattacharya / 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 / Prod. Eng. Res. Devel 3:1~40
Che, Z.H / 2010 / PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding / Computers & Industrial Engineering 58:625~637
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UCI(KEPA) : I410-ECN-0101-2016-581-002077835