Energy Consumption Prediction Model of SiCp/Al Composite in Grinding Based on PSO-BP Neural Network

Abstract:

As the typical particle-reinforced aluminum matrix composite, SiCp/Al composite has low density, high elastic modulus and high thermal conductivity, and is one of the most competitive metal matrix composites. Grinding is the main processing technique of SiCp/Al composite, energy consumption of the grinding process provides guidance for the energy saving, which is the aim of green manufacturing. In this paper, grinding experiments were designed and conducted to obtain the energy consumption of the grinding machine tool. The Particle Swarm Optimization (PSO) BP neural network prediction model was applied in the energy consumption prediction model of SiCp/Al composite in grinding. It showed that the Particle Swarm Optimization (PSO) BP neural network prediction model has high prediction accuracy. The prediction model of energy consumption based on PSO-BP neural network is helpful in energy saving, which contributes to greening manufacturing.

• Periodical: Solid State Phenomena (Volume 305)
• Pages: 163-168
• DOI: https://doi.org/10.4028/www.scientific.net/SSP.305.163
• Online since: June 2020
• Authors: Peng Gu*, Chuan Min Zhu, Yin Yue Wu, Andrea Mura
• Keywords: Energy Consumption, Particle Swarm Optimization, PSO-BP Neural Network, SiCp/Al Composite

References

[1] Pramanik A. Zhang L. Arsecularatne J. Deformation mechanism of MMCs under indention[J]. Composites Science and technology, 2008, 68(6): 1304-1312.

DOI: 10.1016/j.compscitech.2007.12.008

[2] Liang G, Zhou X, Zhao F. The grinding surface characteristics and evaluation of particle-reinforced aluminum silicon carbide[J]. Science and Engineering of Composite Materials, 2016, 23(6): 671-676.

DOI: 10.1515/secm-2014-0377

[3] Zhu C, Gu P, Wu Y, Liu D, Wang X. Surface roughness prediction model of SiCp/Al composite in grinding[J]. International Journal of mechanical Sciences, 2019(155):98-109.

DOI: 10.1016/j.ijmecsci.2019.02.025

[4] Yang Q, Xu Y. Study on springback prediction based on improved PSO⁃BP neural network[J]. Modern Electronics Technique, 2019(42):161-165. (in Chinese).

[5] Lu D. Research on Prediction Method of Profile Grinding Surface Roughness Based on PSO-LS SVM Algorithm[J]. Advanced Materials Research, 2014, 926-930:3684-3687.

DOI: 10.4028/www.scientific.net/amr.926-930.3684

[6] Yin Y, Ding W. A Novel Cloud Model Prediction for Surface Roughness Based on Multidimensional & Multi-rules Reasoning[J]. Journal of Mechanical Engineering, 2016, 52(15):204-212(in Chinese).

DOI: 10.3901/jme.2016.15.204

[7] Kumar S, Batish A, Singh R, et al. A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys[J]. Journal of Mechanical Science & Technology, 2014, 28(7):2831-2844.

DOI: 10.1007/s12206-014-0637-x

[8] Deris, Ashanira Mat, Zain, et al. Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining[J]. Meccanica, 2013, 48(8):1937-1945.

DOI: 10.1007/s11012-013-9710-2

[9] Sahoo P, Pratap A, Bandyopadhyay A. Modeling and optimization of surface roughness and tool vibration in CNC turning of Aluminum alloy using hybrid RSM-WPCA methodology[J]. International Journal of Industrial Engineering Computations, 2017, 8(3): 385-398.

DOI: 10.5267/j.ijiec.2016.11.003