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Information Journal Paper

Title

COMPARISON OF ARTIFICIAL NEURAL NETWORK METHODS FOR MODELING OF TURNING OF POLYMER MATRIX COMPOSITE

Pages

  83-98

Abstract

 In this research, POLYMER MATRIX COMPOSITE filled with aluminum particles was synthesized and turned with different machining condition namely: cutting speed, weight fraction of particle, depth of cut and feed. Then, SURFACE ROUGHNESS was measured and two artificial neural networks models Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were developed to estimate effects of four TURNING parameters on SURFACE ROUGHNESS. Correlation between training data and experimental data were shown that MLP network was better than RBF as a compatible network (correlations were 0.835 for MLP network and 0.542 for RBF network). Because of higher correlation for MLP network, this network was proposed as a model for investigation the effects of TURNING parameters on SURFACE ROUGHNESS.

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    APA: Copy

    DASHTBAYAZI, MOHAMMAD REZA, & GHANBARIAN, MAHDI. (2016). COMPARISON OF ARTIFICIAL NEURAL NETWORK METHODS FOR MODELING OF TURNING OF POLYMER MATRIX COMPOSITE. JOURNAL OF MECHANICAL ENGINEERING AMIRKABIR (AMIRKABIR), 47(2), 83-98. SID. https://sid.ir/paper/165986/en

    Vancouver: Copy

    DASHTBAYAZI MOHAMMAD REZA, GHANBARIAN MAHDI. COMPARISON OF ARTIFICIAL NEURAL NETWORK METHODS FOR MODELING OF TURNING OF POLYMER MATRIX COMPOSITE. JOURNAL OF MECHANICAL ENGINEERING AMIRKABIR (AMIRKABIR)[Internet]. 2016;47(2):83-98. Available from: https://sid.ir/paper/165986/en

    IEEE: Copy

    MOHAMMAD REZA DASHTBAYAZI, and MAHDI GHANBARIAN, “COMPARISON OF ARTIFICIAL NEURAL NETWORK METHODS FOR MODELING OF TURNING OF POLYMER MATRIX COMPOSITE,” JOURNAL OF MECHANICAL ENGINEERING AMIRKABIR (AMIRKABIR), vol. 47, no. 2, pp. 83–98, 2016, [Online]. Available: https://sid.ir/paper/165986/en

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