Development of Multi-Objective Prediction Model for Wire Electrical Discharge Machining of Inconel 718 Nickel-Based Superalloy

Abstract:

Owing to the fact that conventional Taguchi methods cannot predict the experimentresults of non-level values, this study developed a staged Taguchi neural network prediction modelthat combines the merits of experimental data from the Taguchi method and the learningcapabilities of artificial neural networks. We first used the optimal parameter combinations derivedfrom the L9 orthogonal array experiment data and grey relational analysis as training examples forthe Stage-1 network to construct a preliminary network. Next, we used the crucial factors in theoptimal parameter combinations derived from the grey relational analysis as additional trainingexamples for the Stage-2 network. The results of the staged Taguchi neural network predictionmodel indicate that the prediction performance of the preliminary network in Stage 1 was poor dueto an insufficient number of training examples, while the Stage-2 network produced excellentprediction results.

• Periodical: Key Engineering Materials (Volume 789)
• Pages: 37-44
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.789.37
• Online since: November 2018
• Authors: Ching Been Yang*, Cang Ge Lin, Hsiu Lu Chiang, Jia Lin Zhan
• Keywords: Artificial Neural Network, Grey Relational Analysis, Inconel 718, Surface Roughness, Wire Electrical Discharge Machining

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