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A Statistical Scrutiny of Three Prominent Machine-Learning Techniques to Forecast Machining Performance Parameters of Inconel 690

A Statistical Scrutiny of Three Prominent Machine-Learning Techniques to Forecast Machining Performance Parameters of Inconel 690
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Author(s): Binayak Sen (NIT Agartala, India), Uttam Kumar Mandal (NIT Agartala, India) and Sankar Prasad Mondal (Midnapore College (Autonomous), India)
Copyright: 2018
Pages: 17
Source title: Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms
Source Author(s)/Editor(s): Sujata Dash (North Orissa University, India), B.K. Tripathy (VIT University, India) and Atta ur Rahman (University of Dammam, Saudi Arabia)
DOI: 10.4018/978-1-5225-2857-9.ch006

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Abstract

Computational approaches like “Black box” predictive modeling approaches are extensively used technique applied in machine learning operations of today. Considering the latest trends, present study compares capabilities of two different “Black box” predictive model like ANFIS and ANN with a population-based evolutionary algorithm GEP for forecasting machining parameters of Inconel 690 material, machined in a CNC-assisted 3-axis milling machine. The aims of this article are to represent considerable data showing, every techniques performance under the criteria of root mean square error (RSME), Correlational coefficient R and Mean absolute percentage error (MAPE). In this chapter, we vigorously demonstrate that the performance of the GEP model is far superior to ANFIS and ANN model.

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