GPR and RVM-Based Predictions of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel

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Author(s): Pijush Samui (VIT University, India)and H. Yildirim Dalkilic (Erzincan University, Turkey)
Copyright: 2015
Pages: 27
Source title: Handbook of Research on Artificial Intelligence Techniques and Algorithms
Source Author(s)/Editor(s): Pandian Vasant (University of Technology Petronas, Malaysia)
DOI: 10.4018/978-1-4666-7258-1.ch024

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

This chapter examines the capability of Gaussian Process Regression (GPR) and Relevance Vector Machine (RVM) for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel. This chapter uses GPR and RVM as regression techniques. The database contains information about cutting tool, feed rate, cutting speed, surface roughness, and roundness error. Cutting tool, feed rate, and cutting speed are considered inputs of GPR and RVM. The outputs of GPR and RVM are surface roughness and roundness error. In RVM, radial basis function is adopted as kernel function. GPR uses radial basis function as covariance function. The obtained variance can be used to determine uncertainty. A sensitivity analysis is also carried out. This chapter gives robust models based on RVM and GPR for prediction of surface and hole quality in drilling of AISI D2 cold work tool steel.

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GPR and RVM-Based Predictions of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel

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