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Published in: Advances in Manufacturing 2/2021

08-03-2021

Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process

Authors: Chunling Du, Choon Lim Ho, Jacek Kaminski

Published in: Advances in Manufacturing | Issue 2/2021

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Abstract

High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control (CNC) machining. Quality monitoring and prediction is of great importance to assure high-quality or zero defect production. In this work, we consider roughness parameter Ra, profile deviation Pt and roundness deviation RONt of the machined products by a lathe. Intrinsically, these three parameters are much related to the machine spindle parameters of preload, temperature, and rotations per minute (RPMs), while in this paper, spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters. Power spectral density (PSD) based feature extraction, the method to generate compact and well-correlated features, is proposed in details in this paper. Using the efficient features, neural network based machine learning technique turns out to be able to result in high prediction accuracy with R2 score of 0.92 for roughness, 0.86 for profile, and 0.95 for roundness.

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Metadata
Title
Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process
Authors
Chunling Du
Choon Lim Ho
Jacek Kaminski
Publication date
08-03-2021
Publisher
Shanghai University
Published in
Advances in Manufacturing / Issue 2/2021
Print ISSN: 2095-3127
Electronic ISSN: 2195-3597
DOI
https://doi.org/10.1007/s40436-021-00345-2

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