Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145903
Title: Modelling of material removal in abrasive belt grinding process : a regression approach
Authors: Pandiyan, Vigneashwara
Caesarendra, Wahyu
Glowacz, Adam
Tjahjowidodo, Tegoeh
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Source: Pandiyan, V., Caesarendra, W., Glowacz, A., & Tjahjowidodo, T. (2020). Modelling of material removal in abrasive belt grinding process : a regression approach. Symmetry, 12(1), 99-. doi:10.3390/sym12010099
Journal: Symmetry
Abstract: This article explores the effects of parameters such as cutting speed, force, polymer wheel hardness, feed, and grit size in the abrasive belt grinding process to model material removal. The process has high uncertainty during the interaction between the abrasives and the underneath surface, therefore the theoretical material removal models developed in belt grinding involve assumptions. A conclusive material removal model can be developed in such a dynamic process involving multiple parameters using statistical regression techniques. Six different regression modelling methodologies, namely multiple linear regression, stepwise regression, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and random forests (RF) have been applied to the experimental data determined using the Taguchi design of experiments (DoE). The results obtained by the six models have been assessed and compared. All five models, except multiple linear regression, demonstrated a relatively low prediction error. Regarding the influence of the examined belt grinding parameters on the material removal, inference from some statistical models shows that the grit size has the most substantial effect. The proposed regression models can likely be applied for achieving desired material removal by defining process parameter levels without the need to conduct physical belt grinding experiments.
URI: https://hdl.handle.net/10356/145903
ISSN: 2073-8994
DOI: 10.3390/sym12010099
Rights: © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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