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Modelling of Material Removal in Abrasive Belt Grinding Process: A Regression Approach

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639815, Singapore
Smart Manufacturing Group, Advanced Remanufacturing and Technology Centre, A*STAR, Singapore 637143, Singapore
Empa, Swiss Federal Laboratories for Materials Science & Technology, Laboratory for Advanced Materials Processing, Feuerwerkerstrasse 39, 3602 Thun, Switzerland
Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link BE1410, Brunei
Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, Poland
Department of Mechanical Engineering, KU Leuven, Technology campus De Nayer, Jan De Nayerlaan 5, 2860 St.-Katelijne-Waver, Belgium
Author to whom correspondence should be addressed.
Symmetry 2020, 12(1), 99;
Received: 25 November 2019 / Revised: 19 December 2019 / Accepted: 28 December 2019 / Published: 5 January 2020
(This article belongs to the Special Issue Symmetry in Mechanical Engineering)
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.
Keywords: abrasive belt grinding; predictive model; regression; material removal abrasive belt grinding; predictive model; regression; material removal
MDPI and ACS Style

Pandiyan, V.; Caesarendra, W.; Glowacz, A.; Tjahjowidodo, T. Modelling of Material Removal in Abrasive Belt Grinding Process: A Regression Approach. Symmetry 2020, 12, 99.

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