Modelling of material removal in abrasive belt grinding process : a regression approach
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, therefo...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145903 |
| _version_ | 1826123392831455232 |
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| author | Pandiyan, Vigneashwara Caesarendra, Wahyu Glowacz, Adam Tjahjowidodo, Tegoeh |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Pandiyan, Vigneashwara Caesarendra, Wahyu Glowacz, Adam Tjahjowidodo, Tegoeh |
| author_sort | Pandiyan, Vigneashwara |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:03:55Z |
| format | Journal Article |
| id | ntu-10356/145903 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:03:55Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1459032023-03-04T17:25:08Z Modelling of material removal in abrasive belt grinding process : a regression approach Pandiyan, Vigneashwara Caesarendra, Wahyu Glowacz, Adam Tjahjowidodo, Tegoeh School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Abrasive Belt Grinding Predictive Model 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. National Research Foundation (NRF) Published version This work was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme. 2021-01-14T02:13:59Z 2021-01-14T02:13:59Z 2020 Journal Article 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 2073-8994 https://hdl.handle.net/10356/145903 10.3390/sym12010099 1 12 en Symmetry © 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/). application/pdf |
| spellingShingle | Engineering::Mechanical engineering Abrasive Belt Grinding Predictive Model Pandiyan, Vigneashwara Caesarendra, Wahyu Glowacz, Adam Tjahjowidodo, Tegoeh Modelling of material removal in abrasive belt grinding process : a regression approach |
| title | Modelling of material removal in abrasive belt grinding process : a regression approach |
| title_full | Modelling of material removal in abrasive belt grinding process : a regression approach |
| title_fullStr | Modelling of material removal in abrasive belt grinding process : a regression approach |
| title_full_unstemmed | Modelling of material removal in abrasive belt grinding process : a regression approach |
| title_short | Modelling of material removal in abrasive belt grinding process : a regression approach |
| title_sort | modelling of material removal in abrasive belt grinding process a regression approach |
| topic | Engineering::Mechanical engineering Abrasive Belt Grinding Predictive Model |
| url | https://hdl.handle.net/10356/145903 |
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