Nur Khadijah, Mazlan and Nazrin, Mokhtar and Gebremariam, Mebrahitom Asmelash and Azmir, Azhari (2022) Prediction of abrasive waterjet machining of sheet metals using artificial neural network. In: Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021 , 20 September 2021 , Gambang. pp. 43-50., 900. ISSN 1876-1100 ISBN 978-981192094-3 (Published)
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Abstract
High pressure waterjet technology has received a wider acceptance for various applications involving machining, cleaning, surface treatment and material cutting. Machining of soft and thin materials with acceptable cutting quality requires a relatively low waterjet pump capacity typically below 150 MPa. The present study attempts to predict the surface roughness during the waterjet machining process for a successful cutting of sheet metals using low pressure. Artificial neural network model was used as the method for prediction. Taguchi method with L36 orthogonal array was employed to develop the experimental design. A back-propagation algorithm used in the ANN model has successfully predicted the surface roughness with the mean squared error to be below 10%. This summarizes that ANN model can sufficiently estimate surface roughness in the abrasive waterjet machining of sheet metals with a reasonable error range.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Abrasive waterjet; Artificial neural network; Sheet metal; Surface roughness; Waterjet cutting |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures |
| Faculty/Division: | Institute of Postgraduate Studies College of Engineering Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 30 Oct 2024 04:28 |
| Last Modified: | 30 Oct 2024 04:28 |
| URI: | http://umpir.ump.edu.my/id/eprint/42304 |
| Download Statistic: | View Download Statistics |
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