Optimization of waterjet paint removal operation using artificial neural network
Paint removal of automotive parts without environmental effects has become a critical issue around the world. The high pressure waterjet technology has received a wider acceptance for various applications involving machining, cleaning, surface treatment and material cutting. It offers an advantage t...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42307/1/Optimization%20of%20waterjet%20paint%20removal%20operation.pdf http://umpir.ump.edu.my/id/eprint/42307/2/Optimization%20of%20waterjet%20paint%20removal%20operation%20using%20artificial%20neural%20network_ABS.pdf |
| _version_ | 1825815854421377024 |
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| author | Alzaghir, Abdullah Faisal Mohd Nazir, Mat Nawi Gebremariam, Mebrahitom Asmelash Azmir, Azhari |
| author_facet | Alzaghir, Abdullah Faisal Mohd Nazir, Mat Nawi Gebremariam, Mebrahitom Asmelash Azmir, Azhari |
| author_sort | Alzaghir, Abdullah Faisal |
| collection | UMP |
| description | Paint removal of automotive parts without environmental effects has become a critical issue around the world. The high pressure waterjet technology has received a wider acceptance for various applications involving machining, cleaning, surface treatment and material cutting. It offers an advantage to remove the automotive paint due to its superior environmental benefits over mechanical cleaning methods. Therefore, it is important to predict the waterjet cleaning process for a successful application for the paint removal in the automotive industry. In the present work, ANN model was used to predict the surface roughnes after the paint removel process of automotive component using the waterjet cleaning operation. A response surface methodology approach was employed to develop the experimental design involving the first order model and the second order model of central composite design. Into training and testing, a back-propagation algorithm used in the ANN model has successfully predicted the surface roughness with an average of 80% accuracy and 3.02 mean square error. This summarizes that ANN model can sufficiently estimate surface roughness in waterjet paint removal process with a reasonable error range. |
| first_indexed | 2024-12-09T02:30:02Z |
| format | Conference or Workshop Item |
| id | UMPir42307 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-12-09T02:30:02Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | UMPir423072024-10-30T04:29:17Z http://umpir.ump.edu.my/id/eprint/42307/ Optimization of waterjet paint removal operation using artificial neural network Alzaghir, Abdullah Faisal Mohd Nazir, Mat Nawi Gebremariam, Mebrahitom Asmelash Azmir, Azhari T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Paint removal of automotive parts without environmental effects has become a critical issue around the world. The high pressure waterjet technology has received a wider acceptance for various applications involving machining, cleaning, surface treatment and material cutting. It offers an advantage to remove the automotive paint due to its superior environmental benefits over mechanical cleaning methods. Therefore, it is important to predict the waterjet cleaning process for a successful application for the paint removal in the automotive industry. In the present work, ANN model was used to predict the surface roughnes after the paint removel process of automotive component using the waterjet cleaning operation. A response surface methodology approach was employed to develop the experimental design involving the first order model and the second order model of central composite design. Into training and testing, a back-propagation algorithm used in the ANN model has successfully predicted the surface roughness with an average of 80% accuracy and 3.02 mean square error. This summarizes that ANN model can sufficiently estimate surface roughness in waterjet paint removal process with a reasonable error range. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42307/1/Optimization%20of%20waterjet%20paint%20removal%20operation.pdf pdf en http://umpir.ump.edu.my/id/eprint/42307/2/Optimization%20of%20waterjet%20paint%20removal%20operation%20using%20artificial%20neural%20network_ABS.pdf Alzaghir, Abdullah Faisal and Mohd Nazir, Mat Nawi and Gebremariam, Mebrahitom Asmelash and Azmir, Azhari (2022) Optimization of waterjet paint removal operation using artificial neural network. In: Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021 , 20 September 2021 , Gambang. pp. 11-20., 900. ISSN 1876-1100 ISBN 978-981192094-3 (Published) https://doi.org/10.1007/978-981-19-2095-0_2 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Alzaghir, Abdullah Faisal Mohd Nazir, Mat Nawi Gebremariam, Mebrahitom Asmelash Azmir, Azhari Optimization of waterjet paint removal operation using artificial neural network |
| title | Optimization of waterjet paint removal operation using artificial neural network |
| title_full | Optimization of waterjet paint removal operation using artificial neural network |
| title_fullStr | Optimization of waterjet paint removal operation using artificial neural network |
| title_full_unstemmed | Optimization of waterjet paint removal operation using artificial neural network |
| title_short | Optimization of waterjet paint removal operation using artificial neural network |
| title_sort | optimization of waterjet paint removal operation using artificial neural network |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/42307/1/Optimization%20of%20waterjet%20paint%20removal%20operation.pdf http://umpir.ump.edu.my/id/eprint/42307/2/Optimization%20of%20waterjet%20paint%20removal%20operation%20using%20artificial%20neural%20network_ABS.pdf |
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