Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction
This study uses advanced machine learning approaches to predict the kerf open deviation (KOD) when a CO2 laser is used to cut polymeric materials. Four polymeric materials, namely polyethylene (PE), polymethyl methacrylate (PMMA), polypropylene (PP), and polyvinyl chloride (PVC), were cut under the...
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ftdoajarticles:oai:doaj.org/article:6d65a92ad1f541b39cc0438142900b7f 2023-10-09T21:52:13+02:00 Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction Abdulsalam M. Alhawsawi Essam B. Moustafa Manabu Fujii Essam M. Banoqitah Ammar Elsheikh 2023-10-01T00:00:00Z https://doi.org/10.1016/j.jestch.2023.101519 https://doaj.org/article/6d65a92ad1f541b39cc0438142900b7f EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2215098623001970 https://doaj.org/toc/2215-0986 2215-0986 doi:10.1016/j.jestch.2023.101519 https://doaj.org/article/6d65a92ad1f541b39cc0438142900b7f Engineering Science and Technology, an International Journal, Vol 46, Iss , Pp 101519- (2023) Laser cutting Polymeric materials Kerf open deviation Humpback whale optimizer Machine learning Engineering (General). Civil engineering (General) TA1-2040 article 2023 ftdoajarticles https://doi.org/10.1016/j.jestch.2023.101519 2023-09-17T00:34:16Z This study uses advanced machine learning approaches to predict the kerf open deviation (KOD) when a CO2 laser is used to cut polymeric materials. Four polymeric materials, namely polyethylene (PE), polymethyl methacrylate (PMMA), polypropylene (PP), and polyvinyl chloride (PVC), were cut under the same conditions. The process control factors were the power of the laser beam (80–140 W) and cutting speed (1–6 mm/s), while sheet thickness, standoff distance, and gas pressure were kept constant during experiments. KOD between the upper and lower opens of the kerf was the process response. KOD was predicted using three machine learning models, namely a conventional artificial neural network (ANN), a hybrid neural network–humpback whale optimizer (HWO-ANN), and a hybrid neural network–particle swarm optimizer (PSO-ANN). Experimental data for all polymeric materials were employed to train and test all models. The hybrid neural network–humpback whale optimizer model outperformed other models to predict KOD for all cut materials. The root-mean-square error between predicted and experimental data was 0.349–0.627 µm, 0.085–0.242 µm, and 0.023–0.079 µm for conventional neural network, neural network–particle swarm model, and neural network–humpback whale model, respectively. Article in Journal/Newspaper Humpback Whale Directory of Open Access Journals: DOAJ Articles Engineering Science and Technology, an International Journal 46 101519 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Laser cutting Polymeric materials Kerf open deviation Humpback whale optimizer Machine learning Engineering (General). Civil engineering (General) TA1-2040 |
spellingShingle |
Laser cutting Polymeric materials Kerf open deviation Humpback whale optimizer Machine learning Engineering (General). Civil engineering (General) TA1-2040 Abdulsalam M. Alhawsawi Essam B. Moustafa Manabu Fujii Essam M. Banoqitah Ammar Elsheikh Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction |
topic_facet |
Laser cutting Polymeric materials Kerf open deviation Humpback whale optimizer Machine learning Engineering (General). Civil engineering (General) TA1-2040 |
description |
This study uses advanced machine learning approaches to predict the kerf open deviation (KOD) when a CO2 laser is used to cut polymeric materials. Four polymeric materials, namely polyethylene (PE), polymethyl methacrylate (PMMA), polypropylene (PP), and polyvinyl chloride (PVC), were cut under the same conditions. The process control factors were the power of the laser beam (80–140 W) and cutting speed (1–6 mm/s), while sheet thickness, standoff distance, and gas pressure were kept constant during experiments. KOD between the upper and lower opens of the kerf was the process response. KOD was predicted using three machine learning models, namely a conventional artificial neural network (ANN), a hybrid neural network–humpback whale optimizer (HWO-ANN), and a hybrid neural network–particle swarm optimizer (PSO-ANN). Experimental data for all polymeric materials were employed to train and test all models. The hybrid neural network–humpback whale optimizer model outperformed other models to predict KOD for all cut materials. The root-mean-square error between predicted and experimental data was 0.349–0.627 µm, 0.085–0.242 µm, and 0.023–0.079 µm for conventional neural network, neural network–particle swarm model, and neural network–humpback whale model, respectively. |
format |
Article in Journal/Newspaper |
author |
Abdulsalam M. Alhawsawi Essam B. Moustafa Manabu Fujii Essam M. Banoqitah Ammar Elsheikh |
author_facet |
Abdulsalam M. Alhawsawi Essam B. Moustafa Manabu Fujii Essam M. Banoqitah Ammar Elsheikh |
author_sort |
Abdulsalam M. Alhawsawi |
title |
Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction |
title_short |
Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction |
title_full |
Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction |
title_fullStr |
Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction |
title_full_unstemmed |
Kerf characteristics during CO2 laser cutting of polymeric materials: Experimental investigation and machine learning-based prediction |
title_sort |
kerf characteristics during co2 laser cutting of polymeric materials: experimental investigation and machine learning-based prediction |
publisher |
Elsevier |
publishDate |
2023 |
url |
https://doi.org/10.1016/j.jestch.2023.101519 https://doaj.org/article/6d65a92ad1f541b39cc0438142900b7f |
genre |
Humpback Whale |
genre_facet |
Humpback Whale |
op_source |
Engineering Science and Technology, an International Journal, Vol 46, Iss , Pp 101519- (2023) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2215098623001970 https://doaj.org/toc/2215-0986 2215-0986 doi:10.1016/j.jestch.2023.101519 https://doaj.org/article/6d65a92ad1f541b39cc0438142900b7f |
op_doi |
https://doi.org/10.1016/j.jestch.2023.101519 |
container_title |
Engineering Science and Technology, an International Journal |
container_volume |
46 |
container_start_page |
101519 |
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1779315361340981248 |