Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah Optimizer
This study focuses on modeling and optimizing the friction stir welding (FSW) process of wood-plastic composites (WPCs) made of low-density polyethylene reinforced with wood flour to improve joint performance. The input parameters considered are rotational speed (RS) and welding speed (WS), while th...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785424030606 |
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| author | Ammar H. Elsheikh Mohamad Elmiligy Ahmed M. El-Kassas |
| author_facet | Ammar H. Elsheikh Mohamad Elmiligy Ahmed M. El-Kassas |
| author_sort | Ammar H. Elsheikh |
| collection | DOAJ |
| description | This study focuses on modeling and optimizing the friction stir welding (FSW) process of wood-plastic composites (WPCs) made of low-density polyethylene reinforced with wood flour to improve joint performance. The input parameters considered are rotational speed (RS) and welding speed (WS), while the output properties are flexural strength and modulus, measured after tool entry and before tool exit. Three machine learning algorithms—multilayer perceptron (MLP), decision tree (DT), and adaptive neuro-fuzzy inference system (ANFIS)—were used to model the relationships between the input parameters and output responses. The ANFIS model showed the best predictive performance, with R2 values above 0.98 and minimal errors, indicating its reliability in modeling FSW properties. Optimization using the ANFIS model and the Cheetah Optimizer determined the optimal parameters of 1116 RPM for RS and 0.20 mm/s for WS, which resulted in a maximum flexural strength of 13.96 MPa after tool entry and 13.50 MPa before tool exit, along with consistent flexural modulus values. The study emphasizes the importance of uniform heat distribution to prevent polymer degradation and improve weld quality. Verification experiments showed the optimization model's effectiveness, with relative errors of 13.42% for entry strength and 5.50% for exit strength. This research demonstrates that combining machine learning and metaheuristic optimization can significantly enhance FSW joint performance in WPCs, offering insights for improving strength, consistency, and thermal stability in WPC welding technology. |
| format | Article |
| id | doaj-art-b04f219e06a44171903b4cc1a22c3688 |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-b04f219e06a44171903b4cc1a22c36882025-01-19T06:25:59ZengElsevierJournal of Materials Research and Technology2238-78542025-01-013425392552Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah OptimizerAmmar H. Elsheikh0Mohamad Elmiligy1Ahmed M. El-Kassas2Corresponding author.; Department of Production Engineering and Mechanical Design, Tanta University, Tanta, 31527, Egypt; Faculty of Engineering, Pharos University in Alexandria, EgyptDepartment of Production Engineering and Mechanical Design, Tanta University, Tanta, 31527, Egypt; Faculty of Engineering, Pharos University in Alexandria, EgyptDepartment of Production Engineering and Mechanical Design, Tanta University, Tanta, 31527, Egypt; Faculty of Engineering, Pharos University in Alexandria, EgyptThis study focuses on modeling and optimizing the friction stir welding (FSW) process of wood-plastic composites (WPCs) made of low-density polyethylene reinforced with wood flour to improve joint performance. The input parameters considered are rotational speed (RS) and welding speed (WS), while the output properties are flexural strength and modulus, measured after tool entry and before tool exit. Three machine learning algorithms—multilayer perceptron (MLP), decision tree (DT), and adaptive neuro-fuzzy inference system (ANFIS)—were used to model the relationships between the input parameters and output responses. The ANFIS model showed the best predictive performance, with R2 values above 0.98 and minimal errors, indicating its reliability in modeling FSW properties. Optimization using the ANFIS model and the Cheetah Optimizer determined the optimal parameters of 1116 RPM for RS and 0.20 mm/s for WS, which resulted in a maximum flexural strength of 13.96 MPa after tool entry and 13.50 MPa before tool exit, along with consistent flexural modulus values. The study emphasizes the importance of uniform heat distribution to prevent polymer degradation and improve weld quality. Verification experiments showed the optimization model's effectiveness, with relative errors of 13.42% for entry strength and 5.50% for exit strength. This research demonstrates that combining machine learning and metaheuristic optimization can significantly enhance FSW joint performance in WPCs, offering insights for improving strength, consistency, and thermal stability in WPC welding technology.http://www.sciencedirect.com/science/article/pii/S2238785424030606Friction stir weldingWood-plastic compositeJoint propertiesMachine learningCheetah optimizer |
| spellingShingle | Ammar H. Elsheikh Mohamad Elmiligy Ahmed M. El-Kassas Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah Optimizer Journal of Materials Research and Technology Friction stir welding Wood-plastic composite Joint properties Machine learning Cheetah optimizer |
| title | Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah Optimizer |
| title_full | Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah Optimizer |
| title_fullStr | Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah Optimizer |
| title_full_unstemmed | Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah Optimizer |
| title_short | Optimization of joint strength in friction stir welded wood plastic composites using ANFIS and Cheetah Optimizer |
| title_sort | optimization of joint strength in friction stir welded wood plastic composites using anfis and cheetah optimizer |
| topic | Friction stir welding Wood-plastic composite Joint properties Machine learning Cheetah optimizer |
| url | http://www.sciencedirect.com/science/article/pii/S2238785424030606 |
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