Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach
This paper investigates the capability of Laser Surface Textruing (LST) to induce texture on Ti-6Al-4V, aiming on optimizing process parameters viz. average power, pulse frequency, scanning speed, and gas pressure using the Driving Training-based Optimization (DTBO) algorithm. Both single and multi-...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024016712 |
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| author | Ishwer Shivakoti Sunny Diyaley Partha Protim Das Abhijit Bhowmik A. Johnson Santhosh |
| author_facet | Ishwer Shivakoti Sunny Diyaley Partha Protim Das Abhijit Bhowmik A. Johnson Santhosh |
| author_sort | Ishwer Shivakoti |
| collection | DOAJ |
| description | This paper investigates the capability of Laser Surface Textruing (LST) to induce texture on Ti-6Al-4V, aiming on optimizing process parameters viz. average power, pulse frequency, scanning speed, and gas pressure using the Driving Training-based Optimization (DTBO) algorithm. Both single and multi-objective optimizations are conducted to determine optimal parametric settings. The study systematically examines the impression of these LBM process parameters on various responses. Comparative analyses was performed with five other metaheuristic algorithms such as Ant colony optimization, Particle swarm optimization, Differential evolution, Firefly algorithm, Teaching-learning-based optimization, and Artificial bee colony. Furthermore, statistical validation via paired t-tests confirms the unique effectiveness of the DTBO algorithm. Detailed examination through developed box plots and convergence diagrams consistently demonstrates DTBO superior performance in terms of accuracy, minimal variability in optimal solutions, and reduced computational effort. The DTBO achieves a higher MRR by 35.7 %, 20 %, 11.9 %, 54.7 %, and 33.3 % compared to ABC, ACO, FA, DE, and TLBO, respectively. Simultaneously, DTBO also achieves a lower ATW by 13.6 %, 14.8 %, 3.02 %, 15.9 %, and 16.1 % compared to the same algorithms. These results underscore DTBO's superior performance in achieving improved MRR values and reduced ATW values across the considered optimization algorithms. Hence, The DTBO algorithm demonstrates robustness and applicability in optimizing LBM processes in context of laser texturing, which may enhance manufacturing efficiency and product quality significantly. |
| format | Article |
| id | doaj-art-d2ea15cd9de84e67a0f70fa1bd4af6b3 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-d2ea15cd9de84e67a0f70fa1bd4af6b32025-08-20T02:52:28ZengElsevierResults in Engineering2590-12302024-12-012410341910.1016/j.rineng.2024.103419Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approachIshwer Shivakoti0Sunny Diyaley1Partha Protim Das2Abhijit Bhowmik3A. Johnson Santhosh4Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India; Centre for Distance and Online Education, Sikkim Manipal University, Sikkim, IndiaDepartment of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, IndiaDepartment of Mechanical Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, IndiaDepartment of Mechanical Engineering, Dream Institute of Technology, Kolkata 700104, India; Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, IndiaFaculty of Mechanical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia; Corresponding author.This paper investigates the capability of Laser Surface Textruing (LST) to induce texture on Ti-6Al-4V, aiming on optimizing process parameters viz. average power, pulse frequency, scanning speed, and gas pressure using the Driving Training-based Optimization (DTBO) algorithm. Both single and multi-objective optimizations are conducted to determine optimal parametric settings. The study systematically examines the impression of these LBM process parameters on various responses. Comparative analyses was performed with five other metaheuristic algorithms such as Ant colony optimization, Particle swarm optimization, Differential evolution, Firefly algorithm, Teaching-learning-based optimization, and Artificial bee colony. Furthermore, statistical validation via paired t-tests confirms the unique effectiveness of the DTBO algorithm. Detailed examination through developed box plots and convergence diagrams consistently demonstrates DTBO superior performance in terms of accuracy, minimal variability in optimal solutions, and reduced computational effort. The DTBO achieves a higher MRR by 35.7 %, 20 %, 11.9 %, 54.7 %, and 33.3 % compared to ABC, ACO, FA, DE, and TLBO, respectively. Simultaneously, DTBO also achieves a lower ATW by 13.6 %, 14.8 %, 3.02 %, 15.9 %, and 16.1 % compared to the same algorithms. These results underscore DTBO's superior performance in achieving improved MRR values and reduced ATW values across the considered optimization algorithms. Hence, The DTBO algorithm demonstrates robustness and applicability in optimizing LBM processes in context of laser texturing, which may enhance manufacturing efficiency and product quality significantly.http://www.sciencedirect.com/science/article/pii/S2590123024016712DTBO algorithmLaser surface texturingMetaheuristic algorithmsOptimization |
| spellingShingle | Ishwer Shivakoti Sunny Diyaley Partha Protim Das Abhijit Bhowmik A. Johnson Santhosh Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach Results in Engineering DTBO algorithm Laser surface texturing Metaheuristic algorithms Optimization |
| title | Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach |
| title_full | Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach |
| title_fullStr | Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach |
| title_full_unstemmed | Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach |
| title_short | Enhancing laser surface texturing with driving training-based optimization: A metaheuristic approach |
| title_sort | enhancing laser surface texturing with driving training based optimization a metaheuristic approach |
| topic | DTBO algorithm Laser surface texturing Metaheuristic algorithms Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024016712 |
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