Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing
This research investigates the hard part turning of DC53 tool steel, which is engineered for better mechanical properties compared to AISI D2 tool steel, using Xcel cubic boron nitride. The machining input parameters such as workpiece hardness (different heat treatments), cutting speed, feed rate, a...
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| Format: | Article |
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AIP Publishing LLC
2025-01-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0240559 |
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| author | Mehdi Tlija Muhammad Sana Anamta Khan Sana Hassan Muhammad Umar Farooq |
| author_facet | Mehdi Tlija Muhammad Sana Anamta Khan Sana Hassan Muhammad Umar Farooq |
| author_sort | Mehdi Tlija |
| collection | DOAJ |
| description | This research investigates the hard part turning of DC53 tool steel, which is engineered for better mechanical properties compared to AISI D2 tool steel, using Xcel cubic boron nitride. The machining input parameters such as workpiece hardness (different heat treatments), cutting speed, feed rate, and depth of cut are used to thoroughly evaluate process science across conflicting machinability attributes such as cutting tool life, machined workpiece surface roughness, volume of material removed, machine tool power consumption, and tool-workpiece zone temperature. A full factorial design of experiments with two levels, resulting in 16 experiments, is performed with statistical parametric significance analysis to better control process variability. Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. The optimized architectures are used as prediction models to a non-sorting genetic algorithm (NSGA-II) to determine the optimal compromise among all conflicting responses. The significance analysis highlighted that heat treatment is the most influential variable on machinability, with a significance of 74.63% on tool life, 59.03% on roughness, 66.45% on material removed, 38.03% on power consumption, and 29.60% on interaction-zone temperature. The confidence of all ANN architectures is achieved above 0.97 R2 to accurately incorporate parametric relations with physical mechanisms. The compromise against conflicting machinability attributes identified by NSGA-II optimization results in a 92.05% increase in tool life, a 91.83% increase in volume removed, a 33.33% decrease in roughness, a 26.73% decline in power consumption, and a 9.61% reduction in machining temperature. The process variability is thoroughly analyzed using statistical and physical analyses and computational intelligence, which will guide machinists in better decision-making. |
| format | Article |
| id | doaj-art-6e98bd34935946ef8a7f43bc695ee811 |
| institution | Kabale University |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-6e98bd34935946ef8a7f43bc695ee8112025-02-03T16:40:41ZengAIP Publishing LLCAIP Advances2158-32262025-01-01151015017015017-1010.1063/5.0240559Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturingMehdi Tlija0Muhammad Sana1Anamta Khan2Sana Hassan3Muhammad Umar Farooq4Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaDepartment of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Industrial Engineering and Management, University of the Punjab. Lahore 54890, PakistanThe Sargent Centre for Process Systems Engineering, University College London, Torrington Place, London WC1E 7JE, United KingdomThis research investigates the hard part turning of DC53 tool steel, which is engineered for better mechanical properties compared to AISI D2 tool steel, using Xcel cubic boron nitride. The machining input parameters such as workpiece hardness (different heat treatments), cutting speed, feed rate, and depth of cut are used to thoroughly evaluate process science across conflicting machinability attributes such as cutting tool life, machined workpiece surface roughness, volume of material removed, machine tool power consumption, and tool-workpiece zone temperature. A full factorial design of experiments with two levels, resulting in 16 experiments, is performed with statistical parametric significance analysis to better control process variability. Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. The optimized architectures are used as prediction models to a non-sorting genetic algorithm (NSGA-II) to determine the optimal compromise among all conflicting responses. The significance analysis highlighted that heat treatment is the most influential variable on machinability, with a significance of 74.63% on tool life, 59.03% on roughness, 66.45% on material removed, 38.03% on power consumption, and 29.60% on interaction-zone temperature. The confidence of all ANN architectures is achieved above 0.97 R2 to accurately incorporate parametric relations with physical mechanisms. The compromise against conflicting machinability attributes identified by NSGA-II optimization results in a 92.05% increase in tool life, a 91.83% increase in volume removed, a 33.33% decrease in roughness, a 26.73% decline in power consumption, and a 9.61% reduction in machining temperature. The process variability is thoroughly analyzed using statistical and physical analyses and computational intelligence, which will guide machinists in better decision-making.http://dx.doi.org/10.1063/5.0240559 |
| spellingShingle | Mehdi Tlija Muhammad Sana Anamta Khan Sana Hassan Muhammad Umar Farooq Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing AIP Advances |
| title | Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing |
| title_full | Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing |
| title_fullStr | Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing |
| title_full_unstemmed | Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing |
| title_short | Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor manufacturing |
| title_sort | machine learning assisted prediction with data driven robust optimization machining process modeling of hard part turning of dc53 for tooling applications supporting semiconductor manufacturing |
| url | http://dx.doi.org/10.1063/5.0240559 |
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