Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization
Abstract The purpose of this paper is to investigate the effect of the annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical v...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2022-03-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-022-00070-z |
| _version_ | 1811252316805791744 |
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| author | Sanjay Chintakindi Ali Alsamhan Mustufa Haider Abidi Maduri Praveen Kumar |
| author_facet | Sanjay Chintakindi Ali Alsamhan Mustufa Haider Abidi Maduri Praveen Kumar |
| author_sort | Sanjay Chintakindi |
| collection | DOAJ |
| description | Abstract The purpose of this paper is to investigate the effect of the annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (Ψ 1 = 64.5), tool wear (Ψ 2 = 22.3) and metal removal rate (Ψ 3 = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows; surface roughness (2.5 μm, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm3/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%. |
| first_indexed | 2024-04-12T16:32:52Z |
| format | Article |
| id | doaj.art-6cc36f15f2fd445893040d31808219de |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-12T16:32:52Z |
| publishDate | 2022-03-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-6cc36f15f2fd445893040d31808219de2022-12-22T03:25:04ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832022-03-0115112210.1007/s44196-022-00070-zAnnealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm OptimizationSanjay Chintakindi0Ali Alsamhan1Mustufa Haider Abidi2Maduri Praveen Kumar3Industrial Engineering Department, College of Engineering, King Saud UniversityIndustrial Engineering Department, College of Engineering, King Saud UniversityAdvanced Manufacturing Institute, King Saud UniversityLeicester UniversityAbstract The purpose of this paper is to investigate the effect of the annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (Ψ 1 = 64.5), tool wear (Ψ 2 = 22.3) and metal removal rate (Ψ 3 = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows; surface roughness (2.5 μm, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm3/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.https://doi.org/10.1007/s44196-022-00070-zMonel-400AnnealingPrincipal component analysisOptunaMachine learningMulti-objective particle swarm optimization |
| spellingShingle | Sanjay Chintakindi Ali Alsamhan Mustufa Haider Abidi Maduri Praveen Kumar Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization International Journal of Computational Intelligence Systems Monel-400 Annealing Principal component analysis Optuna Machine learning Multi-objective particle swarm optimization |
| title | Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization |
| title_full | Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization |
| title_fullStr | Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization |
| title_full_unstemmed | Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization |
| title_short | Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization |
| title_sort | annealing of monel 400 alloy using principal component analysis hyper parameter optimization machine learning techniques and multi objective particle swarm optimization |
| topic | Monel-400 Annealing Principal component analysis Optuna Machine learning Multi-objective particle swarm optimization |
| url | https://doi.org/10.1007/s44196-022-00070-z |
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