Prediction of laser welding qualities of Al alloys using regression and machine learning techniques
This work compared different machine learning models such as linear regression, polynomial regression and XG-Boost for the prediction of laser welding qualities in aluminum alloys. The key weld quality parameters are ultimate load, weld width and penetration depth. Each model was trained and validat...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Materials Research Express |
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| Online Access: | https://doi.org/10.1088/2053-1591/addd68 |
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| author | Hemant Kumar Soumyabrata Chakravarty Nitesh Kuamr Nikhil Kumar |
| author_facet | Hemant Kumar Soumyabrata Chakravarty Nitesh Kuamr Nikhil Kumar |
| author_sort | Hemant Kumar |
| collection | DOAJ |
| description | This work compared different machine learning models such as linear regression, polynomial regression and XG-Boost for the prediction of laser welding qualities in aluminum alloys. The key weld quality parameters are ultimate load, weld width and penetration depth. Each model was trained and validated based on data experimentally collected by varying laser power, scanning speed and offset distance to compare them. Quantitative results are shown to prove that XG-Boost produces a better predictive accuracy, as it gives a root mean square error (RMSE) of 0.05 for ultimate load, 0.03 for penetration depth, and 0.02 for weld width. On the other hand, its linear regression counterpart has higher values of 0.08, 0.06, and 0.05, while polynomial regression-clearly outperforming its linear variant-averaged about 0.04 in these metrics. While this is so, high R-squared values, predicted by the XG-Boost model across ultimate loads, are indicative of its better competency in the capturing of complicated patterns, especially with regard to data outliers. These findings confirm the capability of XG-Boost to perform precise parameter optimization in laser welding by significantly reducing experimental trial needs and helping manufacturing efficiency with reliable data-driven predictions. |
| format | Article |
| id | doaj-art-5d2ae2ad568f41e68dfed6def4ba8bfc |
| institution | DOAJ |
| issn | 2053-1591 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Materials Research Express |
| spelling | doaj-art-5d2ae2ad568f41e68dfed6def4ba8bfc2025-08-20T02:40:36ZengIOP PublishingMaterials Research Express2053-15912025-01-0112606650110.1088/2053-1591/addd68Prediction of laser welding qualities of Al alloys using regression and machine learning techniquesHemant Kumar0https://orcid.org/0009-0007-2097-8072Soumyabrata Chakravarty1https://orcid.org/0000-0002-3579-9161Nitesh Kuamr2https://orcid.org/0009-0006-7123-2409Nikhil Kumar3https://orcid.org/0009-0007-3873-6477Computer Science & Engineering Department, Kalinga Institute of Technology, Bhubaneswar, 751024, Odisha, IndiaMechanical Engineering Department, Jadavpur University , Kolkata, 700032, West Bengal, India; Mechanical Engineering Department, Brainware University , Kolkata, 700125, West Bengal, IndiaMechanical Engineering Department, Jadavpur University , Kolkata, 700032, West Bengal, India; Mechanical Engineering Department, Brainware University , Kolkata, 700125, West Bengal, IndiaWarwick Manufacturing Group, The University of Warwick , Coventry CV 4 7AL, United KingdomThis work compared different machine learning models such as linear regression, polynomial regression and XG-Boost for the prediction of laser welding qualities in aluminum alloys. The key weld quality parameters are ultimate load, weld width and penetration depth. Each model was trained and validated based on data experimentally collected by varying laser power, scanning speed and offset distance to compare them. Quantitative results are shown to prove that XG-Boost produces a better predictive accuracy, as it gives a root mean square error (RMSE) of 0.05 for ultimate load, 0.03 for penetration depth, and 0.02 for weld width. On the other hand, its linear regression counterpart has higher values of 0.08, 0.06, and 0.05, while polynomial regression-clearly outperforming its linear variant-averaged about 0.04 in these metrics. While this is so, high R-squared values, predicted by the XG-Boost model across ultimate loads, are indicative of its better competency in the capturing of complicated patterns, especially with regard to data outliers. These findings confirm the capability of XG-Boost to perform precise parameter optimization in laser welding by significantly reducing experimental trial needs and helping manufacturing efficiency with reliable data-driven predictions.https://doi.org/10.1088/2053-1591/addd68machine learningweld qualityXG boostregressionaluminium alloy |
| spellingShingle | Hemant Kumar Soumyabrata Chakravarty Nitesh Kuamr Nikhil Kumar Prediction of laser welding qualities of Al alloys using regression and machine learning techniques Materials Research Express machine learning weld quality XG boost regression aluminium alloy |
| title | Prediction of laser welding qualities of Al alloys using regression and machine learning techniques |
| title_full | Prediction of laser welding qualities of Al alloys using regression and machine learning techniques |
| title_fullStr | Prediction of laser welding qualities of Al alloys using regression and machine learning techniques |
| title_full_unstemmed | Prediction of laser welding qualities of Al alloys using regression and machine learning techniques |
| title_short | Prediction of laser welding qualities of Al alloys using regression and machine learning techniques |
| title_sort | prediction of laser welding qualities of al alloys using regression and machine learning techniques |
| topic | machine learning weld quality XG boost regression aluminium alloy |
| url | https://doi.org/10.1088/2053-1591/addd68 |
| work_keys_str_mv | AT hemantkumar predictionoflaserweldingqualitiesofalalloysusingregressionandmachinelearningtechniques AT soumyabratachakravarty predictionoflaserweldingqualitiesofalalloysusingregressionandmachinelearningtechniques AT niteshkuamr predictionoflaserweldingqualitiesofalalloysusingregressionandmachinelearningtechniques AT nikhilkumar predictionoflaserweldingqualitiesofalalloysusingregressionandmachinelearningtechniques |