Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints
�Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classificatio...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2021-10-01
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| Series: | Fracture and Structural Integrity |
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| Online Access: | https://www.fracturae.com/index.php/fis/article/view/3181/3342 |
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| author | Akshansh Mishra Apoorv Vats |
| author_facet | Akshansh Mishra Apoorv Vats |
| author_sort | Akshansh Mishra |
| collection | DOAJ |
| description | �Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K- Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture location in dissimilar Friction Stir Welded AA6061-T651 and AA7075-T651 alloy. In the given dataset, rotational speed (RPM), welding speed (mm/min), pin profile, and axial force (kN) were the input parameters while Fracture location is the output parameter. The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithms |
| format | Article |
| id | doaj-art-ef59de95e12e43088c60b0eb11da7183 |
| institution | DOAJ |
| issn | 1971-8993 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-ef59de95e12e43088c60b0eb11da71832025-08-20T02:42:57ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932021-10-01155824225310.3221/IGF-ESIS.58.1810.3221/IGF-ESIS.58.18Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded jointsAkshansh MishraApoorv Vats�Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K- Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture location in dissimilar Friction Stir Welded AA6061-T651 and AA7075-T651 alloy. In the given dataset, rotational speed (RPM), welding speed (mm/min), pin profile, and axial force (kN) were the input parameters while Fracture location is the output parameter. The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithmshttps://www.fracturae.com/index.php/fis/article/view/3181/3342fracture locationmachine learningclassificationfriction stir weldingdissimilar jointspython programming |
| spellingShingle | Akshansh Mishra Apoorv Vats Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints Fracture and Structural Integrity fracture location machine learning classification friction stir welding dissimilar joints python programming |
| title | Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints |
| title_full | Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints |
| title_fullStr | Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints |
| title_full_unstemmed | Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints |
| title_short | Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints |
| title_sort | supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints |
| topic | fracture location machine learning classification friction stir welding dissimilar joints python programming |
| url | https://www.fracturae.com/index.php/fis/article/view/3181/3342 |
| work_keys_str_mv | AT akshanshmishra supervisedmachinelearningclassificationalgorithmsfordetectionoffracturelocationindissimilarfrictionstirweldedjoints AT apoorvvats supervisedmachinelearningclassificationalgorithmsfordetectionoffracturelocationindissimilarfrictionstirweldedjoints |