Machine Learning: Supervised Algorithms to Determine the Defect in High-Precision Foundry Operation
In this paper, we represent a method for machine learning to predict the defect in foundry operation. Foundry has become a driving tool to produce the part to another industry like automobile, marine, and weapon. These foundry processes mainly have two critical problems to decrease the quality assur...
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| Veröffentlicht in: | Journal of nanomaterials 2022, Vol.2022 (1) |
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| container_title | Journal of nanomaterials |
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| creator | BramahHazela Hymavathi, J. Kumar, T. Rajasanthosh Kavitha, S. Deepa, D. Lalar, Sachin Karunakaran, Prabakaran |
| description | In this paper, we represent a method for machine learning to predict the defect in foundry operation. Foundry has become a driving tool to produce the part to another industry like automobile, marine, and weapon. These foundry processes mainly have two critical problems to decrease the quality assurance. Now, we have to predict the defect to increase the quality of foundry operation. The foundry process’s failure is associated with micro shrinkage and ultimate tensile strength. We process by utilizing a machine learning classifier to predict the micro shrinkage and maximum tensile strength and describe the process, learning process, and evaluate the predataset from the foundry process to compare the accuracy and stability. |
| doi_str_mv | 10.1155/2022/1732441 |
| format | Article |
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Rajasanthosh ; Kavitha, S. ; Deepa, D. ; Lalar, Sachin ; Karunakaran, Prabakaran</creator><contributor>Chelladurai, Samson Jerold Samuel ; Samson Jerold Samuel Chelladurai</contributor><creatorcontrib>BramahHazela ; Hymavathi, J. ; Kumar, T. Rajasanthosh ; Kavitha, S. ; Deepa, D. ; Lalar, Sachin ; Karunakaran, Prabakaran ; Chelladurai, Samson Jerold Samuel ; Samson Jerold Samuel Chelladurai</creatorcontrib><description>In this paper, we represent a method for machine learning to predict the defect in foundry operation. Foundry has become a driving tool to produce the part to another industry like automobile, marine, and weapon. These foundry processes mainly have two critical problems to decrease the quality assurance. Now, we have to predict the defect to increase the quality of foundry operation. The foundry process’s failure is associated with micro shrinkage and ultimate tensile strength. 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| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection; Free Full-Text Journals in Chemistry |
| subjects | Algorithms Classification Datasets Defects Expected values Machine learning Mechanical properties Nanomaterials Quality assurance Shrinkage Tensile strength Ultimate tensile strength |
| title | Machine Learning: Supervised Algorithms to Determine the Defect in High-Precision Foundry Operation |
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