Machine Learning: Supervised Algorithms to Determine the Defect in High-Precision Foundry Operation. (27th May 2022)

• Record Type: Journal Article
• Authors: BramahHazela, ; Hymavathi, J.; Kumar, T. Rajasanthosh; Kavitha, S.; Deepa, D.; Lalar, Sachin; Karunakaran, Prabakaran
• Other Names: Chelladurai Samson Jerold Samuel Academic Editor.
• Is Part Of: Journal of nanomaterials. Volume 2022(2022)
• Publication Date: 2022-05-27
• Subjects: Nanostructured materials -- Periodicals; Nanotechnology -- Periodicals; Nanomatériaux; Nanostructured materials; Nanotechnology; Nanostructures; Nanotechnology; Periodicals; Fulltext; Internet Resources; Periodicals; 620.115
• DOI: 10.1155/2022/1732441
• Languages: English
• ISSNs: 1687-4110

Abstract

Abstract : 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.