Integrating machine learning into one of the manufacturing processes, i.e., forging, is mainly concerned with making the system more intelligent by incorporating them to exhibit global understanding. Sometimes the engineer/operator can find the defects during or after the forging operation. So, the system will need some input to identify the different types of categorized defects. And also, according to that, we will develop the intelligent fault diagnosis process. We should calculate the statistical probability theory. Now, we implement the system which is the structure of the fault analysis system for the forging process. In the structure, we demonstrate the defect of the forged part, use the given imported probability to find the possible causes, and provide some remainders to reduce the fault. For enhancement of feature needs, this work includes more integration of AI with forging.
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.
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