Smart Diagnostic Expert System for Defect in Forging Process by Using Machine Learning Process

Author:

Mewada Shivlal1ORCID,Saroliya Anil2,Chandramouli N.3,Kumar T. Rajasanthosh4,Lakshmi M.5,Mary S. Suma Christal6,Jayakumar Mani7ORCID

Affiliation:

1. Department of Computer Science, Govt. College, Makdone (Vikram University), Ujjain, India

2. Department of IT and Engineering, Amity University in Tashkent, Uzbekistan

3. Department of Computer Science, Vaageswari College of Engineering, Karimanagar, India

4. Department of Mechanical Engineering, Oriental Institute of Science and Technology, Bhopal, India

5. Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, India

6. Department of IT, Panimalar Institute of Technology, India

7. Department of Chemical Engineering, Haramaya Institute of Technology, Haramaya University, P.B.No. 138, Dire Dawa, Ethiopia

Abstract

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.

Publisher

Hindawi Limited

Subject

General Materials Science

Reference31 articles.

1. Machine Learning Acoustic Emission Based Monitoring of Cold Forging for Smart Manufacturing: A Review

2. A review of automation in manufacturing illustrated by a case study on mixed-mode hot forging

3. Using deep reinforcement learning for zero defect smart forging;Y. Ma,2022

4. Intelligent fault diagnosis of hot die forging press based on binary decision diagram and fault tree analysis

5. Diagnostics expert system for defects in forged parts;S. Fujikawa;Transactions on Information and Communications Technologies,1993

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