Intelligent Welding: Real Time Monitoring, Diagnosis, Decision and Control Using Multi-Sensor and Machine Learning
Abstract
In this project, the industry problem of real-time weld quality assurance is studied. An automated weld quality assurance can increase the efficiency and the productivity of weld manufacturing. In order to ensure an adequate weld quality, the selection of proper evaluation approaches is critical. Currently, inspections are usually conducted either destructively or in the post-weld stage. Thus, if defects are found in welded product, few of them can be remedied. This may result in the disposal of expensive material, thus decreasing overall productivity. Therefore, an efficient nondestructive weld quality monitoring method is critically needed.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 31, 2018
- Accession Number
- AD1090879
Entities
People
- Didem Ozevin
Organizations
- University of Illinois at Chicago