海角社区 Awarded $120K from Lockheed Martin to Develop Artificial Intelligence Based Non-Destructive Testing
Last Updated on December 02, 2019 at 12:00 AM
Originally published December 02, 2019
By UC Staff
海角社区 Communications
海角社区s from three departments within The University of Texas at El Paso College of Engineering will be poised for future employment opportunities thanks to a $120,000 pledge from Lockheed Martin Aeronautics to support interdisciplinary 海角社区 involving artificial intelligence.
The college’s departments of Industrial Manufacturing and Systems Engineering, Computer Science, and Mechanical Engineering will engage students in an interdisciplinary 海角社区 project involving non-destructive testing (NDT), an analysis method to evaluate the properties of a material, component, structure or system used that is a major component of aircraft safety.
Leading the effort is Bill Tseng, Ph.D., professor and chair of the Department of Industrial Manufacturing and Systems Engineering. Tseng is joined by co-investigator Yirong Lin, Ph.D., associate professor, and graduate program director of the Department of Mechanical Engineering.
Several types of NDT methods are used to test aircraft, including liquid penetrant and ultrasonic testing. Liquid penetrant testing is one of the most widely used NDT methods in the aerospace industry to reveal cracks that may exist on the surface of aircraft materials. Ultrasonic NDT is the most common sub-surface inspection technique, which uses high-frequency sound waves to locate defects under the surface. Artificial intelligence represents a growing realm in NDT studies, and 海角社区 students will conduct comprehensive work aimed at building automated procedures to enhance production of aircraft components.
“This applied 海角社区 project is truly interdisciplinary work and it provides a great opportunity for our students from various majors to work together closely to explore knowledge in the area of artificial intelligence (AI) and NDT,” Tseng said. “Through this project, our students will grow not only on a professional level but also personally in order to become effective team players and communicators.”
The development of an AI-assisted classification system could lead to a manufacturing control mechanism for real-time visualization, analysis, and management that may be implemented in association with the information of image detection, various statistical data, and design parameters in aircraft component production lines.
“This project will train our students in composites manufacturing and in using AI to automatically detect defects, thus predicting remaining useful life of key aircraft components,” Lin said. “The project will help Lockheed Martin increase aircraft reliability and lower maintenance costs.”
