Portland, Oregon
June 23, 2024
June 23, 2024
June 26, 2024
Manufacturing Division (MFG)
16
10.18260/1-2--48079
https://peer.asee.org/48079
64
PROFESSOR AT LONE STAR COLLEGE. TEACHING AND Research AT ENERGY AND MANUFACTURING INSTITUTE OF LONE STAR COLLEGE SYSTEMS. RESEARCH AND DEVELOPMENT IN THE FIELD OF APPLIED TECHNOLOGY.DESIGN AND IMPLEMENTED COURSES FOR THE STUDENTS..
Dr. Sheng-Jen ("Tony") Hsieh is a Professor in the Department of Engineering Technology and Industrial Distribution and a member of the Graduate Faculty at Texas A&M University, College Station, TX. His research interests include automation, robotics, cyber-manufacturing and Industry 4.0; optical/infrared imaging and instrumentation; micro/nano manufacturing; and design of technology for engineering education. He is also the Director of the Rockwell Automation Laboratory at Texas A&M University, a state-of-the-art facility for education and research in the areas of automation, robotics, and Industry 4.0 systems. He was named Honorary International Chair Professor for National Taipei University of Technology in Taipei, Taiwan, for 2015-23. Dr. Hsieh received his Ph.D. in Industrial Engineering from Texas Tech University, Lubbock, TX.
Technology is changing at a much faster rate than ever. We call this the fourth industrial revolution (Industry 4.0). In the authors’ community colleges and workforce development programs, instructors focus on hands-on learning for high-level courses, including machine vision and capstone courses. Often the learning experience is hindered by lack of resources. To introduce Industry 4.0 concepts to students, a low-cost automated system for sorting candy that uses a portable gantry robotic system with machine vision was developed. This system makes Industry 4.0 concepts—such as Internet of things, smart manufacturing, cloud based manufacturing, and industrial Internet—more tangible and applicable to our courses. Existing work on candy sorting machines can be broadly divided into two categories: optical sorting and mechanical sorting. Optical sorting machines use camera and machine vision algorithms to identify and sort candies by color, shape, size, and flavors; these are typically very fast and accurate. Mechanical sorting machines use a physical mechanism and gates with color sensors to do the sorting; these are typically slower rate and less accurate. The objectives of the work described in this paper are to 1) develop a low-cost portable gantry robotic system with machine vision; 2) design lesson plans and activities for advanced programing and machine learning subjects and outreach to high schools; and 3) evaluate the impact of the system and lesson plans and make suggestions for future improvements. Initial evaluation results suggest that the system and lesson plans have a positive impact on student learning in advanced manufacturing and machine learning. Future work includes using the system for outreach to local high school faculty, investigating which subjects the system can be used to teach, and using the system to help introduce project-based learning in dual credit courses by conducting workshops with high schools and college instructors.
Siddiqi, J. S., & Gandy, A. S., & Hsieh, S. (2024, June), Technical Training for Industry 4.0 Technologies: Low-Cost Gantry Candy Sorting System for Education and Outreach Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--48079
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