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An Effective Learning Approach for Industrial Robot Programming

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2013 ASEE Annual Conference & Exposition


Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013



Conference Session

Simulation and Programming

Tagged Division


Page Count


Page Numbers

23.159.1 - 23.159.14



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Paper Authors


Guanghsu A. Chang Western Carolina University

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Dr. Guanghsu A. Chang is currently an associate professor of the Engineering and Technology Department at Western Carolina University. He has spent the last 21 years in teaching industrial and manufacturing engineering programs. His research interests involve the study of robotic applications, manufacturing automation, Design for Assembly (DFA), and Case-Based Reasoning (CBR) applications. He was a vice president of Southern Minnesota APICS (2009-2012). He holds both MSIE, and Ph.D. degrees from the University of Texas at Arlington.

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Wesley L. Stone Western Carolina University

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Dr. Wes Stone is an associate professor in the Department of Engineering and Technology at Western Carolina University in Cullowhee, NC. He earned his bachelor's degree from the University of Texas at Austin, master's degree from Penn State, and Ph.D. from Georgia Tech, all in Mechanical Engineering. His research interests include manufacturing processes and quality techniques. He also serves as the Engineering Technology program director at WCU.

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An Effective Learning Approach for Industrial Robot ProgrammingProgramming an industrial robot by using the teach pendent is a tedious and time-consumingtask that requires a considerable amount of work-related skills, robotics knowledge andexperience. Industrial robots also require a tremendous amount of programming skills andinput/output controls to make them useful. Obviously, a good robot programmer is a key factorof successful robot applications. In order to teach manufacturing engineering technology (MET)students to program industrial robots, we propose an effective learning approach for industrialrobot programming in our curriculum. Research indicates that the use of off-line programming(OLP) method for learning industrial robot programming has a positive impact on reducing therobotics lab programming time (Ex. only two robots are available for 20 students), reducing thedowntime of equipment when programming new workpieces/variants, and acceleratingprogramming complex paths. This paper describes the development of off-line programmingmethod to help students learn industrial robot programming. The off-line programming methodis based on examples from industry and illustrates several good robot program designs. Overall,The OLP method provides not only our students an excellent learning environment but also apowerful teaching tool for MET instructors. Our results indicate that the students have thefollowing competence to: 1) study multiple scenarios of a robotic workcell before any decision iscommitted, 2) determine the cycle time for a sequence of manufacturing operations, 3) Uselibraries of pre-defined high-level commands for certain types of robotic applications, 4)minimize production interruption and help meet flexible automation goals, and 5) ensure that arobotic system will do the functions that an end-user needs it to do. We also recognize that thestudents who understand both robotics hardware and offline programming software incombination is a challenge for many other colleges and universities. Not many students areproficient at both, but our students are.

Chang, G. A., & Stone, W. L. (2013, June), An Effective Learning Approach for Industrial Robot Programming Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--19173

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