Scaffolding Training on Digital Manufacturing: Prepare for the Workforce 4.0

Rui Li; Victoria Bill; Jack Bringardner

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Mechanical Engineering Division (MECH) Technical Session 15: Automation and Machine Learning
Tagged Division: Mechanical Engineering Division (MECH)
Page Count: 8
DOI: 10.18260/1-2--44183
Permanent URL: https://peer.asee.org/44183
Download Count: 219

Paper Authors

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Rui Li New York University

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Dr. Rui Li earned his Master's degree in Chemical Engineering in 2009 from Imperial College of London and his Ph.D in Electrical and Computer Engineering in 2020 from the University of Georgia, College of Engineering. He is currently an industrial assistant professor, who works in General Engineering program at New York University. He taught first-year engineering course as well as vertically integrated project. He has strong interests in educational robotics, project-based learning and first-year STEM education.

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Victoria Bill New York University

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Victoria Bill is the Director of the MakerSpace Lab and an Adjunct Professor in the First-Year Engineering Program at NYU Tandon School of Engineering. She studied electrical engineering and received her B.S. from the Ohio State University and her M.S. from the University of Texas at Austin. She is currently pursuing her PhD in Engineering Education from the Ohio State University.

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Jack Bringardner New York University orcid.org/0000-0002-5980-384X

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Jack Bringardner is the Assistant Dean for Academic and Curricular Affairs at NYU Tandon School of Engineering. He is an Industry Associate Professor and Director of the General Engineering Program. He teaches the first-year engineering course Introduction to Engineering and Design. He is also the Director of the Vertically Integrated Projects Program at NYU. His Vertically Integrated Projects course is on the future of engineering education. His primary focus is developing curriculum, mentoring students, and engineering education research, particularly for project-based curriculum, first-year engineering, and student success. He is active in the American Society for Engineering Education and is the NYU ASEE Campus Representative. He serves on the First-Year Programs Division Executive Board as well as the Webmaster for the ASEE First-Year Programs Division and the First-Year Engineering Experience Conference. He is affiliated with the NYU Civil and Urban Engineering Department and advisor for NYU student chapter of the Institute for Transportation Engineers.

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Abstract

In this Work-in-Progress paper, a scaffolding training for Workforce 4.0 was described. The onset of Industry 4.0, sometimes known as the fourth industrial revolution, will add new challenges to the shortage of skilled labor, such as CNC programmers and machinists. Like any new technology, new job categories are emerging that require new skill sets, presumably not replacing the current workforce but rather reinventing it. Some projections claim that between 75 and 375 million workers globally may need to change their occupational categories by 2030 due to a sizable amount of employment being automated or digitized.

The fourth industrial revolution introduces the integration of digital technologies into the manufacturing process to increase productivity and efficiency. The phrase "digital manufacturing technologies" (DMTs) describes the use of smart, digital, autonomous, and intelligent technologies, including sensor, cloud, distributed, and additive manufacturing, in modern industry. This new wave of industrialization is anticipated to improve the quality of work by fostering an environment that gives workers more autonomy for self-development and problem-solving. The workers are expected to make strategic decisions and find adaptable solutions to engineering problems in a timely manner. For example, in an automated system involving industrial robots, Workforce 4.0, a new breed of skilled workers can play a more creative and active role.

Within a vertically integrated project program of a large private university, a systematic training scheme was developed for training undergraduate students with the xArm educational robot, as mentioned in our previous ASEE publication. The goal of the training is to lay the technical foundations for undergraduate students who have no experience in robotics for their future careers as Workforce 4.0. By the end of the training, the students should be ready to solve open-ended problems in automated production lines.

The overall training lasts 12 weeks in total. 15 students participate in the training. The training scheme has been divided into two major blocks: the first block is the foundational training, and the second block is the advanced training. In the foundational training, the first week is to understand fundamentals by reviewing at least five research papers. The second week is to work on the mechanical assembly of the xArm robots. Robotic kinematics is introduced from the third to the fifth week. In the advanced training, the students were then divided into two specialized groups based on their own interests: Computer Vision (CV) and Natural Language Processing (NLP). There is a seminar about the Robotic Operation System (ROS). The final week is to assess training outcomes. Collaborative teams are formed to build a mini version of a production line using xArm robots, a conveyor belt, and selected sensors. An end-of-course learning assessment survey indicated that students self-reported improved understanding of the course topics.

Citation
Format

Li, R., & Bill, V., & Bringardner, J. (2023, June), Scaffolding Training on Digital Manufacturing: Prepare for the Workforce 4.0 Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44183

TY - CPAPER
AB - In this Work-in-Progress paper, a scaffolding training for Workforce 4.0 was described. The onset of Industry 4.0, sometimes known as the fourth industrial revolution, will add new challenges to the shortage of skilled labor, such as CNC programmers and machinists. Like any new technology, new job categories are emerging that require new skill sets, presumably not replacing the current workforce but rather reinventing it. Some projections claim that between 75 and 375 million workers globally may need to change their occupational categories by 2030 due to a sizable amount of employment being automated or digitized.

The fourth industrial revolution introduces the integration of digital technologies into the manufacturing process to increase productivity and efficiency. The phrase &quot;digital manufacturing technologies&quot; (DMTs) describes the use of smart, digital, autonomous, and intelligent technologies, including sensor, cloud, distributed, and additive manufacturing, in modern industry. This new wave of industrialization is anticipated to improve the quality of work by fostering an environment that gives workers more autonomy for self-development and problem-solving. The workers are expected to make strategic decisions and find adaptable solutions to engineering problems in a timely manner. For example, in an automated system involving industrial robots, Workforce 4.0, a new breed of skilled workers can play a more creative and active role.

Within a vertically integrated project program of a large private university, a systematic training scheme was developed for training undergraduate students with the xArm educational robot, as mentioned in our previous ASEE publication. The goal of the training is to lay the technical foundations for undergraduate students who have no experience in robotics for their future careers as Workforce 4.0. By the end of the training, the students should be ready to solve open-ended problems in automated production lines.

The overall training lasts 12 weeks in total. 15 students participate in the training. The training scheme has been divided into two major blocks: the first block is the foundational training, and the second block is the advanced training. In the foundational training, the first week is to understand fundamentals by reviewing at least five research papers. The second week is to work on the mechanical assembly of the xArm robots. Robotic kinematics is introduced from the third to the fifth week. In the advanced training, the students were then divided into two specialized groups based on their own interests: Computer Vision (CV) and Natural Language Processing (NLP). There is a seminar about the Robotic Operation System (ROS). The final week is to assess training outcomes. Collaborative teams are formed to build a mini version of a production line using xArm robots, a conveyor belt, and selected sensors. An end-of-course learning assessment survey indicated that students self-reported improved understanding of the course topics.

AU - Rui Li
AU - Victoria Bill
AU - Jack Bringardner
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Scaffolding Training on Digital Manufacturing: Prepare for the Workforce 4.0
UR - https://peer.asee.org/44183
DO - 10.18260/1-2--44183
ER -