Exploring Career Growth for Deaf and Hard-of-Hearing Individuals via Machining Training: A Comparative Behavioral Analysis
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
- Tagged Division
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Manufacturing Division (MFG)
- Tagged Topic
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Diversity
- Permanent URL
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https://peer.asee.org/47399
Paper Authors
Krzysztof Kamil Jarosz Rochester Institute of Technology
Yan-Ting Chen Rochester Institute of Technology
Yan-Ting Chen received his Master of Science degree in Electrical and Computer Engineering from Miami University, U.S., in 2018. During the MS program, his research interest was mainly focused on the methods of measuring ambient toxins by developing an array of chemical sensors and analyzing data using machine learning. Now, he is currently pursuing a degree in Mechanical Engineering Ph.D., Rochester Institute of Technology, U.S. As a Ph.D. student, he works in the Smart Manufacturing Research Group (SMRG) lab, under the leadership of Dr. Rui Liu. The research is about investigating human factors-related issues in the manufacturing field including the analysis of various of human perception models and behavioral patterns, particularly the analysis of human visualization perception and the cognitive process of drawing reading and conceptual design which are potentially crucial in the development of the modern machining industry today.
Trisha Gard-Thompson
Rochester Institute of Technology
orcid.org/0009-0008-6232-3063
Trisha she is a Deaf engineer with 11 years of experience manufacturing engineering in HVAC and Steel Mill. Trisha is currently a Lecturer in the Engineering Studies at Rochester Institute of Technology. She is currently pursuing a Master’s in Manufacturing and Mechanical System Integration at RIT.
Mark Davis Rochester Institute of Technology
Yunbo Zhang Rochester Institute of Technology
Dr. Yunbo Zhang is currently an Assistant Professor in Department of Industrial & Systems Engineering at Rochester Institute of Technology (RIT). Dr. Zhang’s research focuses on investigating computational methods for advancing design and manufacturing
Rui Liu Rochester Institute of Technology
Dr. Rui Liu is currently an Assistant Professor in the Mechanical Engineering Department at Rochester Institute of Technology. He received his B.S. degree in Jet Propulsion at Beijing University of Aeronautics and Astronautics, China, in 2005. In 2010, he received his M.S. in Mechanical Engineering at Northeastern University. In 2014, he completed his Ph.D. in Mechanical Engineering at Georgia Institute of Technology. Dr. Rui Liu’s research areas cover a wide range of topics in advanced manufacturing, including human-centered machining, tool condition monitoring, and machining process optimization for various kinds of machining processes.
Abstract
Overcoming challenges and transitioning from school to work is particularly problematic for individuals who are deaf or hard of hearing, presenting significant issues for both the labor market and vocational training institutions. Due to the lack of research addressing the career maturity and distinctive obstacles faced by this population, this paper endeavors to investigate performance disparities within the machining field. The specific focus is on assessing whether hearing loss may impact students' machining performance. Considering the essential human capabilities for perception in machining, especially in industrial settings, encompass a range of faculties including visualization, hearing, and tactile senses. Thus, addressing concerns related to accommodating individuals with disabilities is important, prompting inquiries into optimizing training programs and quantifying potential disparities in learning or schooling outcomes, behavioral patterns, and overall performance in future careers. The conducted studies involved multiple participants, including hearing, deaf, and hard-of-hearing students with various machining training backgrounds. The investigation will delve into data concerning the qualities of manual machining outputs and the subject’s self-rating feedback. The outcomes from this study are expected not only to allow to obtain more insights into human behavior in machining operations, but also to identify key differences between machinist trainees who exhibit no underlying hearing problems and ones who are deaf/hard of hearing. The findings of this work provide valuable takeaways concerning machinists with hearing loss, revealing little to no effect of hearing loss on trainee performance, alleviating concerns about potential performance weaknesses. The outcomes from this study have shown that trainee experience seems to relate directly to machining proficiency, regardless of hearing loss.
Jarosz, K. K., & Chen, Y., & Gard-Thompson, T., & Davis, M., & Zhang, Y., & Liu, R. (2024, June), Exploring Career Growth for Deaf and Hard-of-Hearing Individuals via Machining Training: A Comparative Behavioral Analysis Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47399
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