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Enhancing Manufacturing Process Education via Computer Simulation and Visualization
Author(s) -
Priyadarshan Manohar,
Sushil Acharya,
Peter Wu
Publication year - 2014
Publication title -
journal of education and learning
Language(s) - English
Resource type - Journals
eISSN - 1927-5269
pISSN - 1927-5250
DOI - 10.5539/jel.v3n3p172
Subject(s) - visualization , process (computing) , computer science , schedule , manufacturing engineering , industrial engineering , engineering drawing , engineering , artificial intelligence , operating system
Industrially significant metal manufacturing processes such as melting, casting, rolling, forging, machining, and forming are multi-stage, complex processes that are labor, time, and capital intensive. Academic research develops mathematical modeling of these processes that provide a theoretical framework for understanding the process variables and their effects on productivity and quality. However it is usually difficult to provide the students with hands-on experience of experimentation with process parameters which leads to disconnect between engineering education and industrial needs. In order to solve this problem, interdisciplinary student projects were undertaken at author’s institution to develop computer simulation tools that would facilitate process visualization, experimentation, exploration, design and optimization. The hypothesis is that these new computer-based tools would enhance educational experience for the manufacturing engineering students as assessed by the ABET-derived educational outcomes and also based on Bloom’s cognitive outcomes modified for STEM disciplines. The first system described in this paper is the visualization of metal ingot production schedule in an industrial setting that provides a basis for interactive decisions. The graphical user interface is created to visualize the schedule according to the specific characteristics of the machines. Another example of process simulation presented in this paper is the design and analysis of flexible rolling technology in industrial processing of low carbon steels. Process simulation tools designed in both cases allow new process sequences to be generated by breaking down existing process routes into key elements and then by recombining them to generate novel alternative and more efficient hot processing sequences. This enables the identification of an optimal process sequence for specified steel compositions that also satisfies simultaneous design criteria such as process feasibility and property maximization. It is proposed that incorporation of such computer simulation tools in the pedagogy would be highly effective to enhancing and enriching undergraduate manufacturing education.

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