Honolulu, Hawaii
June 24, 2007
June 24, 2007
June 27, 2007
2153-5965
Manufacturing
21
12.1279.1 - 12.1279.21
10.18260/1-2--1810
https://peer.asee.org/1810
698
KURT A ROSENTRATER is a Lead Scientist with the United States Department of Agriculture, Agriculture Research Service, in Brookings, SD, where he is spearheading a new initiative to develop value-added uses for residue streams resulting from biofuel manufacturing operations. He is formerly an assistant professor at Northern Illinois University, DeKalb, IL, in the Department of Technology. He received the Faculty of the Year award in 2002 sponsored by the NIU College of Engineering and Engineering Technology.
JERRY VISSER is Operations Manager of the Product Development Center at South Dakota State University in Brookings, SD, where he brings conceptual ideas to tangible products. He serves as a faculty member for the Manufacturing Engineering Technology Program. He leads the American Society for Quality as Chair of the Southeast South Dakota Sub-section.
Simulation as a Means to Infuse Manufacturing Education with Statistics and DOE – A Case Study using Injection Molding
Abstract
Modern manufacturing systems continue to evolve and in so doing can produce many unique products using both traditional as well as novel raw materials. This is especially true in the processing of plastic products. In these environments, there is the need to critically examine material compatibility and to optimize methods of manufacture to realize economic success. Key to these endeavors is the ability to conduct product development efforts in a logical fashion. Experimentation is an important component to this process. Graduates of manufacturing engineering and technology programs should thus have knowledge of formal Design of Experiments (DOE) and statistical procedures. But, most undergraduate students are not exposed to these methodologies – only in graduate level statistics classes do engineering and technology students typically receive this type of training. Moreover, implementing formal, hands-on experiments can be problematic in many undergraduate curricula because they can be extremely time and resource consuming. Computer simulation can be one way to effectively implement and achieve these objectives, though. The goal of this paper is to describe how to use simulation software to conduct formal experiments using dedicated injection molding software. This paper will discuss several key topics, including a brief introduction regarding the teaching of statistics and DOE to engineering and technology students, as well as injection molding, a common manufacturing unit operation. An example simulation exercise will then be presented to illustrate the concepts discussed. Educators in manufacturing programs should find this useful as they consider how best to augment laboratory work, student understanding of statistics, as well as to achieve proficiency with computer simulation, as this approach to laboratory experiences transcends injection molding specifically, and has a wide range of applicability with many manufacturing operations.
Introduction
As evidenced by the many presentations at annual ASEE national and regional meetings, educators are constantly developing and implementing improved curricula to meet emerging challenges in the various fields of engineering and technology. Some of these activities encompass developing novel subject matter. Many of these endeavors, however, include teaching fundamental, traditional topics using new methods, approaches, and strategies.
Statistics is a skill that is essential for all engineering and technology professionals, but has not been overly emphasized over the years. Many graduates will frequently need to use these tools once they enter the workforce. This is especially true for those involved in research and development as well as testing and validation activities. Basic and applied statistics is key to analyzing laboratory studies, deciphering what the data mean, and discerning trends and patterns1. Even so, the teaching of statistics to engineers has been the subject of only a few studies in recent years2-4. Essential statistics topics should include independent and dependent variables, factorial experimental designs and coding schemes, summarizing collected data with estimates of central tendency (i.e., means) and estimates of observed error (i.e., standard
Rosentrater, K., & Visser, J. (2007, June), Simulation As A Means To Infuse Manufacturing Education With Statistics And Doe – A Case Study Using Injection Molding Paper presented at 2007 Annual Conference & Exposition, Honolulu, Hawaii. 10.18260/1-2--1810
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