The Application of Gage R&R Analysis in s Six Sigma Case of Improving and Optimizing an Automotive Die Casting Product’s Measurement System

<p>With the rapid development of automation technology in automotive manufacturing processes, massive and efficient production is a current trend. Therefore, measurement systems with accurate and automated measuring instruments are sought by automotive companies and suppliers. However, the problematic measuring instruments with unreliable accuracy and stability lead to erroneous measurements and wrong quality decisions that cause manufacturers huge profit losses. An effective method called “measurement system analysis” can be applied to define and eliminate erroneous measurements to ensure adequate reliability. An automotive transmission die casting parts supplier called company T was suffering a serious profit loss due to the erroneous measurements from one type of their product’s measurement system. These erroneous measurements caused the company to deliver nonconforming products to their customers. The researcher conducted a study applying Six Sigma methodology to find out the root cause of the erroneous measurements and eliminate the erroneous measurements to retain adequate reliability. The researcher used DMAIC (Define, Measure, Analysis, Improve, and Control) process as framework to conduct the study and the measurement system analysis, Gage R&R method, to process several experiments for data collection and analysis. Through processing the experiments and analyzing the results, the researcher was able to detect the source of variation and find the root cause that caused the erroneous measurements. Based on the findings, the researcher then corrected the erroneous measurements and improved the problematic measurement system’s performance.</p>

Name thesis:degree_name

Master of Science

Discipline thesis:degree_discipline

Department of Architectural and Manufacturing Sciences

Year

2015

Author dc:creator

• Ren, Qizheng

Contributors dc:contributor

• Mark Doggett (Director), Daniel Jackson, and John Khouryieh

• Industry
• Manufacturing
• Quality
• Engineering
• Management
• Architectural Technology
• Industrial Technology
• Other Operations Research, Systems Engineering and Industrial Engineering

Repository record dc:identifier

https://digitalcommons.wku.edu/theses/1536

OAI identifier oai:identifier

oai:digitalcommons.wku.edu:theses-2543