The Repository @ St. Cloud State

Open Access Knowledge and Scholarship

Date of Award


Culminating Project Type

Starred Paper

Degree Name

Engineering: M.E.M


Mechanical and Manufacturing Engineering


College of Science and Engineering

First Advisor

Ben Beliga

Second Advisor

Hiral Shah

Third Advisor

Balsy Kasi

Keywords and Subject Headings

Data driven analysis, inspection demand, quality department


There are always fluctuations in production systems. When a quality department is given the task to gear up to inspect a brand new part, many items must be taken into consideration. There are four areas that were analyzed to determine the best way to get the new part measured in the inspection department. First, the option of buying more inspection equipment was evaluated. Second, a subcontracted inspection company inspected the part and the resulting data was compared to inspection data that had been measured at MTMN. The resulting analysis determined that there were some differences in the outcomes of the data and more evaluation will have to be done in order to have confidence in the subcontracted company’s measured data. Third, the idea of using the part supplier’s data to evaluate the part is discussed. The data was determined to be unusable because the length and width measurements are missing location data. Fourth, the subject of overtime was looked at to see if using the department’s limited manpower can be an answer to an increase in inspection demand. The required 12 hours of additional time needed would be somewhat taxing on personnel. A summary of these four ideas will be detailed at the end of this report.


I would like to thank the following people for helping me make this project possible. First I would like to thank Matt Rindal and Chris Carlson at Advanced Inspection Systems for all of their help in gathering critical inspection data on the ceramic parts that I brought to them for measurement. I would like to thank Travis Buckingham from Keyence Corporation of America. Travis was very helpful in my comparative analysis of vision system machines, specifically the Keyence machine. I also would like to thank Brian Gracek. Brian was kind enough to walk me through all the statistical parts of this capstone and was not only my professor but was an excellent resource throughout my time in the Masters of Engineering Management program at St. Cloud State. Finally I would like to thank Multitest Minnesota (MTMN) for allowing me the opportunity to perform this study. Thank you everyone.



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