Journal Published Online: 19 March 2021
Volume 5, Issue 1

Realization of System Robustness by Clustering to Predict New Product Performance Levels

CODEN: SSMSCY

Abstract

Final test metrics that evaluate product system performance usually depend upon numerous variables, such as dimensions or other characteristics of parts and assemblies. Many product systems are expensively comprised of numerous parts. Therefore, during new product system development, the challenge becomes how to rapidly learn estimated system results from combinations of many variables at the smallest possible sample size to minimize cost and improve product quality. In this work, we introduce a fundamental Vector-Based Clustering technique to predict a cluster range of system test results for comparison to other machine learning techniques in a commercial software tool. This work expands to include two additional techniques that account for significance among many variables. All three of these techniques were tested and compared to the machine learning algorithm from a commercial tool best suited for each training set from a high dimensional open-source data set representative of manufacturing system data. These case study results show improvement in predictive accuracy over many prevalent machine learning techniques at small sample sizes. Furthermore, since a best-suited machine learning technique is selected by trial and error for each training set, the computational time is significantly improved as well.

Author Information

Eddy, Douglas
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA, USA
Krishnamurty, Sundar
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA, USA
Grosse, Ian
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA, USA
Pages: 13
Price: Free
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Details
Stock #: SSMS20200030
ISSN: 2520-6478
DOI: 10.1520/SSMS20200030