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contributor authorAbell, Jeffrey A.
contributor authorChakraborty, Debejyo
contributor authorEscobar, Carlos A.
contributor authorIm, Kee H.
contributor authorWegner, Diana M.
contributor authorWincek, Michael A.
date accessioned2017-11-25T07:17:56Z
date available2017-11-25T07:17:56Z
date copyright2017/24/8
date issued2017
identifier issn1087-1357
identifier othermanu_139_10_101009.pdf
identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4234850
description abstractDiscussion of big data (BD) has been about data, software, and methods with an emphasis on retail and personalization of services and products. Big data also has impacted engineering and manufacturing and has resulted in better and more efficient manufacturing operations, improved quality, and more personalized products. A less apparent effect is that big data have changed problem solving: the problems we choose to solve, the strategy we seek, and the tools we employ. This paper illustrates this point by showing how the big data style of thinking enabled the development of a new quality assurance philosophy called process monitoring for quality (PMQ). PMQ is a blend of process monitoring and quality control (QC) that is founded on big data and big model (BDBM), which are catalysts for the next step in the evolution of the quality movement. Process monitoring (PM) for quality was used to evaluate the performance of the ultrasonically welded battery tabs in the new Chevrolet Volt, an extended range electric vehicle.
publisherThe American Society of Mechanical Engineers (ASME)
titleBig Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy
typeJournal Paper
journal volume139
journal issue10
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4036833
journal fristpage101009
journal lastpage101009-12
treeJournal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 010
contenttypeFulltext


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