Research Papers

Big Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy

[+] Author and Article Information
Jeffrey A. Abell

GM Technical Fellow
Global Research and Development,
General Motors,
Warren, MI 38092
e-mail: jeffrey.abell@gm.com

Debejyo Chakraborty

Global Research and Development,
General Motors,
Warren, MI 38092
e-mail: debejyo.chakraborty@gm.com

Carlos A. Escobar

Global Research and Development,
General Motors,
Warren, MI 38092
e-mail: carlos.1.escobar@gm.com

Kee H. Im

Global Research and Development,
General Motors,
Warren, MI 38092
e-mail: kee.im@gm.com

Diana M. Wegner

Global Research and Development,
General Motors,
Warren, MI 38092
e-mail: diana.wegner@gm.com

Michael A. Wincek

Global Research and Development,
General Motors,
Warren, MI 38092
e-mail: mike.wincek@gm.com

1Corresponding author.

Manuscript received January 31, 2017; final manuscript received May 17, 2017; published online August 24, 2017. Assoc. Editor: Ivan Selesnick.

J. Manuf. Sci. Eng 139(10), 101009 (Aug 24, 2017) (12 pages) Paper No: MANU-17-1066; doi: 10.1115/1.4036833 History: Received January 31, 2017; Revised May 17, 2017

Discussion 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.

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Fig. 1

Big data—big models

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Fig. 2

The five V's of big data—big models

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Fig. 4

The bellows chart: a mnemonic for selection in model building

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Fig. 5

Feature selection methods in classification: (a) filter and wrapper methods and (b) embedded methods

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Fig. 6

Ultrasonic welding schematic for battery tabs (see Ref. [9])

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Fig. 7

The first generation Chevrolet Volt battery: (a) cell, (b) module, and (c) graphical rendering of the battery

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Fig. 8

Process monitoring for quality: a blend of process monitoring and quality control: (a) traditional view and (b) updated view

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Fig. 9

Acsensorization of ultrasonic welder and an example of observed signals: (a) some additional sensors to ultrasonic welder, (b) signal from power sensor (high resolution), (c) signal from power sensor (low resolution), (d) signal from microphone, and (e) signal from LVDT

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Fig. 10

Discovering features for big models

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Fig. 11

Multi-objective pareto optimization

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Fig. 12

Software interface to PMQ at Brownstown battery assembly plant

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Fig. 15

Iconic representation of the PMQ philosophy

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Fig. 14

Manual inspection station at Brownstown battery assembly plant (see color figure online)

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Fig. 13

Stratified software architecture used in PMQ

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Fig. 16

Suspect rate over the span of the first year of implementation

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Fig. 18

Quality philosophies: (a) statistical quality control, (b) total quality management, (c) six sigma, and (d) design for six sigma

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Fig. 19

The quality evolutionary trajectory

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Fig. 17

Process monitoring for quality (PMQ): extended view



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