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IIE Transactions Volume 38, 2006 - Issue 12
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Original Articles

A data mining approach to study the significance of nonlinearity in multistation assembly processes

Yuan Ren Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA
,
Yu Ding Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, 77843, USA
&
Shiyu Zhou Department of Industrial and Systems Engineering, University of Wisconsin—Madison, Madison, WI, 53706, USA
Pages 1069-1083 | Received 01 Jun 2005, Accepted 01 Mar 2006, Published online: 23 Nov 2006

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Read on this site (4)

Jianjun Shi. (2023) In-process quality improvement: Concepts, methodologies, and applications. IISE Transactions 55:1, pages 2-21.
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Noa Ruschin-Rimini, Irad Ben-Gal & Oded Maimon. (2013) Fractal geometry statistical process control for non-linear pattern-based processes. IIE Transactions 45:4, pages 355-373.
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Jianjun Shi & Shiyu Zhou. (2009) Quality control and improvement for multistage systems: A survey. IIE Transactions 41:9, pages 744-753.
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Jian Liu, Jianjun Shi & S. Jack Hu. (2009) Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes. IIE Transactions 41:4, pages 323-334.
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Articles from other publishers (2)

Biao Lu, Zhen Chen & Xufeng Zhao. (2021) Data-driven dynamic predictive maintenance for a manufacturing system with quality deterioration and online sensors. Reliability Engineering & System Safety 212, pages 107628.
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A. K. Choudhary, J. A. Harding & M. K. Tiwari. (2008) Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20:5, pages 501-521.
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