Engineering driven cause-effect modeling and statistical analysis for multi-operational machining process diagnosis

Jian Liu; Jing Li; Jianjun Shi

Engineering driven cause-effect modeling and statistical analysis for multi-operational machining process diagnosis

Jian Liu, Jing Li, Jianjun Shi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Process fault identification for product quality improvement is a critical issue in both design and manufacturing, especially for multistage manufacturing processes. In this paper, an integrated approach is proposed to develop cause-effect models from engineering knowledge and to conduct associated statistical analysis of the measurement data. First, a cause-effect diagram and predicted symptom vectors (PSV) are formulated to recognize the cause-effect relationship between process variables and product qualities. Then factor analysis and factor rotating technique are employed to extract the symptoms reflected from measurement data. Finally the potential process faults are identified by comparing predicted symptoms and extracted symptoms. A case study is conducted to demonstrate the effectiveness of the proposed methodology.

Original language	English (US)
Title of host publication	Transactions of the North American Manufacturing Research Institute of SME
Pages	65-72
Number of pages	8
Volume	33
State	Published - 2005
Externally published	Yes
Event	North American Manufacturing Research Conference, NAMRC 33 - New York, NY, United States Duration: May 24 2005 → May 27 2005

Other

Other	North American Manufacturing Research Conference, NAMRC 33
Country/Territory	United States
City	New York, NY
Period	5/24/05 → 5/27/05

Keywords

Cause-effect relationship
Multistage manufacturing process
Predicted symptom vector
Root cause diagnosis

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Engineering driven cause-effect modeling and statistical analysis for multi-operational machining process diagnosis",

abstract = "Process fault identification for product quality improvement is a critical issue in both design and manufacturing, especially for multistage manufacturing processes. In this paper, an integrated approach is proposed to develop cause-effect models from engineering knowledge and to conduct associated statistical analysis of the measurement data. First, a cause-effect diagram and predicted symptom vectors (PSV) are formulated to recognize the cause-effect relationship between process variables and product qualities. Then factor analysis and factor rotating technique are employed to extract the symptoms reflected from measurement data. Finally the potential process faults are identified by comparing predicted symptoms and extracted symptoms. A case study is conducted to demonstrate the effectiveness of the proposed methodology.",

keywords = "Cause-effect relationship, Multistage manufacturing process, Predicted symptom vector, Root cause diagnosis",

author = "Jian Liu and Jing Li and Jianjun Shi",

year = "2005",

language = "English (US)",

volume = "33",

pages = "65--72",

booktitle = "Transactions of the North American Manufacturing Research Institute of SME",

note = "North American Manufacturing Research Conference, NAMRC 33 ; Conference date: 24-05-2005 Through 27-05-2005",

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TY - GEN

T1 - Engineering driven cause-effect modeling and statistical analysis for multi-operational machining process diagnosis

AU - Liu, Jian

AU - Li, Jing

AU - Shi, Jianjun

PY - 2005

Y1 - 2005

N2 - Process fault identification for product quality improvement is a critical issue in both design and manufacturing, especially for multistage manufacturing processes. In this paper, an integrated approach is proposed to develop cause-effect models from engineering knowledge and to conduct associated statistical analysis of the measurement data. First, a cause-effect diagram and predicted symptom vectors (PSV) are formulated to recognize the cause-effect relationship between process variables and product qualities. Then factor analysis and factor rotating technique are employed to extract the symptoms reflected from measurement data. Finally the potential process faults are identified by comparing predicted symptoms and extracted symptoms. A case study is conducted to demonstrate the effectiveness of the proposed methodology.

AB - Process fault identification for product quality improvement is a critical issue in both design and manufacturing, especially for multistage manufacturing processes. In this paper, an integrated approach is proposed to develop cause-effect models from engineering knowledge and to conduct associated statistical analysis of the measurement data. First, a cause-effect diagram and predicted symptom vectors (PSV) are formulated to recognize the cause-effect relationship between process variables and product qualities. Then factor analysis and factor rotating technique are employed to extract the symptoms reflected from measurement data. Finally the potential process faults are identified by comparing predicted symptoms and extracted symptoms. A case study is conducted to demonstrate the effectiveness of the proposed methodology.

KW - Cause-effect relationship

KW - Multistage manufacturing process

KW - Predicted symptom vector

KW - Root cause diagnosis

UR - http://www.scopus.com/inward/record.url?scp=32844462556&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=32844462556&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:32844462556

VL - 33

SP - 65

EP - 72

BT - Transactions of the North American Manufacturing Research Institute of SME

T2 - North American Manufacturing Research Conference, NAMRC 33

Y2 - 24 May 2005 through 27 May 2005

ER -

Engineering driven cause-effect modeling and statistical analysis for multi-operational machining process diagnosis

Abstract

Other

Keywords

ASJC Scopus subject areas

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