A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

Ceglarek, Darek; Shi, Jianjun; Wu, S. M.

doi:10.1115/1.2902133

Cited by 112 publications

(44 citation statements)

References 8 publications

(8 reference statements)

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“…Since the design task function is an implicit function, the partial derivatives are resolved as in Eq. (6), where ''f T,k,i '' (f T,k,t ) represents the partial derivative of the design function task ''f T,k '' with respect to the ith (tth) design parameter. …”

Section: Methodsmentioning

confidence: 99%

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Hierarchical synthesis of multi-level design parameters in assembly system

Franciosa

Ceglarek

2015

CIRP Annals

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Section: Methodsmentioning

confidence: 99%

“…It has been reported that only 60-70% of Right First Time (RFT) is reached during the design stage [5,6]. Failures that are not predicted during the design phase can appear during ramp-up, which in turn, require engineering changes thus, leading not only to significant cost increase but also trigger delays in the launch of a new product.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical synthesis of multi-level design parameters in assembly system

Franciosa

Ceglarek

2015

CIRP Annals

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“…The methodologies developed so far are based on measuring the parameters throughout the process and analyzing them for detecting the process deviations and identifying the root causes of the process anomalies. Such methods can be divided into two groups: (1) methods established based on the modeling of process variation propagation [10][11][12], and (2) methods based on Statistical Process Control (SPC) [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Ardakani

Lee

2018

Machines

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Abstract:For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products' manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality.

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“…China E-mail: huawang@sjtu.edu.cn Tel: +86- (OCMM) have recently become popular. Much research [2, 4,7,8] on OCMM data processing has been conducted. Some main topics of the research are the knowledge-based diagnostic approach [3], correlation and principal component analysis [5,6], time series theory [8,10], characteristic detection using wavelets [12], fixture failure modeling [9,13] and stream-ofvariation theory [11].…”

Section: Introductionmentioning

confidence: 99%

Periodic trend detection from CMM data based on the continuous wavelet transform

Wang

Chen

Zhu

et al. 2005

Int J Adv Manuf Technol

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The main goal of this paper is to describe a method based on the continuous wavelet transform (CWT). The Shannon entropy of CWT coefficients in each scale is calculated in order to detect the characteristic trends of the CMM measurement data efficiently. This method is successfully implemented in a bodyin-white (BIW) assembly for the detection of periodic trends in coordinate measure machine (CMM) data. It would contribute to main cause location in BIW quality control. The principle of the CWT and the property of the periodic trend after the CWT are explained from a mathematical viewpoint. An actual qualitycontrol case is analyzed using the CWT method. Consistency between the results and the actual situation proves the effectiveness of this CWT method.

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A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

Cited by 112 publications

References 8 publications

Hierarchical synthesis of multi-level design parameters in assembly system

Hierarchical synthesis of multi-level design parameters in assembly system

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Periodic trend detection from CMM data based on the continuous wavelet transform

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