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Article
Publication date: 25 July 2019

Yinhua Liu, Rui Sun and Sun Jin

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality…

Abstract

Purpose

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

Design/methodology/approach

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

Findings

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

Originality/value

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 27 June 2019

Yinhua Liu, Shiming Zhang and Guoping Chu

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship…

Abstract

Purpose

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Design/methodology/approach

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

Findings

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

Originality/value

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 7 September 2015

Yinhua Liu, Xialiang Ye, Feixiang Ji and Sun Jin

– This paper aims to provide a new dynamic modeling approach for root cause detection of the auto-body assembly variation.

Abstract

Purpose

This paper aims to provide a new dynamic modeling approach for root cause detection of the auto-body assembly variation.

Design/methodology/approach

The dynamic characteristics, such as fixture element wear and quality of incoming parts, are considered in assembly variation modeling with the dynamic Bayesian network. Based on the network structure mapping, the parameter learning of different types of nodes is conducted by integrating process knowledge and Monte Carlo simulation. The inference was that both the measurement data and maintenance actions are evidence for the improvement of diagnosis accuracy.

Findings

The proposed assembly variation model which has incorporated dynamic manufacturing features could be used to detect multiple process faults effectively.

Originality/value

A dynamic variation modeling method is proposed. This method could be used to provide more accurate diagnosis results and preventive maintenance guidelines for the assembly process.

Details

Assembly Automation, vol. 35 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 25 September 2009

Sun Jin, Kuigang Yu, Xinmin Lai and Yinhua Liu

The purpose of this paper is to focus on optimal sensor placement for the fixture variation diagnosis of compliant sheet metal assembly process. Fixture variations are the…

Abstract

Purpose

The purpose of this paper is to focus on optimal sensor placement for the fixture variation diagnosis of compliant sheet metal assembly process. Fixture variations are the main sources for complex automotive body dimensional failures. An effective measurement strategy can help exactly and timely diagnose these fixture variations. Research on sensor placement strategy of compliant sheet metal assembly process is not much stated formerly.

Design/methodology/approach

The impact principle of fixture variations is analyzed to set up the relationship between the assembly variation and fixture variations applying the method of influence coefficients and the effective independence (EI) method is used to find the optimal sensor positions based on the impact principle analysis of fixture variations.

Findings

The obtained fixture variation sensitivity matrix describes the influence of fixture variations to compliant sheet metal assembly variation and can be used for diagnosing fixture variations. The EI method can effectively solve the optimal sensor positions for compliant sheet metal assembly by a case demonstration.

Originality/value

The proposed method can solve the sensor placement of online assembly station for diagnosing fixture variations. It takes the compliant characteristics of sheet metal parts into account and the sensor information has much greater diagnosability than that from applying other methods.

Details

Assembly Automation, vol. 29 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 12 April 2011

Yinhua Liu, Sun Jin, Zhongqin Lin, Cheng Zheng and Kuigang Yu

Fixture failures are the main cause of the dimensional variation in the assembly process. The purpose of this paper is to focus on the optimal sensor placement of…

Abstract

Purpose

Fixture failures are the main cause of the dimensional variation in the assembly process. The purpose of this paper is to focus on the optimal sensor placement of compliant sheet metal parts for the fixture fault diagnosis.

Design/methodology/approach

Based on the initial sensor locations and measurement data in launch time of the assembly process, the Bayesian network approach for fixture fault diagnosis is proposed to construct the diagnostic model. Furthermore, given the desired number of sensors, the diagnostic ability of the sensor set is evaluated based on the mutual information of the nodes. Thereby, a new sensor placement method is put forward and validated with a real automotive sheet metal part.

Findings

The new proposed method can be used to perform the fixture fault diagnosis and sensor placement optimization effectively, especially in a data‐rich environment. And it is robust in the presence of measurement noise.

Originality/value

This paper presents a novel approach for fixture fault diagnosis and optimal sensor placement in the assembly process.

Details

Assembly Automation, vol. 31 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

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