Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

He, Yihai; He, Zhenzhen; Wang, Linbo; Gu, Changchao

doi:10.1155/2015/379098

Cited by 20 publications

(12 citation statements)

References 23 publications

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“…According to equation (17), WIP quality characteristic variation q i (t) at machine i can be characterized to measure the stability of process quality using a given KQC variations vector Y and corresponding contribution index as follows…”

Section: Quantification Of Explicit Risk In Production Task Executionmentioning

confidence: 99%

“…15 From the view of product quality and component reliability, since the quality–reliability (QR) co-effect 16 was proposed, the complex propagation and transmission among the system have been gradually expounded in depth. 17,18 He et al 19 presented an optimal integrated predictive maintenance strategy that combines quality and reliability and minimizes the total cost. On the other hand, considering the production requirement assigned to machines and manufacturing modes, the states of components are dependent and mutually influenced.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Zhao

Liu

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Predictive health analysis and diagnostics are essential functional elements of “Smart Manufacturing,” but existing studies are mostly limited to discrete manufacturing equipment without a holistic analysis on the entire system health state. In consideration of the enhancement of risk control in production quality management advocated in ISO 9001:2015, modeling and monitoring the risk during operation are the core aspects and bottlenecks for achieving prognostics. Therefore, a novel holistic operational risk modeling approach for discrete manufacturing systems is proposed by fusing the operational data to quantify the risk-oriented operational performance systematically. First, the connotation of operational risk is expounded and associated with system operational mechanism and mission reliability connotation. Second, a modeling foundation is proposed on the basis of quantifying machine, task execution and work-in-process quality from mission reliability framework in accordance with the operational data characteristics and properties. Third, operational risk evaluation is conducted using the above indicators, where reasonable aggregation of three indicators is performed by fuzzy evidence theory and the risk levels are determined. Finally, a case study is conducted to illustrate the effectiveness and advantages of the proposed approach.

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Section: Quantification Of Explicit Risk In Production Task Executionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Zhao

Liu

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, most of the current literature considers the adverse effect of assembling variations on assembling process quality only; the impact of assembling process quality on the reliability of the final product is still ignored. 22 For the error compensation of thin-walled workpieces, Si and Wang 23 proposed a research method of error compensation to improve the reliability of thin-walled products. Jiang et al 24 considered the influence of workpiece variation accumulation on the current process.…”

Section: Introductionmentioning

confidence: 99%

Reliability loss–oriented product assembling quality risk modeling approach based on reliability–quality–reliability chain

Zhang

Liu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Assembling variations caused by assembling system reliability degradation are the root causes of poor-assembled product reliability, and the reliability loss is the barometer of assembling quality risks. However, few studies have integrated reliability loss with assembling quality risk analysis. Therefore, a modeling approach of assembling quality risk regarding product reliability loss is expounded in this work. First, the assembling quality risk is divided into systematic, exterior, and interior risks, and a formation mechanism of assembling quality risk is presented. Second, the fusion framework of big operational data in assembling is described, an assembling reliability–quality–reliability chain is established, and the parameters of the chain constitute assembling system reliability ( R), assembling process quality ( Q), and assembled product reliability ( R). Third, on the basis of the reliability–quality–reliability chain, the risk priority number is adopted to quantify the assembling quality risk, which is extended by quantifying the undetectable rate of the assembling system, the occurrence possibility of process variations, and the product reliability loss. Finally, an assembling quality risk of a circuit maintenance cover of a car is conducted to validate the effectiveness and advancement of the presented approach. The result shows that the proposed method can systematically quantify the assembling quality risk.

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“…Also a process reliability assessment with a Bayesian approach which can predict whether the process holds the quality reliability requirements was introduced by Lin [8]. Reliability Modelling and Optimization Strategy for Manufacturing System Based on RQR Chain done by He [9]. Bhamare et al [10] pointed out the evolution of reliability engineering in the last six decades and the various statistical and mathematical models developed during this tenure.…”

Section: Introductionmentioning

confidence: 99%

Effect-Cause Analysis and Prediction Convergence of Random Failure Gate in a Probabilistic Competitive Environment with Case Study on Quality Control Process

Choorikkat¹,

Gajjele²,

Srinivas³

et al. 2020

MMEP

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The world of competition is not always deterministic. Probabilistic approach is more relevant and practical in any competitive environments. The effect of random failure gate which usually comes in the way of anything that promotes the low quality and at the same time denies high quality to come up in a competition and having a probability distribution for an instant of time is discussed in the present article. For this purpose the article introduces a novel methodology using the outcome probability of each rank along with the newly added concept of position ratios with a view to study the possibility of making proper predictions in the competitive environment discussed. The study also extends to infinite rank model cases as well. Moreover, a case study has been conducted to predict the risk of appropriate forecasting of different quality level using the model developed. The article emphasized the impact of number of failure gate and their respective influence in the area of ascertaining production quality making use of the concept of position ratio along with the outcome probabilities, which in turn improves the decision making to foresee the possible product quality variations in any manufacturing system, after a specific tenure of production. The methodology developed here will definitely find its application in the area of designing some powerful Decision Support Systems (DSS), where competition is fundamentally concerned with.

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Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

Cited by 20 publications

References 23 publications

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Reliability loss–oriented product assembling quality risk modeling approach based on reliability–quality–reliability chain

Effect-Cause Analysis and Prediction Convergence of Random Failure Gate in a Probabilistic Competitive Environment with Case Study on Quality Control Process

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