Risk-based quality accident ranking approach using failure mechanism and Axiomatic domain mapping

He, Yihai; Cui, Jiaming; Liu, Fengdi; Zhu, Chunling

doi:10.1080/14783363.2018.1453300

Cited by 10 publications

(10 citation statements)

References 30 publications

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“…These are caused by random and systematic factors that can arise over time, such as managerial issues and technical limitations, mainly linked to wear, fatigue and operation failures (Juran and Godfrey, 1999; Lim et al , 2015; Souza et al , 2018). These failures can have origins in several mechanisms, or even, in a combination of these, requiring analysis for identification and solution through actions on its root causes (He et al , 2018). To maintain or improve the process reliability it is necessary to analyse and remove the causes of these failures, enabling companies to review the standards and established quality levels (Souza et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Industry practices on the rework of defective products: survey results

Soares

Sousa

Tereso

2020

TQM

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PurposeThe general objective of this research was to identify the practices of the mass production industries, on the decisions related to the detection of defective products and to identify relevant criteria, actions, effects and variables to be used in a decision-making model.Design/methodology/approachA survey questionnaire was developed and structured in 20 questions, with 18 closed questions and 2 open questions. The questions were constructed based on the literature review, with the identification of 15 theoretical and practical concepts of quality. Seven other information requests were included, concerning the initial characterization of the defective items and industry. The company, the product, the processes and the defects were contextualized, and then the decision-making process was framed, to understand the factors that influenced it.FindingsThe industries of the Industrial Pole of Manaus discard or rework their defective products influenced by promoting the lowest cost and the required quality. The factors with the biggest influence on decisions are rework and disposal costs, and time available for rework and replacement. The main requirements defined for the decision-making model were: 1) compare the rework and discard options, only if the rework reaches the required quality level; and 2) identify and account the effects of defective items on quality, productivity and costs.Research limitations/implicationsOverall, 109 questionnaires were sent, representing 21.3% (109/512) of the companies at the Industrial Pole of Manaus. The respondents represent 14.6% (75/512) of the population.Originality/valueDefects can occur, requiring a decision that promotes the lowest cost and the required quality. Quality cost models do not show a systematic analysis for identification, accounting, evaluation of effects and criteria associated with the destination of manufactured defective items. Therefore this research was done to fill this gap.

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Section: Literature Reviewmentioning

confidence: 99%

Industry practices on the rework of defective products: survey results

Soares

Sousa

Tereso

2020

TQM

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“…3 Therefore, to ensure that reliable products are manufactured in batch productions with low risks, manufacturing engineers have to continuously improve the assembling quality. 4…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al 16 used the positive and negative aspects of the quality variation analysis method to innovate quality risk analysis technology and to suggest a new mass loss function. He et al 4 proposed a quantitative risk method in consideration of product reliability by analyzing the low quality in assembling processes. Based on the cost of quality risk, Mo et al 17 sort the process errors that lead to quality risks and to determine the priority of risk control to reduce the quality risk of the composite fiber–reinforced plastic assembly process.…”

Section: Introductionmentioning

confidence: 99%

“…3 Therefore, to ensure that reliable products are manufactured in batch productions with low risks, manufacturing engineers have to continuously improve the assembling quality. 4 The newly released ISO9001:2015 standard emphasizes the application of risk-based thinking on the quality assurance of products, thereby making integrated risk management an unavoidable trend. 5 Therefore, to reduce potential assembling risk with regard to product reliability, assembling quality should be controlled with the aid of risk thinking.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The assembly process is a classic and critical step in manufacturing, whose quality considerably affects product reliability [1,2]. The assembly system is a direct carrier of the assembly process, and its health is an important factor affecting the assembly quality [3].…”

Section: Introductionmentioning

confidence: 99%

Risk-Oriented Product Assembly System Health Modeling and Predictive Maintenance Strategy

Liu

Zhao

et al. 2019

Sensors

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Assembly quality is the barometer of assembly system health, and a healthy assembly system is an important physical guarantee for producing reliable products. Therefore, for ensuring the high reliability of products, the operational data of the assembly system should be analyzed to manage health states. Therefore, based on the operational data of the assembly system collected by intelligent sensors, from the perspective of quality control based on risk thinking, a risk-oriented health assessment method and predictive maintenance strategy for managing assembly system health are proposed. First, considering the loss of product reliability, the concept of assembly system health risk is proposed, and the risk formation mechanism is expounded. Second, the process variation data of key reliability characteristics (KRCs) collected by different sensors are used to measure and assess the health risk of the running assembly system to evaluate the health state. Third, the assembly system health risk is used as the maintenance threshold, the predictive maintenance decision model is established, and the optimal maintenance strategy is determined through stepwise optimization. Finally, the case study verifies the effectiveness and superiority of the proposed method. Results show that the proposed method saves 37.40% in costs compared with the traditional method.

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Risk-based quality accident ranking approach using failure mechanism and Axiomatic domain mapping

Cited by 10 publications

References 30 publications

Industry practices on the rework of defective products: survey results

Industry practices on the rework of defective products: survey results

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

Risk-Oriented Product Assembly System Health Modeling and Predictive Maintenance Strategy

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