A robust tolerance optimization method based on fuzzy quality loss

Cao, Yan; Mao, Jianhua; Ching, Ho; Yang, Jiangxin

doi:10.1243/09544062jmes1451

Cited by 23 publications

(14 citation statements)

References 26 publications

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“…A numerical example is used in this research to show how the model works in a case. The example in this research refers to the case in Cao, Mao, Ching and Yang (2009) with some adjustments of the parameters to fit the model. In this research, the product consists of three components: Revolution axis, End shied nut and Sleeve.…”

Section: Numerical Examplementioning

confidence: 99%

Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

Pratama

Rosyidi

Pujiyanto

2018

JIEM

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Purpose:The aim of this research is to develop two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve. The first stage model is developed to determine the optimal selection of process/suppliers and the component allocation to the selected process/suppliers. The second stage model deals with quality improvement efforts to determine the optimal investment to maximize Return on Investment (ROI) by taking into consideration the learning and forgetting curve.Design/methodology/approach: The research used system modeling approach by mathematically modeling a system which consists of a manufacturer with multi suppliers where the manufacturer tries to determine the best combination of their own processes and suppliers to minimize certain costs and provides funding for quality improvement efforts for their own processes and suppliers. Findings:This research provides better decisions in make or buy analysis and to improve the components by quality investment considering learning and forgetting curve. Research limitations/implications:This research has limitations concerning investment fund that assumed to be provided by the manufacturer which in the real system the fund may be provided by the suppliers. In this model, we also do not differentiate between two types of learning, namely autonomous and induced learning.Practical implications: This model can be used by a manufacturer to gain deeper insight in making decisions concerning process/suppliers selection along with component allocation and how to improve the component by investment allocation to maximize ROI.Originality/value: This paper combines two models, which in previous research both models are discussed separately. The inclusion of learning and forgetting also gives a new perspective in quality investment decision.

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Section: Numerical Examplementioning

confidence: 99%

Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

Pratama

Rosyidi

Pujiyanto

2018

JIEM

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“…Various fuzzy factors are usually involved in the tolerance design process. 29 With regard to the assembly requirements, theoretical value y 0 and tolerance T are determined on the basis of theories or formulas that in turn often hold to certain assumptions, such as approximate or previous experience in engineering applications. Absolute accuracy cannot be obtained automatically, and a certain fuzziness is observed.…”

Section: Gear and Shaft Assemblymentioning

confidence: 99%

Research on assembly tolerance allocation and quality control based on fuzzy reliability

Wang

Liu

Wang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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The enhancement between cost and reliability is the developmental direction of modern manufacturing enterprises. On the basis of fuzzy theory, the relationship among the cost of product quality loss, the reliability of the assembly dimension chain and assembly tolerance is studied together in this article. Processing cost can be considerably reduced and the target of quality engineering is realized by optimization design. As an example, a tolerance design model is determined for gear and shaft assembly. Moreover, the mathematical model of the relationship between the cost of fuzzy quality loss and the fuzzy reliability of the assembly dimension chain is determined in combination with a processing cost function. The optimistic results of key dimensions of gear and shaft assembly are identified through through orthogonal experiments. This method can facilitate product quality control by enterprises and the realization of economic targets. The study findings can also serve as references for other similar studies.

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“…In view of the asymmetry problem, asymmetric quality loss function models were proposed by Spring [4] and Wu [5]. Using fuzzy theory, the concept of fuzzy quality loss was presented and the corresponding fuzzy quality loss model was established by Cao [6]. As most studies have focused on the quality loss function with single characteristic, the total quality loss model with multiple correlated quality characteristics and its tolerance design method were presented by Lee and Tang [7].…”

Section: Introductionmentioning

confidence: 99%

Critical Quality Source Diagnosis for Dam Concrete Construction Based on Quality Gain - loss Function

Wáng¹,

Li²,

Gao³

et al. 2014

JESTR

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In dam concrete construction process, it not only has quality loss arising from quality fluctuation, but also gains quality compensation effect due to the mutual cooperation and adaptation coupling between working procedures (WPs). The calculation and transmission complexity of the quality loss and quality compensation affect the quality management of dam concrete construction. As the quality compensation effect existing in the production practice cannot be described by Taguchi quality loss function, the concept of quality gain-loss function was presented in this paper, which was based on endowing the constant term in the expansion of Taylor series with physical meaning-quality compensation. Based on quality gain-loss function theory, a new quality gain-loss transmission model of dam concrete construction based on GERT network was constructed and its effective algorithm was designed. WP quality gain-loss and its impact on the final product were reasonably measured, and the critical quality routes and critical quality WPs were detected and diagnosed in dam concrete construction network. Summer temperature-controlled concrete construction in the third phase of Three Gorges Project (TGP) was taken as an example to carry out the study, and the calculation results showed the validity and practicability of the presented model and algorithm.

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A robust tolerance optimization method based on fuzzy quality loss

Cited by 23 publications

References 26 publications

Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

Research on assembly tolerance allocation and quality control based on fuzzy reliability

Critical Quality Source Diagnosis for Dam Concrete Construction Based on Quality Gain - loss Function

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