Using a new bathtub curve to correlate quality and reliability

Roesch, William J.

doi:10.1016/j.microrel.2012.08.022

Cited by 27 publications

(14 citation statements)

References 5 publications

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“…Jiang and Murthy (2009) put forward a statistical method to model the effect of quality variations in manufacturing on product reliability based on Weibull distribution. Roesch (2012) proposed a new bathtub curve to reveal quality and reliability correlations, and he emphasized that the relationship can predict future experiences in reliability based on manufacturing quality data. Kim (2013) investigated the effect of manufacturing defects on the infant failure rate and observed that for any product, the failure rate decreases if the device-to-device variability of the number of defects is large enough.…”

Section: State Of the Artmentioning

confidence: 99%

“…Infant failures have often been referred to the quality problems occurred in the "infant" region of leftmost of bathtub curve of product life (Roesch, 2012), and infant failures are assumed to be caused mainly by design vulnerabilities and manufacturing defects (Jiang and Murthy, 2009). Because of the high occurrence possibility of early failures in infant life, the infant failure rate always keeps unexpectedly high and received extensive attention.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

Wang

et al. 2016

Engineering Applications of Artificial Intelligence

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Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

Wang

et al. 2016

Engineering Applications of Artificial Intelligence

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“…However, these studies ignore the interference of harmful factors in the manufacturing process and cannot fundamentally guide the reliability improvement program of a product. From another perspective, due to defects in the manufacturing process, hidden defects in the product only explode with time and stress in the initial stage of use [ 31 ]. He et al believed that the high failure rate of a product in the early stages of use could be considered a manifestation of low production reliability [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

Zhang

Han

et al. 2020

Sensors

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For intelligent manufacturing systems, there are many deviations in operational characteristics, and the coupling effect of harmful operational characteristics leads to the variations in quality of the work-in-process (WIP) and the degradation of the reliability of the finished product, which is reflected as a loss of product manufacturing reliability. However, few studies on the modeling of product manufacturing reliability and mechanism analysis consider the operating mechanism and the coupling of characteristics. Thus, a novel modeling approach based on quality variations centered on the coupling of operational characteristics is proposed to analyze the formation mechanism of product manufacturing reliability. First, the PQR chain containing the co-effects among the manufacturing system performance (P), the manufacturing process quality (Q), and the product manufacturing reliability (R) is elaborated. The connotation of product manufacturing reliability is defined, multilayered operational characteristics are determined, and operational data are collected by smart sensors. Second, on the basis of the coupling effect in the PQR chain, a multilayered product quality variation model is proposed by mining operational characteristic data obtained from sensors. Third, an integrated product manufacturing reliability model is presented on the basis of the variation propagation mechanism of the multilayered product quality variation model. Finally, a camshaft manufacturing reliability analysis is conducted to verify the validity of the proposed method. The method proposed in this paper proved to be effective for evaluating and predicting the product reliability in the smart manufacturing process.

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“…Jin et al 11 applied the Six Sigma methodology and presented a closed-loop control framework for estimating infant failures of products. In the field of semiconductor manufacturing, Roesch 12 discussed the connotations of yield, quality, and reliability and then proposed a new bathtub curve that measures fallout to predict batch reliability. Kim 13 focused on the effect of manufacturing defect accumulation on the product infant failure and presented a defect-based reliability model.…”

Section: Introductionmentioning

confidence: 99%

Product reliability–oriented optimization design of time-between-events control chart system for high-quality manufacturing processes

Chen

Cui

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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In high-quality manufacturing processes, the yield is no more the only issue, and the quality control approach that also considers the product’s actual performance gradually becomes the focus. Reliability is a critical dimension of quality, and its degradation in usage is always determined by the manufacturing quality. To mitigate the degradation of reliability, this article presents a product reliability–oriented optimization design of the time-between-events control chart system, where the quantitative impact of process quality on the product reliability is analyzed. First, critical-to-reliability process parameters are identified, and a product reliability degradation model is proposed considering the effect of process variations and defects. Second, the observed event and the used statistics are determined to prearrange the time-between-events chart system. Third, all individual time-between-events charts are systematically optimized to minimize the expected batch product reliability degradation caused by potential process shifts, where the cost and statistical performance are considered as constraints. The result of the case study shows that the proposed design can reduce 27.02% of batch product reliability degradation due to manufacturing process quality, and this model can also save the operating cost on the basis of attaining minimum reliability degradation in certain situations.

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Using a new bathtub curve to correlate quality and reliability

Cited by 27 publications

References 5 publications

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

Product reliability–oriented optimization design of time-between-events control chart system for high-quality manufacturing processes

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