Research on maintenance optimal policy based on product quality control

Xiao-mei, Yang; Zeng, Jianchao

doi:10.1177/0954405416654415

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…Dimensional variation and internal defect are the two main indicators of process quality. 3 The accumulation of dimensional variations results in an accelerated product crash by the original mechanism that has been considered in design stage, whereas internal defect reduces product reliability by introducing a new failure mode. Both process variations and defects may accumulate and remain in delivered products, so their effects should be considered in the process of establishing the reliability degradation model.…”

Section: Tbe Control Chart System Predesign For Enhancing Product Reliabilitymentioning

confidence: 99%

“…1,2 Nowadays, a high-quality manufacturing process with low defective percentage and process shift rate can be achieved more easily than decades ago. 3,4 In this background, the focus of quality control cannot be restricted only to the yield anymore. The actual reliability is severely dependent on the manufacturing quality and the effectiveness of process control.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Tbe Control Chart System Predesign For Enhancing Product Reliabilitymentioning

confidence: 99%

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:

View full text Add to dashboard Cite

show abstract

“…In addition to the above-integrated studies of production and quality control, many studies also made an integrated investigation of maintenance and quality control. 16–18…”

Section: Introductionmentioning

confidence: 99%

A two-stage integrating optimization of production scheduling, maintenance and quality

Zheng

Yang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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During the process of actual production, production scheduling, preventive maintenance and quality control have a close interaction with each other and thus need to be integrated for improving manufacturing system performance. The aim of this article is to jointly optimize the job sequence, the preventive maintenance locations, the preventive maintenance interval and decision variables of control chart such that the expected cost per unit time and penalty cost due to schedule delay are minimized. A two-stage integrating optimization model of production scheduling, maintenance and quality in a single machine system is developed. A control chart is adopted to monitor the whole process with Taguchi’s loss function estimating the quality cost of each condition and perfect preventive maintenance activity is conducted at the end of each interval. A genetic algorithm is designed to solve this model in order to obtain the optimal job sequence and the locations of preventive maintenance. Numerical examples and thorough sensitivity analyses are provided to illustrate the proposed integrated model. Finally, the efficiency of the proposed two-stage integration model is verified by the comparison of the stand-alone model under the same condition. Compared with the stand-alone model, the proposed integrated method can save the cost rate by 12%–42% under five different situations.

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“…Moreover, the quality data, as the key performance data of manufacturing system operation process, are also being applied in production equipments maintenance policy optimization and manufacturing system health prognosis. 9, 10 The advantage of data-based methods is that they fully utilize big operational data and translate them into useful information for diagnostic/prognostic decisions, which can significantly boost efficiency and reduce costs in the industry. However, the efficacy is highly dependent on the accurate comprehension on the meaning of the quantity and quality of health system operational data.…”

Section: Introductionmentioning

confidence: 99%

Health prognosis approach for manufacturing systems based on quality state task network

Cui

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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Previous studies on health prognosis are exceedingly dependence on the failure data and sensor data of a single component of manufacturing systems, and the holistic health prognosis techniques applicable to whole manufacturing systems still remain a challenge due to its increasingly physical and functional complexity. Therefore, a generalized health prognosis method is presented based on the deep fusion of quality-oriented big data of operational process of manufacturing systems. First, the generalized connotation of manufacturing system health is explained from the aspects of the physical composition and functional characteristics of manufacturing systems, and the quality state task network is proposed to organize quality-oriented big operational data, which improve the state transparency of the manufacturing system and lay the foundation of holistic health prognosis. Second, key characterization parameters in quality state task network are defined. Specifically, the performance state is analyzed based on multistate characteristics by considering the effects of stochastic degradation processes; the product quality state is quantified by using a process model that is established based on monitoring and inspection data; and the task execution state is quantitatively described by analyzing the evolution of task demand among machines. Third, an integrated model is built by integrating the three above-mentioned states as two key indicators, namely, qualified degree and mission reliability, for the comprehensive prognosis of the health of manufacturing systems. Finally, the effectiveness of the proposed approach is verified with a case study on the health prognosis of a cylinder head manufacturing system.

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Research on maintenance optimal policy based on product quality control

Cited by 11 publications

References 19 publications

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

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

A two-stage integrating optimization of production scheduling, maintenance and quality

Health prognosis approach for manufacturing systems based on quality state task network

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