Modelling infant failure rate of electromechanical products with multilayered quality variations from manufacturing process

He, Yihai; Wang, Linbo; He, Zhenzhen; Xiao, Xun

doi:10.1080/00207543.2016.1154215

Cited by 25 publications

(14 citation statements)

References 32 publications

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“…Zhang et al (2015) discuss specific techniques for modelling BD and analytics in the context of computational efficiency. Others present explicit analytical modelling for designated business fields, such as quality control in manufacturing (He et al 2016).…”

Section: Analytics and Data Managementmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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Digital transformation (DT) has become a buzzword, triggering different disciplines in research and influencing practice, which leads to independent research streams. Scholars investigate the antecedents, contingencies, and consequences of these disruptive technologies by examining the use of single technologies or of digitization, in general. Approaches are often very specialized and restricted to their domains. Thus, the immense breadth of technologies and their possible applications conditions a fragmentation of research, impeding a holistic view. With this systematic literature review, we aim to fill this gap in providing an overview of the different disciplines of DT research from a holistic business perspective. We identified the major research streams and clustered them with co-citation network analysis in nine main areas. Our research shows the main fields of interest in digital transformation research, overlaps of the research areas and fields that are still underrepresented. Within the business research areas, we identified three dominant areas in literature: finance, marketing, and innovation management. However, research streams also arise in terms of single branches like manufacturing or tourism. This study highlights these diverse research streams with the aim of deepening the understanding of digital transformation in research. Yet, research on DT still lacks in the areas of accounting, human resource management, and sustainability. The findings were distilled into a framework of the nine main areas for assisting the implications on potential research gaps on DT from a business perspective.

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Section: Analytics and Data Managementmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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“…Furthermore, inherent reliability established during manufacturing will directly determine the rate of product infant failure. Hence, the accumulation of quality deviations in manufacturing is generally accepted as the key cause of high infant failure rate [2].…”

Section: Mechanism Of Product Infant Failure and Its Fuzzinessmentioning

confidence: 99%

“…With the advent of Industry 4.0 and big data era, the Artificial Intelligence (AI) techniques should be widely adopted to the field of quality management and improvement [1]. The infant failure is deemed to be built in the manufacturing process, and infant failure root cause analysis is a routine task for quality managers of product manufacturers [2]- [4], which is also a challenge for most of manufacturers due to the nonintelligent analyzing means and vague understanding of its mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Shukla et al [14] proposed a methodology for optimal sensor allocation for root cause analysis that maximizes key characteristics, which include product characteristics and control characteristics in a multi-station assembly process. He et al [2] presented a multilayered model structure for modeling infant failure rate by integrating and quantifying the influence of quality variations into the manufacturing process for electromechanical products. Tang et al [15] proposed a weighted risk priority number (AMWRPN) based on the FMEA fuzzy metric, and by measuring the degree of ambiguity assessed by experts, the relative weights of different risk factors are determined.…”

Section: Introductionmentioning

confidence: 99%

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Big Data-Oriented Product Infant Failure Intelligent Root Cause Identification Using Associated Tree and Fuzzy DEA

Liu

et al. 2019

IEEE Access

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“…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 ]. However, these studies focused on expressing the reliability of the product under the interference of the initial environmental stress, and could not accurately express product reliability after the manufacturing process.…”

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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Modelling infant failure rate of electromechanical products with multilayered quality variations from manufacturing process

Cited by 25 publications

References 32 publications

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Big Data-Oriented Product Infant Failure Intelligent Root Cause Identification Using Associated Tree and Fuzzy DEA

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

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