Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

Friederich, Jonas; Lazarova-Molnar, Sanja

doi:10.1016/j.jmsy.2023.11.001

Cited by 7 publications

(2 citation statements)

References 58 publications

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“…The primary aim of risk identification in the production of steel structural components is to promptly and accurately detect risk factors that affect quality, a crucial element in developing an effective risk assessment model. This research integrates quality management theory with actual risk identification practices to establish a risk identification framework, further incorporating the 5M1E theory, as discussed by P. Cao et al [52] and Friederich & Lazarova-Molnar [53]. Subsequently, the theoretical framework was revised based on the findings from actual production surveys: the "measurement" factor has been substituted with "management scheme" risk to accurately reflect the impact of management decisions on production quality; similarly, "method" has been replaced by "technological" risk, highlighting the pivotal role of production technology and process flows in risk management.…”

Section: Risk Identification For the Quality Of Steel Component Produ...mentioning

confidence: 99%

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Zhong,

Peng

2024

Buildings

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This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform a probabilistic analysis of quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the “low professional level of designers”, “poor production refinement”, and “poor storage conditions for finished products” have a significant impact on quality risks. This study offers a scientific risk assessment tool designed to address the quality control challenges commonly experienced in the manufacturing of steel structural components. Identifying the critical risk factors that influence quality empowers actual production enterprises to develop risk management strategies and improvement measures in a more focused manner, thereby facilitating more effective resource allocation and risk prevention and control. Consequently, this approach has a significant impact on enhancing the overall production level and quality within the industry.

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Section: Risk Identification For the Quality Of Steel Component Produ...mentioning

confidence: 99%

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Zhong,

Peng

2024

Buildings

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“…With the development of artificial intelligence and big data technologies, intelligent scheduling plays a decisionmaking role in resource allocation and equipment management of advanced manufacturing systems 1 . Flexible job-shop scheduling problem (FJSP), covering operation research, sequencing theory, and optimization methods, is mainly to determine the processing equipment and process path planning.…”

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confidence: 99%

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

Wang,

Zhong,

Liu

et al. 2024

Sci Rep

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This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference network is proposed to minimize the makespan. Firstly, the FDSSP is defined as the mathematical model of the flexible job-shop scheduling problem joined to the assembly constraint level. It is translated into a Markov decision process that directly selects behavioral strategies according to historical machining state data. Secondly, the proposed ten generic state features are input into the deep neural network model to fit the state value function. Similarly, eight simple constructive heuristics are used as candidate actions for scheduling decisions. From the greedy mechanism, optimally combined actions of all machines are obtained for each decision step. Finally, a deep temporal difference reinforcement learning framework is established, and a large number of comparative experiments are designed to analyze the basic performance of this algorithm. The results showed that the proposed algorithm was better than most other methods, which contributed to solving the practical production problem of the manufacturing industry.

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A novel fine-grained assembly sequence planning method based on knowledge graph and deep reinforcement learning

Jiang,

Guo,

Huang

et al. 2024

Journal of Manufacturing Systems

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Reliability assessment of manufacturing systems: A comprehensive overview, challenges and opportunities

Cited by 7 publications

References 58 publications

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

A novel fine-grained assembly sequence planning method based on knowledge graph and deep reinforcement learning

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