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

Zhang, Anqi; He, Yihai; Han, Xiao; Yao, Li; Yang, Xiuzhen; Zhang, Zixuan

doi:10.3390/s20195677

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…in the operating characteristics of the manufacturing system, which are harmful to the operating characteristics. The coupling effect leads to the change of WIP quality and ultimately affects the decline of finished product reliability, which is reflected as the loss of product manufacturing reliability [64]. Efficient production process design can reduce WIP quality loss, rework, errors and failures, thereby reducing manufacturing system costs and improving the quality of the final product [65].…”

Section: Research On Reliability Optimization Of Manufacturing Proces...mentioning

confidence: 99%

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

et al. 2022

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With the application of new-generation information technology in the full life cycle process of a complex product, it is showing the characteristics of multi-source, real-time, heterogeneous, cross-domain transmission. Large data volume and low value density emerge in the process of complex product design manufacturing and services (DMS). This leads to “information islands” and insufficient utilization of cross-domain reliability data in the process of integration of DMS for complex product R&D design data, manufacturing data and operation and maintenance services (O&MS) data. This paper proposes and illustrates a framework of complex product DMS integration based on reliability data, including complex product design optimization based on manufacturing and service reliability data, complex product intelligent manufacturing process optimization based on real-time reliability data and complex product O&MS optimization based on multi-source heterogeneous reliability data. Additionally, it then realizes complex product design reliability and optimization, manufacturing process reliability and optimization and O&MS reliability and intelligent decision optimization based on reliability data. Finally, the DMS integration framework based on reliability-data-driven proposal is corrected through the case of engine MDS integration, which can effectively improve the cross-domain reliability data utilization and overall product reliability of complex products. The proposed framework extends the application of reliability theory in the process of complex product DMS integration and provides a reference for enterprises in the R&D, manufacturing and O&MS of complex products.

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Section: Research On Reliability Optimization Of Manufacturing Proces...mentioning

confidence: 99%

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

et al. 2022

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Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications

Jamwal¹,

Agrawal²,

Sharma³

2022

International Journal of Information Management Data Insights

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A Machine Learning Approach for Improving Wafer Acceptance Testing Based on an Analysis of Station and Equipment Combinations

Wang

Yang

2023

Mathematics

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Semiconductor manufacturing is a complex and lengthy process. Even with their expertise and experience, engineers often cannot quickly identify anomalies in an extensive database. Most research into equipment combinations has focused on the manufacturing process’s efficiency, quality, and cost issues. There has been little consideration of the relationship between semiconductor station and equipment combinations and throughput. In this study, a machine learning approach that allows for the integration of control charts, clustering, and association rules were developed. This approach was used to identify equipment combinations that may harm production processes by analyzing the effect on Vt parameters of the equipment combinations used in wafer acceptance testing (WAT). The results showed that when the support is between 70% and 80% and the confidence level is 85%, it is possible to quickly select the specific combinations of 13 production stations that significantly impact the Vt values of all 39 production stations. Stations 046000 (EH308), 049200 (DW005), 049050 (DI303), and 060000 (DC393) were found to have the most abnormal equipment combinations. The results of this research will aid the detection of equipment errors during semiconductor manufacturing and assist the optimization of production scheduling.

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

Cited by 3 publications

References 33 publications

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications

A Machine Learning Approach for Improving Wafer Acceptance Testing Based on an Analysis of Station and Equipment Combinations

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