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Updated: Jul 21, 2025

Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
Research on the integrated management and mapping method of BOM multi-view for complex products
Jun Chen1,2, Yuan Xiao1, Gangfeng Wang2
1Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China.
This study introduces a novel Bill of Materials (BOM) multi-view integrated management and mapping method to enhance BOM accuracy and consistency in complex product development. The research addresses challenges in modern manufacturing by improving BOM data transformation efficiency.
Area of Science:
- Manufacturing Engineering
- Product Lifecycle Management
- Data Management
Background:
- Modern manufacturing faces challenges with increasing complexity and frequent transformations of Bill of Materials (BOM) data.
- Ensuring diversity, accuracy, and consistency of BOM is crucial for efficient product development.
Purpose of the Study:
- To research and develop a BOM multi-view integrated management and mapping method for complex products.
- To improve the efficiency of BOM management and ensure data integrity throughout the product lifecycle.
Main Methods:
- Established a complex product BOM integrated management framework and evolution model based on multiple views.
- Developed a BOM transformation model and a rule-based BOM multi-view mapping algorithm.
- Utilized rule definition and mathematical modeling for key components to elaborate complex mapping principles.
Main Results:
- A feasible and versatile framework and method for BOM integrated management and transformation were proposed.
- The rule-based mapping algorithm effectively handles complex BOM data transformations.
- Demonstrated feasibility and versatility through BOM multi-view transformation cases and a prototype system.
Conclusions:
- The proposed BOM multi-view integrated management and mapping method significantly enhances BOM management efficiency and data consistency for complex products.
- The research provides a robust solution for challenges in intelligent manufacturing environments.
- The developed model and method are validated for practical application in diverse manufacturing scenarios.
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