Digital twin-oriented dynamic optimization of multi-process route based on improved hybrid ant colony algorithm

To improve the dynamic adaptability and flexibility of the process route during manufacturing, a dynamic optimization method of the multi-process route based on an improved ant colony algorithm driven by digital twin is proposed. Firstly, based on the analysis of the features of the manufacturing pa...

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Bibliographic Details
Published in:Bulletin of the Polish Academy of Sciences. Technical sciences 2024, Vol.72 (3)
Main Authors: Chen, Zhaoming, Zou, Jinsong, Wang, Wei
Format: Article
Language:English
Subjects:
Adaptive algorithms
Ant colony optimization
Carbon
Decision making
Digital twins
Efficiency
Emissions
Energy consumption
Fuzzy sets
Heat treating
Integer programming
Machining
Manufacturing
Methods
Multiple objective analysis
Optimization algorithms
Precedence constraints
Process planning
Product development
Production costs
Production management
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Summary:To improve the dynamic adaptability and flexibility of the process route during manufacturing, a dynamic optimization method of the multi-process route based on an improved ant colony algorithm driven by digital twin is proposed. Firstly, based on the analysis of the features of the manufacturing part, the machining methods of each process are selected, and the fuzzy precedence constraint relationship between machining metas and processes is constructed by intuitionistic fuzzy information. Then, the multi-objective optimization function driven by the digital twin is established with the optimization objectives of least manufacturing cost and lowest carbon emission, also the ranking of processing methods is optimized by an improved adaptive ant colony algorithm to seek the optimal processing sequence. Finally, the transmission shaft of some equipment is taken as an engineering example for verification analysis, which shows that this method can obtain a process route that gets closer to practical production.
ISSN:0239-7528
2300-1917
DOI:10.24425/bpasts.2024.148875