Multi-process planning comprehensive evaluation system and method based on digital twinning and deep learning

Abstract

The invention relates to a multi-process planning comprehensive evaluation system and method based on digital twinning and deep learning, and belongs to the field of process planning virtual simulation. The system comprises a physical equipment layer, a data sensing layer, an information processing layer, a virtual space layer and a digital twinning layer, the data sensing layer collects related real-time data and historical data of the physical equipment layer in the machining process and sends the data to the information processing layer for data fusion analysis and processing; meanwhile, the acquired data is transmitted to a virtual space layer, and a dynamic virtual model corresponding to the physical entity is constructed under the guidance of requirements of related models in a digital twinning layer; the digital twinning layer is used for leading the other layers together, carrying out virtual-real interaction feedback, fusion analysis and iterative optimization, and carrying out comprehensive evaluation analysis on different process schemes of the to-be-processed part, so that process parameter optimization and process route improvement are realized. According to the method, the flexibility and dynamic adaptability of process planning are improved, the resource utilization rate can be improved, the processing period is shortened, and the production cost is reduced.

Description

technical field [0001] The invention belongs to the technical field of process planning virtual simulation, and relates to a multi-process planning comprehensive evaluation system and method based on digital twins and deep learning. Background technique [0002] With the rapid development of a new generation of information, communication, and Internet of Things technologies, the manufacturing industry is leaping forward in the direction of digitization, networking, and intelligence. The traditional large-volume and large-scale manufacturing model has been gradually eliminated, and new products are being updated more and more frequently. The new market demand is facing severe challenges such as diversification and personalization, small batches of multiple varieties, and short-term and fast response. In the whole life cycle of products, as the transition of mechanical products from design stage to manufacturing stage, processing technology planning is an important basic work ...

AI Technical Summary

Problems solved by technology

[0003] The traditional model-based and knowledge-based evaluation of processing technology planning schemes mainly relies on single factors and empirical knowledge, without comprehensively considering the combined effects of various influencing factors in the production process, and the process evaluation indicators are often empirical, fuzzy and uncertain , leading to the subjectivity and application limitations of the evaluation results

At the same time, most process formulation processes are carried out under the assumption that the workshop has sufficient resources, which is a static process planning; when the enterprise resources change dynamically during the process of process route planning and process route implementation, the original process planning route Efficiency will be greatly reduced, or even invalid

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