Product Digital Twin Mimic Modeling And Adaptive Evolution Method For Machining Process

Complex products such as aerospace are characterized by a complex structure,high precision and high performance,and its production mode is multi-variety and variable batch.Most of these complex products and their parts are processed by cutting.When the variety and batch of products change,the cutting process needs to be changed,and the machining preparation time is time-consuming and laborious.The cutting process of products from blank to gradually obtaining complex features is faced with complex changes of force,deformation and heat,and the shape control of the cutting process has always been a difficulty.The adaptive research on the cutting process of complex products in this dissertation has important engineering value.Digital twin technology can realize the quality control of cutting dynamic process by establishing high fidelity mechanism model,combined with real-time collection data and data-driven model.It is a hot spot in cutting research at present.The core of the machining system based on digital twin is the digital twin model composed of model,data and algorithm.Firstly,high fidelity and evolvable models are needed to reflect the dynamics and changes in the machining process at the model level.Secondly,data integration,knowledge expression and organization in the context of machining need to be formed to provide decision support for the machining of the same or similar types of products at the data level.Finally,an adaptive decision-making model is needed to ensure decision-making accuracy under changing working conditions at the algorithm level.This dissertation systematically summarizes the development status of digital twin-driven machining system and studies the basic scientific problems of the evolution method from the three levels of model,data,and algorithm for machining systems.It is of great theoretical significance to deeply study the adaptability of the digital twin model in the machining process.The main innovations are reflected in three aspects.(1).At the model level,in view of the lack of high fidelity and evolvable models to reflect the dynamics and variability in the machining process,inspired by biomimicry,a digital twin mimic model is proposed,and the product digital twin modeling method based on biomimicry is explored.The digital twin mimic model contains three aspects of geometric,physical and contextual information,and has adaptive changing characteristics,which can synchronize the changes of workpiece in the machining process.It not only reflects the machining process of the product object,but also supports the decisionmaking in the machining process through a variety of display means.(2).At the knowledge level,aiming at the lack of effective product quality knowledge expression and data organization and management,the multi-scale evolution mechanism of the digital twin mimic model is studied,and the knowledge generation method of twin data is explored.This method excavates the implicit relationship between the data of the digital twin mimic model,and organizes the product quality knowledge model from the macro,meso and micro levels.The product quality knowledge model involves the factors such as cutting force,clamping force and heat suffered by the product in the machining process,including the quality factors such as product geometric deformation and surface defects.It has the representation characteristics of multidimensional and fine-grained,and provides support for subsequent machining decisions.In addition,the knowledge model is dynamically updated through the memory-forgetting model to adapt to the product machining process in batch.(3).At the algorithm level,in view of the lack of adaptability of the current product decision model to deal with the change of scene,the adaptive reconstruction and evaluation method of the digital twin decision model is studied.This method integrates the concept of transfer learning to adjust the decision model of the digital twin machining system.The process,manufacturing characteristics and manufacturing elements related to product quality are analyzed,and an adaptive evaluation network is constructed to evaluate the reconstructed decision model.Through the above methods,the reliability of the reconstructed algorithm is evaluated while ensuring the rapid reconstruction of the algorithm under the new working conditions.Finally,aiming at the parts of an aerospace product,a prototype of the digital twindriven machining system is developed,and the virtual-real fusion machining environment is built.The rationality and the advanced nature of the digital twin modeling based on biomimicry,knowledge generation and updating mechanism,and algorithm adaptive reconstruction method are verified.