Machining process dynamic evaluation method based on digital twin

Abstract

The invention discloses a machining process dynamic evaluation method based on digital twin. According to the technical scheme; perceptual data in the machining process are obtained and mapped and fused with process evaluation data to form twinning data, a dynamic evaluation method of the machining process is established, an evaluation result is output, dynamic adjustment of the machining processis achieved, the purpose of improving the reuse effectiveness of process knowledge is achieved, and technical support is provided for intelligent design of the machining process.

Description

technical field [0001] The invention relates to a dynamic evaluation method of machining process, in particular to a dynamic evaluation method of machining process based on digital twin. Background technique [0002] Computer-aided process planning is one of the key technologies for enterprises to improve their core competitiveness, and it is a bridge between product design and manufacturing. In order to meet the 3D processing environment, 3D process planning technology based on model definition technology has been applied. With the rapid development of information technology (Internet of Things, big data, cloud computing, etc.), computer-aided process planning technology is facing severe challenges. Intelligent process design has become an inevitable trend of process planning and an inevitable product of the development requirements of intelligent manufacturing. [0003] However, with the continuous change of process requirements and dynamic changes of process equipment, ...

AI Technical Summary

Problems solved by technology

Combining process models and process knowledge to achieve process evaluation is the most widely used method. Based on ideal processing conditions and process equipment, optimal processing parameters can be obtained. However, the obtained optimal parameters are difficult to adapt to actual processing conditions, such as tool The wear condition of the machine tool, the stability of the machine tool, etc., ultimately lead to low effectiveness of the optimization results

With the development of big data technology and cloud computing, process evaluation methods based on process big data have been widely studied. However, most of the process big data used are static data, which are not matched with the actual processing status data, and the obtained results are difficult to match. Actual on-site docking

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