Ultra-precision machining error compensation method based on multi-modal information fusion, medium and equipment

By decoupling errors through multimodal information fusion and physical information neural networks, precise separation and compensation of errors in ultra-precision machining are achieved, improving machining accuracy and stability, and solving the problems of inaccurate error compensation and machine tool vibration in traditional methods.

CN121979103APending Publication Date: 2026-05-05SHANGHAI AEROSPACE CONTROL TECH INST
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Patent Information

Application Number
CN202610153005.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-03
Publication Date
2026-05-05

AI Technical Summary

Technical Problem

In existing ultra-precision machining technologies, machine tool geometric errors, thermal errors, and dynamic errors limit the improvement of machining accuracy. Traditional error compensation methods ignore the time-varying nature of the measurement process and lack physical mechanism support, resulting in inaccurate error compensation and potentially causing machine tool vibration.

Method used

A multimodal information fusion method is adopted, which integrates in-situ measurement device, distributed sensor and physical information neural network to construct multimodal spatiotemporal tensor, decouple static geometric error, time-varying thermal drift error and dynamic vibration error, generate four-way maintenance positive toolpath, and conduct dynamic feasibility verification.

Benefits of technology

It significantly improves machining accuracy, reducing the root mean square error to below 5nm, which is more than 70% higher than traditional methods. It ensures compensation accuracy and machining stability, and solves the problems of false thermal drift misjudgment and insufficient dynamic feasibility in traditional methods.

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Abstract

The invention relates to the technical field of ultra-precision intelligent manufacturing, in particular to an ultra-precision machining error compensation method based on multi-modal information fusion, a medium and equipment. The method comprises the following steps: acquiring three-dimensional shape data of a workpiece through an in-situ measurement device, synchronously acquiring temperature field and vibration data, constructing a multi-modal space-time tensor, inputting a physical information neural network to decouple a static geometric error, a time-varying thermal drift error and a dynamic vibration error, and generating a four-dimensional correction tool path to realize reverse compensation. According to the method, physical constraints are embedded through a PINN model, the problem of false thermal drift misjudgment in a traditional method is solved, experiments show that the RMS error of free-form surface machining is reduced to 5 nm or below, and the precision is improved by 70% or above; and meanwhile, error decoupling is endowed with physical interpretability, the small sample generalization ability is enhanced, and a technical basis is provided for process optimization.
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