Self-adaptive control optimization method of surface grinding machine for mold component manufacturing

Through digital twin technology and adaptive optimization methods, the processing parameters of mold parts are dynamically adjusted, which solves the problem of difficult to meet the machining accuracy and efficiency in the existing technology at the same time, and realizes efficient and intelligent mold parts manufacturing.

CN119703924AActive Publication Date: 2025-03-28KUNSHAN GUANYEDA PRECISION IND CO LTD
11 Cites 5 Cited by

Patent Information

Application Number
CN202510017134.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-06
Publication Date
2025-03-28
Estimated Expiration
2045-01-06

AI Technical Summary

Technical Problem

In the prior art, the processing parameters of mold parts cannot be dynamically optimized, resulting in difficult to meet the processing accuracy and efficiency at the same time, unstable processing quality, and high trial and error costs.

Method used

By obtaining mold manufacturing design information, digital twin technology is used to generate mold digital twin models, collecting plane grinder manufacturing data for matching optimization, monitoring processing data in real time and optimizing adaptive parameters based on prediction results.

Benefits of technology

Real-time prediction of processing results, dynamic adjustment of processing parameters, improve processing accuracy and efficiency, solve the problems of unstable processing quality and high trial and error costs, and provide an efficient and intelligent optimization method for the manufacturing of complex mold parts.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a surface grinding machine self-adaptive control optimization method for mold component manufacturing, and belongs to the technical field of intelligent manufacturing. The method comprises the steps that machining prediction modeling is conducted on manufacturing design information through the digital twinning technology; a mold component manufacturing data set of the surface grinding machine is collected and obtained, and retrieval matching is conducted on the basis of the manufacturing design information and the mold component manufacturing data set; carrying out processing production and real-time monitoring on the target mold component based on the target grinding machine processing parameters; performing processing prediction on the real-time processing data of the mold component by using the digital twin model of the mold component; and adaptive optimization is carried out on the target grinding machine machining parameters based on the mold component prediction manufacturing information, and adaptive optimization control is carried out on the target mold component through the grinding machine optimization machining parameters. The technical problems that in the prior art, die component machining parameters cannot be dynamically optimized, and machining precision and efficiency are difficult to meet at the same time, so that machining quality is unstable, and trial and error cost is high are solved.
Need to check novelty before this filing date? Find Prior Art