High-precision injection-molded titanium alloy part and machine learning assisted preparation method thereof

By combining three-dimensional filling numerical simulation and machine learning methods, the injection molding process parameters of Ti-6Al-4V alloy parts were optimized, solving the problems of low processing efficiency and unstable precision of complex Ti-6Al-4V alloy parts. This achieved high-precision and low-cost preparation and expanded its application range.

CN121551604APending Publication Date: 2026-02-24UNIV OF SCI & TECH BEIJING +1
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Patent Information

Application Number
CN202511474719.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-15
Publication Date
2026-02-24

AI Technical Summary

Technical Problem

Existing technologies suffer from low processing efficiency, unstable quality, and high cost when preparing complex or micro-precision parts from Ti-6Al-4V alloys. In particular, it is difficult to achieve high precision and consistency control in traditional casting processes and mold precision compensation methods.

Method used

By combining three-dimensional filling numerical simulation with machine learning, and through data cleaning, correlation screening and feature importance ranking, a process parameter prediction model is constructed to optimize injection molding process parameters and achieve rapid preparation of high-precision parts.

Benefits of technology

It significantly improves the dimensional accuracy and consistency of Ti-6Al-4V alloy parts, shortens the research and development cycle, reduces manufacturing costs, and broadens its application fields.

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Abstract

The invention belongs to the technical field of powder metallurgy, and particularly relates to a high-precision injection-molded titanium alloy workpiece and a machine learning assisted preparation method thereof.The machine learning assisted preparation method of the high-precision injection-molded titanium alloy workpiece comprises the steps that an original data set is obtained through three-dimensional mold filling numerical simulation, performing data processing on data in the original data set to obtain an expanded data set; based on the expanded data set, multiple different machine learning algorithms are adopted to construct an injection molding process parameter prediction model, and an optimal prediction model is obtained; and optimal process parameters are obtained based on the model, and a target titanium alloy workpiece is prepared. According to the method provided by the invention, three-dimensional mold filling numerical simulation and machine learning are fused, and the process parameter prediction model is constructed and optimized, so that the actual MIM process is guided, the titanium alloy workpiece with high dimensional precision can be obtained, the research and development period is greatly shortened, and the trial-manufacturing cost is reduced.
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