An aluminum alloy forging process optimization method and device based on digital twinning

By constructing a digital twin model of an aluminum alloy forging production line, acquiring multi-source heterogeneous data for preprocessing and feature construction, and using regression models to predict strength and elongation, the process parameters are optimized, solving the problems of insufficient quality and stability in existing technologies and achieving efficient process optimization.

CN122242050APending Publication Date: 2026-06-19WUHAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WUHAN UNIV OF TECH
Filing Date
2026-04-21
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing aluminum alloy forging production lines are unable to meet the demands for high-quality and high-stability manufacturing. They lack the ability to predict key performance indicators of forgings, rely on manual experience for process adjustments, and the nonlinear coupling of the production process leads to long optimization cycles and high costs.

⚗Method used

Based on the digital twin method, a digital twin model of an aluminum alloy forging production line is constructed, multi-source heterogeneous data is acquired, preprocessed, and a set of physical enhancement features is constructed. Strength and elongation are predicted through a regression model, process optimization constraints are constructed, and process parameters are optimized.

🎯Benefits of technology

This technology enables the prediction of key performance characteristics and optimization of process parameters in the aluminum alloy forging process, thereby improving the quality stability and efficiency of production and reducing trial and error costs.

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Abstract

This application provides a method and apparatus for optimizing aluminum alloy forging processes based on digital twins, relating to the field of digital twins. The method includes: acquiring multi-source heterogeneous data during the operation of an aluminum alloy forging production line and constructing a digital twin model; preprocessing the multi-source heterogeneous data based on the digital twin model and constructing a set of physical enhancement features; inputting the set of physical enhancement features into a first-stage prediction model to obtain strength-related performance prediction results; constructing a derived feature set based on the strength-related performance prediction results and inputting it together with the physical enhancement feature set into a second-stage prediction model to obtain elongation prediction results; constructing process optimization constraints and combining the strength-related performance prediction results and elongation prediction results to search, filter, and sort adjustable process parameters to obtain a set of target process parameter evaluation results. This application solves the problem that existing aluminum alloy forging production lines cannot meet the current demands for high-quality and high-stability manufacturing.
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