High-toughness amorphous alloy component design method and system based on machine learning
The complex relationship between amorphous alloy composition and fracture toughness is unlocked through machine learning technology, and the problem of low efficiency in designing high-toughness amorphous alloy composition in the existing technology is solved, fast and accurate component design and toughness prediction are achieved, and material research and development efficiency is improved.
CN119694447AInactive Publication Date: 2025-03-25INSTITUTE OF MATERIALS & INTELLIGENT MANUFACTURING JIANGXI ACADEMY OF SCIENCES
3 Cites 1 Cited by
Patent Information
- Application Number
- CN202411865984.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-18
- Publication Date
- 2025-03-25
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Technical Problem
It is difficult for the prior art to design high-toughness amorphous alloy components quickly and accurately, resulting in low efficiency, long cycle and high cost of new materials.
Method used
By mining the data on amorphous alloy composition and fracture toughness in existing literature reports, the model is established using machine learning technology to unlock the implicit complex relationship between 'component-performance', and the rapid and accurate design of amorphous components and predict fracture toughness according to performance requirements.
Benefits of technology
It improves the efficiency of high-toughness amorphous alloy component design, reduces trial and error costs, shortens the R&D cycle, and is suitable for all amorphous alloy component designs.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a high-toughness amorphous alloy component design method and system based on machine learning. The method comprises the following steps: (1) establishing a data set according to historical data; (2) dividing the data into a training set and a test set; (3) carrying out normalization processing on the data; (4) creating various machine learning models; (5) fitting the training set in the model, and adjusting hyper-parameters to obtain an optimal machine learning model; (6) predicting the test set in the model; (7) quantitatively evaluating the performance of various models, and comparing to obtain an optimal model; and (8) inputting component elements to obtain fracture toughness, and performing experimental verification. According to the method, a large number of existing data about the amorphous alloy components and the fracture toughness are excavated, the implicit complex relation between the components and the performance is unlocked through the machine learning technology, and the alloy components are rapidly and accurately designed according to the performance requirement. According to the method, the component design efficiency of the high-toughness amorphous alloy is improved, the trial and error cost is low, and the adaptability is high.
Need to check novelty before this filing date? Find Prior Art