无缝内螺纹铜管制造工艺设计方法及系统
By using a knowledge graph of internally threaded copper tubes and a deep learning model, combined with various network models and attention mechanisms, copper tube processing data is processed automatically, solving the problems of long processing time and cumbersome process in traditional copper tube forming, and achieving efficient and accurate process design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 常州润来科技有限公司
- Filing Date
- 2024-03-04
- Publication Date
- 2026-07-17
AI Technical Summary
In the traditional process of forming internally threaded copper tubes, multiple trials and adjustments are required when dealing with new products, which is time-consuming and cumbersome, making it difficult to quickly determine the optimal processing technology.
By employing a knowledge graph of internally threaded copper tubes and artificial intelligence technology, combined with a deep learning model, and utilizing a multilayer perceptron, convolutional neural network, and long short-term memory network, along with attention mechanism and secondary weighting method, we can automatically process structured, image, and time-series data and recommend the optimal manufacturing process.
It improves the accuracy and efficiency of process design, reduces manual intervention, enhances the generalization ability of the model, and adapts to the processing needs of copper tubes of different types and complexities.
Smart Images
Figure CN118133450B_ABST