基于CNC机床的深度学习进行本地角落光滑
Bai Jiang1, Rong Sun2, Ze-Long Li3
1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150006, China.
Scientific reports
|January 3, 2025
概括
这项研究引入了一种新的深度学习算法,通过优化曲率来平滑加工工具路径. 这提高了加工质量,并允许更高的料率,提高效率.
科学领域:
- 制造业 工程 制造工程
- 计算机辅助设计 (CAD)
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 加工工具路径通常使用线性段,限制速度和质量.
- 优化工具路径曲率对于更顺的加工至关重要.
- 现有的智能优化算法面临着计算资源的挑战.
研究的目的:
- 提出一个新的策略,以优化曲水平的加工工具路径.
- 为工具路径平滑开发一个高效的深度学习算法.
- 通过解决当地的角落光滑性来提高加工进料率和质量.
主要方法:
- 引入了用于曲率级工具路径优化的三个基本组件.
- 开发了双响网局部平滑 (DRLS) 算法,结合了第一双局部平滑 (FDLS) 和第二双局部平滑 (SDLS).
- 集成的几何,驱动状态和轮误差限制用于料率规划.
主要成果:
- 与传统的智能算法相比,DRLS算法显著提高了优化效率.
- FDLS和SDLS有效地优化了NURBS控制点和重量,使工具路径更顺.
- 模拟验证了该方法在实现更高的料率和保持加工质量的有效性.
结论:
- 拟议的DRLS算法为平滑加工工具路径提供了有效的解决方案.
- 在局部角落优化工具路径曲率可以提高加工性能.
- 该方法成功地平衡了更高的料率与基本的加工质量限制.
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