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基于粒子群算法识别方法,确定坐标测量机的最佳测量区域
Hongfang Chen1, Huan Wu1, Yi Gao1
1Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124, China.
The Review of scientific instruments
|August 8, 2024
概括
本研究提出了一种粒子群优化方法,以确定大型坐标测量机 (CMM) 的最佳测量区域. 该方法通过优化CMM的空间定位和测量参数来提高测量精度.
科学领域:
- 计量学和测量科学 计量学和测量科学
- 机械工程 机械工程
- 计算智能是一种计算智能.
背景情况:
- 坐标测量机 (CMM) 对于精确的维度验证至关重要.
- 在CMM中优化测量空间对于最大限度地提高准确性和效率至关重要.
- 现有的方法可能无法完全解决大型CMM中复杂的体积误差分布.
研究的目的:
- 开发一种智能方法,以确定大型CMM的最佳测量区域.
- 通过智能优化测量空间来提高CMM测量的精度.
- 用实验数据和高精度标准验证拟议的方法.
主要方法:
- 利用激光追踪器多站技术绘制体积误差分布图.
- 使用逆距离权重 (IDW) 算法进行体积误差插值.
- 应用了LASSO算法来解决半刚体模型并获得几何错误.
- 开发了一个错误优化模型,并使用粒子群优化 (PSO) 进行区域识别.
主要成果:
- 在特定的CMM空间坐标内,PSO算法成功地确定了一个 (35 × 35 × 35) mm3物体的最佳测量区域.
- 确定的最佳区域进一步细化为精确的范围 (例如,280 mm < X < 315 mm).
- 使用高精度球体进行实验验证,证实了拟议的基于公共服务任务的方法的准确性和可靠性.
结论:
- 提出的粒子群优化方法有效地确定了CMM的最佳测量区域.
- 这种智能方法通过优化CMM空间利用来提高测量精度和效率.
- 这些发现为改进精密计量学应用提供了有价值的工具.
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