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基于几何特征参数的机械模具数控加工形位误差预测

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在数控加工过程中,由于缺少必要的数控加工几何特征参数使得建模过程复杂,模型精度较低,导致形位误差指标预测结果偏差较大.因此,设计一种基于几何特征参数的机械模具数控加工形位误差预测方法.采用最小二乘法,计算拟合采样信号,通过分析其与真实采样信号之间的误差,获取最佳拟合采样信号;结合加工刀具的轨迹信息,检测加工过程中的形位误差,并测量机械模具的几何特征参数,获取刀具离散点的坐标,利用三角函数原理,构建加工刀具位姿模型;以圆度误差为例,采用最小区域法,预测圆度误差,并结合最小二乘法拟合实际误差数据,实现机械模具数控加工形位误差预测.实验结果表明,相同测试环境下,所提方法得到的各个形位误差指标值与实际情况接近,验证了设计方法的有效性.
Prediction of shape and position errors in CNC machining of mechanical dies based on geometric feature parameters
In the process of CNC machining,the lack of necessary geometric feature parameters makes the modeling process complex and the model accuracy low,resulting in a significant deviation in the prediction results of the shape and position error indicators.Therefore,a geometric feature parameter based method for predicting the shape and position errors of mechanical mold CNC machining is designed.Using the least squares method,calculate the fitted sampling signal and obtain the best fitted sampling signal by analyzing the error between it and the actual sampling signal.Combined with the path information of the machining tool,the form and position errors in the machining process are detected,and the geometric feature parameters of the mechanical mold are measured to obtain the coordinates of the discrete points of the tool.The trigonometric functions principle is used to build the pose model of the machining tool.Taking roundness error as an example,the minimum region method is used to predict roundness error,and the least squares method is combined to fit actual error data to achieve shape and position error prediction in mechanical mold CNC machining.The experimental results show that under the same testing environment,the various shape and position error indicators obtained by the proposed method are close to the actual situation,verifying the effectiveness of the design method.

geometric feature parametersmechanical moldCNC machiningerror prediction

张家峰

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湄洲湾职业技术学院,福建莆田 351100

几何特征参数 机械模具 数控加工 误差预测

2019年度福建省中青年教师教育科研项目

JAT191509

2024

齐齐哈尔大学学报(自然科学版)
齐齐哈尔大学

齐齐哈尔大学学报(自然科学版)

影响因子:0.182
ISSN:1007-984X
年,卷(期):2024.40(2)