Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach
Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach
复制标题
DOI：
10.1007/s00170-009-2260-z
复制
发表时间：
2010-04
期刊：
影响因子：
--
通讯作者：
H. Shahabi;Mani Maran Ratnam
中科院分区：
文献类型：
--
作者：
H. Shahabi;Mani Maran Ratnam
过去，直接在加工表面上测量的粗糙度值被用来开发数学模型，用于预测车削中的表面粗糙度。由于需要大量工件来获取粗糙度数据，因此这种方法缓慢且乏味。在这项研究中，切削刀具的二维图像用于生成模拟工件，从中确定表面粗糙度和尺寸偏差数据。与现有的使用从真实工件提取的特征来表示粗糙度参数的基于视觉的方法相比，在所提出的方法中，仅需要工件的模拟轮廓来获得粗糙度数据。平均表面粗糙度Ra以及从不同进给率、切削深度和切削速度的模拟轮廓中提取的尺寸偏差数据被用作响应面法（RSM）模型的输出。使用传统方法（即表面粗糙度测试仪和千分尺）对实际工件进行测量获得的数据对模型的预测进行了实验验证，并且观察到两种方法之间具有良好的相关性。
In the past, roughness values measured directly on machined surfaces were used to develop mathematical models that are used in predicting surface roughness in turning. This approach is slow and tedious because of the large number of workpieces required to obtain the roughness data. In this study, 2-D images of cutting tools were used to generate simulated workpieces from which surface roughness and dimensional deviation data were determined. Compared to existing vision-based methods that use features extracted from a real workpiece to represent roughness parameters, in the proposed method, only simulated profiles of the workpiece are needed to obtain the roughness data. The average surface roughnessRa, as well as dimensional deviation data extracted from the simulated profiles for various feed rates, depths of cut, and cutting speeds were used as the output of response surface methodology (RSM) models. The predictions of the models were verified experimentally using data obtained from measurements made on the real workpieces using conventional methods, i.e., surface roughness tester and a micrometer, and good correlation between the two methods was observed.