Prediction of surface roughness in CNC turning by model-assisted response surface method
Prediction of surface roughness in CNC turning by model-assisted response surface method
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DOI：
10.1016/j.precisioneng.2019.12.004
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发表时间：
2020-03-01
影响因子：
3.6
通讯作者：
Furukawa, Yoshiyuki
中科院分区：
文献类型：
--
作者：
Misaka, Takashi;Herwan, Jonny;Furukawa, Yoshiyuki
各种统计方法，如经典回归和现代机器学习方法，已被应用于测量数据，以估计制造过程的状态，这是现在由物联网（IoT）的运动推动。在这项研究中，我们试图整合一个分析工具模型的表面粗糙度和测量数据的数控车削开发的建模方法，不太依赖于数据，但也有效地利用现有的分析模型。在以前的研究中，我们使用切削速度，进给速度，切削深度和三个加速度分量从加速度计来预测表面粗糙度。采用协同克里格法将上述测量结果与车削表面粗糙度模型进行整合。结果表明，该方法提高了预测精度时，只有少量的数据可用于模型构建。同时，普通Kriging方法的精度仅依赖于测量数据，当测量数据足够跨越参数空间时，其精度是合适的，但在实际操作中可能很少见。我们还尝试使用协克里格方法检测测量的离群值，当没有额外的信息来评估测量数据的有效性时，这可能是一个不平凡的任务。
Various statistical approaches such as classical regression and modern machine learning methods have been applied to measurement data for estimating the status of manufacturing processes, which is now boosted by the movement of Internet of Things (IoT). In this study, we attempt to integrate an analytical tool model of surface roughness and measurement data of CNC turning to develop a modeling approach which does not depend too much on data, but also effectively uses existing analytical models. As in previous researches, we use cutting speed, feed rate, depth of cut and three acceleration components from an accelerometer to predict surface roughness. Co-Kriging method is employed to integrate the above measurements and a well-known model of surface roughness in turning. It was confirmed that the approach improved the prediction accuracy when only small amount of data is available for model construction. Meanwhile, the accuracy of ordinary Kriging method, which only depends on data, is suitable when measurement data sufficiently spans the parameter space, being expected that it may be rare in actual operations. We also attempted to detect outlier of measurements using the Co-Kriging method, which might be a non-trivial task when there is no additional information to evaluate the validity of the measurement data.