论文标题
使用高斯工艺模型并限制了贝叶斯优化的自我优化磨床机器
Self-Optimizing Grinding Machines using Gaussian Process Models and Constrained Bayesian Optimization
论文作者
论文摘要
在这项研究中，在生产成本方面证明了研磨机的自优化，同时满足了质量和安全性的限制。最终工件的质量要求是定义的，这些质量要求在烧伤和表面粗糙度方面定义，并且安全限制是针对磨削表面的温度定义的。使用高温计和光纤纤维在砂轮和工件之间的接触区域测量磨水温度，该光纤嵌入了旋转的磨轮内。限制性的贝叶斯优化与高斯工艺模型相结合，以确定杯车轮研磨机的最佳进料速率和切割速度制造碳化氢碳酸盐切割插件。该方法仅在几次磨练后确定未知工件和工具组合的最佳参数。它还通过使用随机过程模型在预测最佳参数中的约束不确定性。
In this study, self-optimization of a grinding machine is demonstrated with respect to production costs, while fulfilling quality and safety constraints. The quality requirements of the final workpiece are defined with respect to grinding burn and surface roughness, and the safety constraints are defined with respect to the temperature at the grinding surface. Grinding temperature is measured at the contact zone between the grinding wheel and workpiece using a pyrometer and an optical fiber, which is embedded inside the rotating grinding wheel. Constrained Bayesian optimization combined with Gaussian process models is applied to determine the optimal feed rate and cutting speed of a cup wheel grinding machine manufacturing tungsten carbide cutting inserts. The approach results in the determination of optimal parameters for unknown workpiece and tool combinations after only a few grinding trials. It also incorporates the uncertainty of the constraints in the prediction of optimal parameters by using stochastic process models.
