Abstract
Acetone-Sensing Properties of WO3-Based Gas Sensors Operated in Dynamic Temperature Modulation Mode —Effects of Loading of Noble Metal and/or NiO onto WO3—
Highlights
For a grinding machine, the machining error means the relative position error between the grinding head and the workpiece
In this study, we aim to build a thermal error model using Grey system theorem (GT) and deep neural network (DNN) hybrid schemes to enhance the thermal precision of a CNC grinding machine with a challengeable long and narrow bed and three-head column structures
The convolutional neural network (CNN) has proven to be the state of the art of the comprehensive network of DNNs, and is capable of mapping multi-output with multi-input modelling problems
Summary
The machining error means the relative position error between the grinding head and the workpiece This machining error mainly comes from, for example, the structure dynamic deformation, thermal distortion, grinding head error, workpiece error, mechanism kinematic error, controller error, and controlling rule error. Among these errors, the thermal deformation error plays the most important role and is difficult to handle. There are three strategies to reduce the thermal error of grinding machines: adopting a thermally stabilized structural design, restraining heat generation from components, machine users, and the environment, and compensating the thermal error in real time Among these strategies, the thermal error compensation technique is the most effective and popular
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