Modelling and simulation of the dynamic cutting process and surface topography generation in nano/micro cutting

In nano/micro cutting process, the surface quality is heavily dependent on all the dynamic factors in machining including those from the material, tooling, cutting parameters, servo accuracy, mechanical structure deformation, and non-linear factors as well. The machined surfaces are generated based on the tool profile and the real tool path combining with the various external and internal disturbances. To bridge the gap between the machining conditions and the surface quality, the integrated simulation system presented involves the dynamic cutting process, control/drive system and surface generation module. It takes account all the intricate aspects of the cutting process, such as material heterogeneity, regenerative chatter, built-up edge (BUE), spindle run-out, environmental vibration, and tool interference, etc. The frequency ratio method is used to interpret the surface topography and texture formation. The proposed systematic modelling approach is verified by the cutting experiment

A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution and hidden pattern in both original distinguishing features and latent space, the encoder-decoder-encoder three-sub-network is employed in GAN, based on Deep Convolution Generative Adversarial Networks (DCGAN) but without Tanh activation layer and only trained on normal samples. In order to verify the validity and feasibility of our approach, we test it on rolling bearing data from Case Western Reserve University and further verify it on data collected from our laboratory. The results show that our proposed approach can achieve excellent performance in detecting faulty by outputting much larger evaluation scores