73,993 research outputs found
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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
Recommended from our members
Dynamic cutting process modelling and its impact on the generation of surface topography and texture in nano/micro cutting
In the nano/micro cutting process, the surface quality is heavily dependent on all the dynamic factors, including those from the material, tooling, process parameters, servo accuracy, mechanical structural stiffness, and non-linear factors as well. The machined surface is 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 cutting process and the surface topography/texture generation, an integrated simulation-based approach is presented involving the dynamic cutting process, control/drive system, and the surface generation. The simulations take account of all the intricate aspects of the cutting process resulting in the surface topography and texture formation, such as material heterogeneity, regenerative chatter, built-up edge (BUE), tool wear, spindle runout, environmental vibration, tool interference, etc. Both the frequency ratio method and sampling theoremare used to interpret the surface topography and texture formation. The effects of non-linear factors on the surface generation are simulated and analysed through the power spectral density (PSD) and significance on surface texture. The relationships among cutting force, tool path, and surface profile are discussed in detail. Furthermore, the proposed systematic modelling approach is verified by cutting trials, which provide the coincident results of the surface topography and areal power spectral density (APSD)
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
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