Defect detection is an essential link in the fabric production process. Due to the diversity of patterns and scarcity of defect samples for colour‐patterned fabrics, reconstruction‐based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. Among them, unsupervised reconstruction models based on variational autoencoders (VAEs) have been shown to be effective. However, there is a problem of posterior collapse in the process of modelling parametric distributions of continuous variables by VAEs. Therefore, VAE‐based defect detection methods for colour‐patterned fabrics usually produce ambiguous reconstruction results, thereby affecting the defect detection performance. In this article, an attention‐based vector quantisation variational autoencoder (AVQ‐VAE) is proposed for colour‐patterned fabric defect detection. The method adopts autoregressive modelling of discrete variables to avoid the posterior collapse problem of traditional VAEs, and utilises attention mechanism to enhance the feature representation ability of the model. AVQ‐VAE consists of encoder, embedding space, decoder and attention mechanism. The encoder is used to map the input image into multiple feature vectors. Vector quantisation in embedding space is used for discretisation and autoregressive modelling of feature vectors. A decoder is used to decode discrete variables into images of the same size as the original input. Furthermore, an attention mechanism is used to capture channel and spatial correlations, which help the model focus on important information by adaptively recalibrating feature maps. Experimental results on public datasets demonstrate that the proposed method is robust and effective for colour‐patterned fabric defect detection.
This study mainly constructs a finite element analysis model. The temperature, energy partition and changes of FC300 gray cast iron workpiece during dry grinding are investigated. Thermal load and mechanical deformation of workpiece during grinding can leave residual stress on the surface, which has a considerable impact on the fatigue properties of the workpiece. In the experimental part, different grinding wheel speeds (1200, 900, 600, 400 rpm), workpiece speeds (1000, 3000, 5000, 10000 mm/min), and grinding depths (10, 20 mm/min) were measured μm) Changes in residual stress under conditions. A moving heat source model and a multi abrasive model are established to simplify the condition of the grinding wheel contacting the workpiece. The comparison of material residual forces measured by X-ray diffraction measurements shows that the finite element analysis model proposed in this paper has good reliability. Finally, this model is used to analyze the effects of grinding wheel speed, workpiece speed, and grinding depth on the residual stress on the workpiece surface, and to understand the possible causes of residual stress during grinding.
In the electrical discharge machining process, preliminary research has been able to effectively estimate machining accuracy in response to its long machining history and high discharge frequency characteristics. However, when processing abnormalities occur, it is difficult to identify them since the electrical discharge process contains multiple processing parameters, which increases the cost of repair or loss afterwards. Therefore, the question concerning how to monitor the abnormality of the discharge process in real time represents the main purpose of this research. This research develops an EDM process abnormal diagnosis system. First, the data are stored in a circular array to speed up the processing time, and the coefficient of variation feature is added, which has effectively extracted the abnormal characteristics. In terms of diagnostic methods, the composite voting model established by neural networks, random forests, and XGB-RF (extreme gradient boosting applying RF) can provide robust diagnostic results. Finally, through the Node-RED webpage and MQTT agreement, it can provide the ability to monitor machine abnormalities in real time. Through refinement and optimization of the previous research results, this study took the electrical discharge machining diamond grinding wheel as an example, and developed a warning that can be issued within 3 min when abnormalities (abnormal patterns such as polycrystalline diamond high protrusions) occur, with an accuracy of 93% and a false positive rate. The abnormal diagnosis ability is less than 0.2%. Therefore, the online abnormality monitoring system developed by this research institute will be able to provide online abnormality diagnosis for electrical discharge machining.
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