Papers by Author: Quan Yu

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Authors: Quan Yu, Ke Sheng Wang
Abstract: 3D vision based quality inspection has been widely applied in manufacturing industry. Product quality is retrieved from the point cloud obtained using 3D vision methods. Generally, three sorts of quality inspection methods can be selected according to the specific requirements. This paper studied a combining quality inspection method for the quality inspection of a plastic molded part with multiple geometry shapes. Only incomplete point cloud is available because of the characteristics of the part material. Shape fitting and template matching methods are applied for deformation detection with respect to different shapes. Experiment result shows the proposed method can accomplish the quality inspection task for the part with multiple geometry shapes.
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Authors: Ke Sheng Wang, Vishal S. Sharma, Quan Yu
Abstract: Looking at the high rates of production and the steep competition in the world market, it becomes quite essential that the fault control is done in a very efficient way. This article presents a summary on the maintenance, the monitoring techniques, and the diagnosis methods for the condition based maintenance of machine tools. The paper initially gives a brief introduction on the condition based maintenance of machine tools. In the next part, the various methods for the monitoring are discussed followed by the models for data mining. The paper concludes that most of the techniques have their own advantages and drawbacks, so a careful selection of the techniques is needed to form a proper monitoring system.
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Authors: Lelija Stupar, Quan Yu, Ke Sheng Wang
Abstract: This paper describes two methods for the industrial quality inspection: Supervised classification algorithm Chi-Square Automatic Interaction Detector (CHAID) and unsupervised clustering algorithm Self-Organizing Map (SOM). The classification and clustering are modelled in IBM software SPSS. Models’ functioning is illustrated on a wheel assembly geometric features inspection. The classifying accuracies are compared for the two methods. CHAID has shown better classifying ability than SOM, while SOM can be used to improve quality of predictor values, and therefore classifiers accuracy.
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