For its good mechanical, thermal, and chemical property, ceramic materials are widely used in construction, chemical industry, electric power, communication and other fields. However, due to its particularity and complex production process, quality problems usually occur, of which the most common one is surface defects. For ceramic products, the defects are usually small and complicated, and manual methods are difficult to ensure the accuracy and speed of detection. Relevant researchers have proposed a variety of machine vision-based ceramic defect detection methods, but these methods still need to break through in solving the key problems of ceramic surface glaze reflection, complex detection environment, low algorithm efficiency and low real-time performance. To this end, this article reviews the application status of machine vision on ceramic surface defect detection in recent years, summarizes and analyzes the existing non-destructive testing (NDT) technology method, and points out the main factors that affect the development of ceramic surfaces defect detection technology and puts forward the corresponding solutions.

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Glazing deposition rate model is a key issue of the off-line trajectory planning for robotic spray glazing. In order to perform the automatic trajectory planning, achieve the accuracy control of glaze film thickness, a modeling method of the glazing thickness deposition rate fitted by the artificial neural network is presented. Based on the experimental data of the glazing thickness, the model is fitted by using the Bayesian normalization and LM optimization algorithm respectively. In contrast with two kinds of simulated results, it shows two models are consistent with the experimental data. However, compared with LM optimization algorithm, Bayesian normalization algorithm converges faster and more accurate. So Bayesian normalization algorithm is better than LM optimization algorithm in fitting the model. The method is feasible to control the precision of glazing thickness. This paper provides a specific theoretical and methodological support for robotic offline programming in ceramic spray glazing manufacturing.

To improve measurement accuracy of safety belt pin, the size-sorting system which mainly includes a machine vision subsystem and a driving subsystem, is researched. The machine vision subsystem includes a industry camera, a double telecentric lens and a backlight. Image process steps that have sub-pixel accuracy are presented. The driving subsystem is designed with Atmega128L MCU. The state machine is designed for safety pin control logic. The two sub systems communicate with RS232 serial port. The test shows that the system has a maximum accuracy error of 0.05mm, repeatability error of 0.013mm.

Nowadays, CAD/CAE system has been widely used in manufacturing industries. Since every CAD/CAE system is fixed in the different platforms and has its own individual information model and data structure, the transfer of design information from one system to another often causes incompatible and/or incomplete data. The ability to vertically and horizontally share data becomes an increasing concern for manufactures. This paper presents a framework, which is oriented to concurrent engineering, to support the integration of the information flow between CAD and CAE systems. A prototype system based on this framework is developed. Common methods for information integration and two kinds of distributed structures (Client/Server and Browser/Server) are also discussed in this paper