This article deals with the issue of computer vision on a rolling mill. The main goal of this article is to describe the designed and implemented algorithm for the automatic identification of the character string of billets on the rolling mill. The algorithm allows the conversion of image information from the front of the billet, which enters the rolling process, into a string of characters, which is further used to control the technological process. The purpose of this identification is to prevent the input pieces from being confused because different parameters of the rolling process are set for different pieces. In solving this task, it was necessary to design the optimal technical equipment for image capture, choose the appropriate lighting, search for text and recognize individual symbols, and insert them into the control system. The research methodology is based on the empirical-quantitative principle, the basis of which is the analysis of experimentally obtained data (photographs of billet faces) in real operating conditions leading to their interpretation (transformation into the shape of a digital chain). The first part of the article briefly describes the billet identification system from the point of view of technology and hardware resources. The next parts are devoted to the main parts of the algorithm of automatic identification—optical recognition of strings and recognition of individual characters of the chain using artificial intelligence. The method of optical character recognition using artificial neural networks is the basic algorithm of the system of automatic identification of billets and eliminates ambiguities during their further processing. Successful implementation of the automatic inspection system will increase the share of operation automation and lead to ensuring automatic inspection of steel billets according to the production plan. This issue is related to the trend of digitization of individual technological processes in metallurgy and also to the social sustainability of processes, which means the elimination of human errors in the management of the billet rolling process.
In this paper is presented part of the results from grant project, which is concern the area of primary cooling in continuous steel casting device, especially crystallizer's surface quality. It was presented part results from crystallizer's defect catalogue and Methodology of their evaluation. In terms of this paper was presented a method to evaluate narrow crystallizer's desk qualities, which are dismantled when they are in maintenance department. In this paper is described proposal and development of monitoring and information system for support of crystallizer maintenance control and increase quality of processes which are performing basic functions of monitoring and localization of crystallizer's desks, crystallizer lifetime prediction and prediction of blank defects. Target of the system is to optimize preventive maintenance of crystallizers desks, with utilizing maximum count of relevant data from technological and maintenance processes. The Department of Automation and Computing in Metallurgy, VSB-TU Ostrava has long dealt with the challenge of life of technical systems. In the frame of this solution it is solved an area of metallurgical objects life. One of these objects is the mold of the continuous steel casting device. The current solution results shows that is necessary to use knowledge, not based only on objective qualitative knowledge, so then knowledge subjective and heuristics. The results are the constructions processes using effective model structures, the same principles which utilize human experts. This approach to solving complex systems leading to deployment of systems based on artificial intelligence methods.
This article is designed to determine the life of technical objects, in particular molds. Specifying lifetime of technical objects are followed by an explanation of standards and in particular standard EN 61649:2008 Weibull analysis. Further detail is given technical characteristics of the molds. Within the two models have been developed mold life-the first based on analytical expressions of the degradation mechanism transferred to the shape of the marginal probability of failure with the application of Weibull distribution, the second model using artificial neural networks based on the frequency dependence of individual brands of cast steel and mold profile of the average value measured at each system levels MKL 100/420. Reliability and maintenance control systems gives us an analysis of the maintenance actions, failure analysis, costs analysis spent to made actions and spare parts. It gives the answers to questions, which would be without a complex look over the data derived hardly-if the given crystallizer's desks can be used in operation or if is necessary to make its renovation. On history basis of previously actions or another historical data the maintenance control system gives sufficient information for correct decision make. Thanks to recently made maintenance data collection the base for maintenance planning is improving. Introduction of reliability control system and maintenance further help to increase availability and serviceability of devices from the reason of lowering unplanned time delays, breakdowns and increase device's service life.
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