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The process of sorting marble plates according to their surface texture is an important task in the automated marble plate production. Nowadays some inspection systems in marble industry that automates the texture and shade classification tasks are too expensive and are compatible only with specifictechnological equipment in the plant. In this paper a new approach for design of adaptive classification system of marble tiles with similar textures is proposed, aiming at high classification accuracy, applying simple processing and application of only standard devices. It is based on simple image preprocessing, on adaptive training of MLP neural network (MLP NN) with marble histograms and implementation of the algorithm in a Programmable Logic Controller (PLC) for real-time operation. The experimental test results when recognizing marble textures with added motion blur and different illuminations are represented and discussed. The performance of the modeling technique is assessed with different training and test sets. The classification accuracy results are presented and analyzed. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the control algorithm of tool monitoring system (TCM) that can detect tool breakage on-line by using a neural decision system. In proposed system, the sensors are used to collect the signals during milling through a data acquisition module. The signal processing module analyses the machining signals for extracting features sensitive to tool wear. The monitoring module uses a strategy to analyse the signals from the sensors and to provide reliable detection of tool and process failures. A neural network is used in TCM as a decision making system to predict the condition of the tool. In this study, the cutting forces are used as the indicator of the tool flank wear variation. [ABSTRACT FROM AUTHOR]