Managing tool-wear is an important issue associated with all material removal processes. This paper deals with the application of two nature-inspired computing techniques, namely, artificial neural network (ANN) and (in silico) DNA-based computing (DBC) for managing the toolwear.\ud Experimental data (images of worn-zone of cutting tool) has been used to train theANNand, then, to perform the DBC. It is demonstrated that the ANN can predict the degree of tool-wear from a set of tool-wear images processed under a given procedure whereas the DBC can identify the degree of similarity/dissimilar among the processed images. Further study can be carried out while solving other complex problems integrating ANN and DBC where both prediction and pattern-recognition are two important computational problems that need to be solved simultaneously

Researchers have evaluated a great number of monitoring techniques in order to control the surface condition of ground parts. Piezoelectric diaphragms of lead zirconate titanate are used in many fields, but these sensors are not common in the monitoring of the machining processes. This paper proposes a method for monitoring the workpiece surface condition (normal grinding and burn) by using a piezoelectric diaphragm and feature extraction techniques. A comparison is made with a conventional acoustic emission sensor, which is a traditional sensor in the monitoring of the machining processes. Grinding tests were performed in a surface-grinding machine with Society of Automotive Engineers (SAE) 1045 steel and cubic boron nitride (CBN) grinding wheel, where the signals were collected at 2 MHz. The workpieces were thoroughly analyzed through visual inspection, surface roughness and hardness measurements, and metallographic analyses. Study on the frequency content of both signals was carried out in order to select bands closely related to the workpiece surface condition. Digital filters were applied to the raw signals and features were extracted and analyzed. The root mean square values filtered in the selected bands for both sensors presented a better fitting to the linear regression, which is highly desirable for setting a threshold to detect burn and implementing into a monitoring system. Also, the basic damage index results show an excellent behavior for grinding burn monitoring for both sensors. The method was verified by using a different grinding wheel, which clearly shows its effectiveness and demonstrates the potential use of the low-cost piezoelectric diaphragm for grinding burn monitoring

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In the manufacturing systems, one of the most important issues is to estimate the rest of cutting tool life under a given\ud cutting conditions as accurately as possible. In fact, machining efficiency is easily influenced by the kind of tool selected at\ud each cutting process. One of the most complex problems for tool selection is that of estimating the tool life under a given cutting\ud conditions as accurately as possible. As the quality of the cutting tool is directed related to the quality of product, the level of tool\ud wear should be kept under control during machining operations. In order to monitor the tool wear development during machining\ud processes, the interface chosen between the working procedure and the computer was a digital image of the cutting tool detected by\ud an optical sensor. Images, however, are not homogeneous. Images with standard size and pixel density were produced elaborating\ud tool images files obtained during machining tests. This paper is focused on a procedure for the processing of cutting tool images\ud detected during tests. A methodology to design and optimized artificial neural networks for automatic tool wear recognition using standard images of cutting tool is proposed

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