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In recent years, artificial intelligence played an important role in machine tool automation. Artificial neural networks, as one of the artificial intelligence algorithms, has superiority in representing the relation between the inputs and outputs of the multi-variable system. Hence, it can be applied to sophisticated operations such as grinding operation. The aim of this research is to use artificial neural networks as the brain of grinding machine controller. The target of this controller was to achieve the desired workpiece surface roughness under grinding wheel surface topography variations. The core of the system consists of two multi-layers feed forward artificial neural networks based on back error propagation learning algorithm. The first one was used for process design to achieve the desired surface roughness. It extracts suitable process variables such as grinding wheel speed and feed rate. The second one monitors the cutting operation using sensors' readings. It extracts the different controlling decisions; these are accept the process, redesign the process or start dressing operation under automatic control. According to these decisions, a PC master control program generates the appropriate control codes and sends them to the machine controllers to take the required actions.

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Shape recognition is an important task in the field of computer vision. Many algorithms were developed to extract features from captured images of valuable objects. The traditional polygonal approximation algorithm (PAA) is a well-known algorithm in shape analysis for digital image processing and pattern recognition. The main objective of this algorithm is to convert a set of connected points in digitized images into a list of fitted lines, which represent a two-dimensional polygon. The main problem with this algorithm is that it lacks the ability to extract curved objects from the digitized images. In this paper, a new algorithm called an enhanced polygonal approximation algorithm (EPAA) is developed, based on the PAA, to extract both straight and curved features from digitized images of twodimensional products. The EPAA is capable of classifying line and arc segments as well as circles. A description of the EPAA and an example of its application in the field of production engineering are presented.