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Stress measurement and its variation are key problems in the operating performance of materials. Stress can affect the material properties and the life of components. There are several destructive and nondestructive techniques that are used to measure stress. However, no single nondestructive testing (NDT) technique or method is satisfactory to fully assess stress. This paper presents an NDT data fusion method to improve stress measurement. An aluminum alloy 2024 specimen subjected to stress simulation is nondestructively inspected using pulsed eddy current and ultrasonic techniques. Following these nondestructive examinations, the information gathered from these two NDT methods has been fused using a suitable fuzzy combination operator. The results obtained with these processes are presented in this paper and their efficiency is discussed. It is shown that the fusion of NDT data with a suitable fuzzy operator can be adequate to improve the reliability of stress measurements.

Tool life is an important indicator of the milling operation in manufacturing process. Studies and analyses of milling process are usually based on three main parameters composed of cutting speed, feed rate and depth of cut. The aim of this study is to discover the role of these parameters in tool life prediction in milling operations by using artificial neural networks and Taguchi design of experiment. Machining experiments were performed under various cutting conditions by using sample specimens. A very good agreement between predicted model and experimental results was obtained. The correlation between the estimated and experimental data was 0.96966 for train and 0.94966 for test. Index Terms-Artificial neural networks, Face milling, Taguchi Design of Experiment, Tool life prediction.I.