One of the most important requirements of part manufacturing is the surface quality. This is so because the most important part is meeting the specific requirements of customers. The surface roughness is a leading indicator of the quality of the machined surface Parts. In the present work in an experimental study to achieve by application of Taguchi method to investigate the effect of three parameters, which known as cutting speeds of (45, 90, and 135 m/min), feed rate of (0.1, 0.2, and 0.3 mm/rev), and cut depth of (0.05, 0.1, and 0.15 mm) on performance measure of surface roughness (Ra). Thus to determine the optimal levels and to analyze the cutting parameter’s effect on the surface finish values by employing different method of Orthogonal array, S/N ratio, analysis of variance (ANOVA). During our work two models for prediction have been used. The first one is known as the method of regression analysis, and the second is the method of Adaptive - Neural Network (ANN) relying on practical results. The achieved results show that the estimation and prediction ability of neural networks is better than the regression analysis. Experimental results confirmed with optimal levels of the machining parameters which are clarified by using Taguchi optimization method. Also, the indicated results of the Taguchi’s method show its ability to improve the process.

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Muscle force estimation, is important in order to understand human control strategies, rehabilitation, motion analysis and human-machinery interfacing. To estimate the force, bio-signals analysis is used. Bio-signals enable signal recognition and analysis of muscle action and it is used to estimate muscle force. In this work, a method to estimate human muscle force is proposed. This model, which is based on Fuzzy Logic Theorem, is fast and easy to implement. Fuzzy Model process a raw of rectified smoothed electromyography (RSEMG) signal and extract muscle force. Our model is a general one, it can use for any muscles. To prove that, we used biceps brachii muscle of the left and right arms and two different arm muscles as validation. Our results, demonstrate that the new model improves the accuracy of muscle force estimation. This model can efficiently extract muscle force features from (EMG) signals. Our results showed that algorithmic regression exceeds 99%. The mean square error (MSE) results for Fuzzy Model was very small (MSE equal to 1.11x10-08).

In recent years, applications have been proven finite-element method (FEM) in metal-cutting operations to be effective process in the study of cutting and chip formation. In this study, the simulation results are useful for both researchers and machine tool manufacturers for improving the design of cutting parameters. Finite-element analysis (FEA) that used in this study of simulation the cutting parameters and tool geometries effects on the force and temperature in turning AISI 1040. The simulation parameters that used in this study are cutting speed (75 - 300 m/min),feed rate (0.2 mm/rev), cut depth (0.75-1.5 mm), and rake angle (0-20 °). The results of cutting forces were (240 – 520 N), the temperature were (300-420 °C), and the heat rate (14202.3-83772.8 W/mm3) on the cutting edge. The simulation process also show that the increase of cutting speed leads to decrease in the cutting forces, while it has increasing in temperature, and heat rate. Also, the results show that the increase of cutting depth associated increase the cutting force only.