In the current scenario, aluminium metal matrix composites (AMMCs) are most important as an auspicious high class of materials. Metal matrix composites are emerging as very much endowed materials exclusively in the fields of automotive, aerospace, electrical and electronics for their numerous applications and technical challenging properties. In the present work, a courageous is perfect to prepare and compare the hardness and tensile properties of LM25-graphite (Gr) and LM25-graphite (Gr)-Ferrous oxide (Fe 3 O 4 ) hybrid composites. The composites were primed to make use of stir casting process in which quantity of reinforcement is speckled from 4 wt% of Gr and 3 wt% of Fe 3 O 4 . The prepared composites are characterized by micro-structural studies and tensile properties were estimated as per the standards. The microphotographs of the composites revealed the reasonably homogeneous supply of the particles in composites with a group at a small number of places. The dispersed graphite and Fe 3 O 4 in LM25 alloy contributed to enhancing the tensile strength of the composites. The SEM of the illustration specifies the homogeneous supply of the reinforcement particles in the matrix without any annulled.
The aerospace aluminium alloy AA7050 was reinforced with Al2O3 of average particle size 5 m in this study using the stir casting method. To eliminate surface imperfections, AA7050/Al2O3 composites with varied weight percentages (0, 2, 4, 6) were manufactured, and wear tests on composites were carried out utilizing a pin-on-disc apparatus that varied load, velocity, temperature, and weight %. The tensile and hardness tests were carried out at a high temperature. The inclusion of particles enhances wear resistance by establishing a mechanically mixed layer (MML), according to the findings. The wear resistance at 300°C was 100% higher in comparison with resistance at 150°C. Because of the Orowan strengthening and Hall–Petch effect, the tensile strength and hardness of composites enhanced. Temperature, tracked by the weight % of strengthening powders, was the most important factor that influences the wear resistance of the composites. The findings showed that the material properties of AA7050/4wt%Al2O3 at 150°C and AA7050/2wt%Al2O3 at 300°C are superior than base alloy.
Manufacturing simulation is an encouraging research area in resent decade. Creation or development of better simulation tool or technique is one of the major intension in manufacturing simulation. In resent research most of the manufacturing processes are simulated successfully. But some processes are not yet simulated effectively, especially automatic air conditioning (AC) system or refrigeration system. The automatic AC system for the passenger vehicle are not yet effectively simulated. Hence in this paper a machine learning technique is adopted for the effective prediction of parameter of automatic AC system. The proposed system uses k-nearest neighbour technique for the prediction of parameter will less error and high accuracy. The proposed system is implemented using MATLAB and its performance is compared with the support vector machine and ANN in terms of mean square error and accuracy. The proposed technique outperforms the conventional technique and suggest that the k-nearest neighbour become the most suitable technique for the modelling and performance analysis of automatic AC system.
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