No abstract

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.

In this experimental study mainly focused the thermal property such as thermal resistance and heat transfer analysis for the air gap variations provided in between two glasses on window for the domestic purposes. Easily available surrounding air considered as the experimental mater because of its arrangements. Thermal resistance of inside, outside and glasses (both glasses have same thermal conductivity such as 0.78 W/mK) all are maintained as constant throughout the investigation. But only air gap distance increased from 2 mm to 14 mm with gradual increase focused. The thermal conductivity of air considered as 0.026 W/mK. Thermal resistance and heat transfer impact with respect to the air gap between glasses were identify with the help of graphical representations.