Papers by Author: Amir Abdullah

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Authors: Abbas Pak, Amir Abdullah
Abstract: Force and specific energy are important factors in all abrasive machining operations especially in creep-feed grinding of hard materials. They have a high influence on the wheel wear, grinding accuracy, grinding temperature and surface integrity. This paper investigates the effect of grinding technological parameters on grinding force and specific energy in up-cut creep-feed grinding of cemented tungsten carbide with 20% cobalt using a resin-bonded nickel-coated diamond wheel. It was observed that increase of feed rate resulted in grinding force increase and specific energy decrease. Increased wheel-peripheral speed resulted in minor decrease of grinding force and specific energy increase.
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Authors: Alireza Hajialimohammadi, Saeed Ahmadisoleymani, Amir Abdullah, Omid Asgari, Foad Rezai
Abstract: Constant volume transparent test combustion chambers are extensively used for investigating injection and fuel burning properties of various combustion engines. Their configuration depends on the engine type and the research purpose. Material of components, shape and dimensions of the chamber and its parts, ease of use, accessibility, sealing and safety of the assembly are the parameters needed to be considered in designing the test cell. This paper explains, structural design of a test combustion chamber and its optical windows using finite element analysis of ANSYS 12.0 software for bearing high pressure variations and thermal shocks of combustion. It was designed for conducting CNG direct injection study on direct injection SI CNG engines for maximum design pressure of 100 bars. Optical diagnostic methods and high speed photography through quartz windows are used for the jet and flame developments. Satisfactory test results of the fabricated system proved that the finite element method can successfully be employed for design of such a system.
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Authors: Sina Eskandari, Behrooz Arezoo, Amir Abdullah
Abstract: Thermal errors of CNC machines have significant effects on precision of a workpiece. One of the approaches to reduce these errors is modeling and on-line compensating them. In this study, thermal errors of an axis of the machine are modeled by means of artificial neural networks along with fuzzy logic. Models are created using experimental data. In neural networks modeling, MLP type which has 2 hidden layers is chosen and it is trained by backpropagation algorithm. Finally, the model is validated with the aid of calculating mean squared error and correlation coefficients between outputs of the model and a checking data set. On the other hand, an adaptive neuro-fuzzy inference system is utilized in fuzzy modeling which uses neural network to develop membership functions as fuzzifiers and defuzzifiers. This network is trained by hybrid algorithm. At the end, model validation is done by mean squared error like previous method. The results show that the errors of both modeling techniques are acceptable and models can predict thermal errors reliably.
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