Neuro-Genetic Optimization of LDO-fired Rotary Furnace Parameters for the Production of Quality Castings

Abstract

The rising demand for high quality homogenous castings necessitate that vast amount of manufacturing knowledge be incorporated in manufacturing systems. Rotary furnace involves several critical parameters like excess air, flame temperature, rotational speed of the furnace drum, melting time, preheat air temperature, fuel consumption and melting rate of the molten metal which should be controlled throughout the melting process. A complex relationship exists between these manufacturing parameters and hence there is a need to develop models which can capture this complex interrelationship and enable fast computation. In the present work, we propose a generic approach where the applicability and effectiveness of neural network in function approximation is used for rapid estimation of melting rate and they are integrated into the framework of genetic evolutionary algorithm to form a neuro-genetic optimization technique. A neural network model is trained with the experimental results. The results indicate that the heuristic converges to better solutions rapidly as it provides the values of various process parameters for optimizing the objective in a single run and thus assists for the improvement of quality in development of sound parts.