A novel hybridization of artificial neural network and moth-flame optimization (ANN–MFO) for multi-objective optimization in magnetic abrasive finishing of aluminium 6060

In industries, the impact of magnetic abrasive finishing (MAF) is well recognized in achieving accurate surfaces, minimizing imperfections and providing high-quality surface finish especially in micro- and nano-range. In the same context, this paper presents a hybrid optimization method for multi-objective optimization that combines back-propagation artificial neural network (ANN) with a newly developed nature-inspired optimization algorithm, i.e. moth-flame optimization (MFO) algorithm, which is used to predict optimal process parameters of magnetic abrasive finishing process. The back-propagation ANN is gradient-based local search algorithm, while MFO is a rapid and global search algorithm for controlling the generation of candidate solutions. Initially, the MFO algorithm is integrated for training the neural network with unknown set of weights and biases and in the process overcomes the shortcomings of traditional training algorithms having slow convergence and local optima stagnation. Further, the MFO algorithm is also employed to predict the number of neurons in hidden layers. Finally, the proposed hybrid ANN–MFO methodology establishes a multi-objective optimization model in terms of important process parameters for optimizing the MAF process conditions. The optimization of different MAF process parameters is performed using MFO algorithm by incorporating the adaptive search procedure, which further enhances the exploration behaviour. Surface roughness, temperature of workpiece during the finishing operation and hardness of finished surface are investigated as the output response in the finishing of aluminium 6060. The design variables considered are working gap, abrasive weight, voltage and rotational speed. The results predicted by the proposed hybrid ANN–MFO algorithm provide effective and accurate process parameters to enhance surface finish and part quality that will be beneficial for real manufacturing environment.