Study of Quality Function Deployment Model Based on Artificial Neural Network with Optimization Techniques

Abstract

In perceptible world the advanced technique is to progress the model of quality function deployment (QFD) procedure with the consumer consummation level in the industry with the help of artificial neural network (ANN) with inspired optimization methods. QFD is a productive instrument for refining customer serenity in the new product planning (NPP) procedure. QFD precedes the voice of the consumer from the commencement of artifact growth and organizes it through the firm. The QFD procedure forecasts the number of percentages (%) in the dissimilar voices of the consumers. In our projected work, consumer level, learning rate, momentum rate, and correlation value are utilized to assess the customer consummation rate in QFD. On the basis of these parameters, the ANN model is used in this hidden layer, and neuron differs for each feature for training procedure-changed algorithms like particle swarm optimization (PSO), harmony search (HS) optimization, and genetic algorithm (GA) are utilized. In this procedure, HS algorithm is used to optimize the hidden layer and neuron, though it achieved better performance when compared with the other methods. The optimization is transpired and the artificial network structure has intended the model of QFD.