A multi-performance prediction model based on ANFIS and new modified-GA for machining processes

In the recent years, there has been an increasing interest in presenting a comprehensive modeling technique to predict machining performances in different processes. As well, this paper proposes a new hybrid technique anchored in adaptive network-based fuzzy inference system (ANFIS) and modified genetic algorithm (MGA) to model the relationship between machining parameters and multi performances. MGA which employs a new type of population is effectively applied as the training algorithm to optimize the modeling parameters, finding appropriate fuzzy rules and membership function in the model. In the proposed MGA, a list of parameters is randomly considered as a solution and a collection of experiences 'in optimizing the solution' is utilized as population. To show the effectiveness of the presented model, it is applied to wire electrical discharge machining (WEDM) process for predicting material removal rate and surface roughness. The prediction results are compared with the most common prediction modeling techniques based on ANN and ANFIS-GA. The statistical evaluation results reveal that the ANFIS-MGA considerably enhances accuracy of the optimal solution and coverage rate.