ABSTRACT
The basic sense of Artificial Intelligence (AI) is to develop artificial understanding capability, human-like intelligence, and decision-making potential in artificial machines. Metal matrix composites (MMCs) are becoming very famous due to their various properties such as conductivity, wear resistance, strength, and hardness. In this work, aluminum as matrix and TiB2 as reinforcing material-based MMC is fabricated by the stir casting technique. The tool wear rate (TWR) has been chosen as a response while performing electric discharge machining (EDM). The Box–Behnken design is applied for modeling the TWR. Teaching–learning-based optimization (TLBO) and cuckoo search algorithm (CSA) have been chosen as optimization techniques for optimization of TWR. A comparison between TLBO and CSA has been made (considering the equal number of calculations). From response surface methodology (RSM), it has been found that the linear model is the best for TWR. Analysis of variance (ANOVA) suggested that the peak current is the greatest important parameter for minimizing TWR. Through this study, one can estimate tool cost during EDM on Al-TiB2 composite, so mechanization will improve.
