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To overcome the limitation of the Traditional machining, Non-Traditional machining has immersed as potential machining techniques. However, with the advent of new technologies related to machining and ignoramus development in the field of material science the evolution of hybrid technology came in to being. During the process of Non-Traditional machining huge amount of data gets generated. In order to satisfy the objective function during the process various optimization techniques are applied. Machine learning is one such growing technology which is used to mine knowledge from data. It is mend from automatic learning from various data set. Statistical Learning Algorithms are advanced tools and models which are currently used, rather than relying on classical and trial and error methods, in order to enhance quality of engineering processes and productivity. Optimization techniques are commonly used as a part of machine learning algorithm. Some learning algorithms are inspired from nature for the development of novel problem solving techniques.
Artificial intelligence based random search algorithms, namely, Genetic Algorithm, Ant Colony Optimization and so forth have found their applicability in solving various Non-Traditional Machining problems of complex nature.
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