A brief overview of artificial intelligence applications in machining

Abstract

At the present, almost every branch of science and engineering uses the artificial intelligence (AI) tools. In machining, AI techniques have achieved several applications. Almost certainly, the most remarkable of them are cutting processes monitoring and control, cutting parameters modeling, processes optimization and computer-aided process planning. Due to their complex nature, machining processes are very difficult to be understood, modeled or simulated. Relationships between cutting variables are highly non-linear and poorly known; therefore the current analytical models, in spite of their theoretical relevance, have a very limited practical application. Furthermore, using conventional techniques, such as statistical and numerical methods, for modeling cutting processes, have not shown a completely satisfactory effectiveness. On the contrary, AI, which is based on simulate human and nature behavior, has the ability of deal with complex problems. Some AI techniques, such as neural networks, fuzzy logic and neuro-fuzzy systems, can model complex relationships and extract information from raw noisy data. By the other hand, other AI tools, such as evolutionary computation, simulating annealing, and ant-colony algorithm, allow carrying out the optimization of complex systems, where conventional techniques fail. In the following sections, a very brief overview of the main applications of AI in machining is carried out. This does not pretend to be and exhaustive review but also a simple outline of current state-of-the art and future trend in this branch. In accordance with this, only review papers or very representative and recent works are cited.