A classification methodology of intelligent manufacturing systems

In this recent years, the advent and introduction of Artificial Intelligence in the area of manufacturing engineering has given a new perspective of Intelligent Manufacturing System (IMS). Many research activities in this area have been conducting on various directions to form a fundamental concept of IMS, e.g., the intelligent process controlling systems, machine tools condition monitoring system, real-time machining state detection using multi-axis force sensing, fail-safe system, tele-machining, and so on. Nevertheless, a consensus definition of IMS is not existent. Generally, IMS is known as the autonomous or near-autonomous system that can acquire all relevant information through sensing, render decisions for its optimum operation, and implement control functions to achieve the objectives of its manufacturing tasks, including the overhead functions. Though there are many research activities on IMS, most of the them are still implemented only in the laboratory since there are many different conditions between laboratory and real industrial environments. IMS technology in Thailand is very new. Few or may be none of industries are paying attention to it. It is very difficult to correctly forecast the future development of IMS in Thailand. There is only an expectation that IMS will show its role in the near future because of the rapid changed of technology and business competition. At the beginning phase of implementation of intelligent systems to industries, especially for Thailand’s, it is very important to introduce a proper direction a proper direction and philosophy of IMS to them. This research proposes a classification methodology for evaluating the levels of intelligence of the machines, cells, lines, areas, factories, and the entire manufacturing system. The proposed methodology is devised to provide a clear boundary for the Intelligent Manufacturing Systems and also can broadly answer how and which direction a company should do to introduce the intelligent manufacturing to their organization.