Expert Systems in Special Machinery: Increasing the Productivity of Processes in Commissioning

Abstract

Due to the megatrend globalization, special machinery is gaining significance for the capital goods sector. Characterized by the fulfillment of individual customer requirements, companies in special machinery have to deal with very specific and technologically complex tasks. Hence, managing information and knowledge becomes vital for a company's competitive ability, notably when it comes to expert knowledge. The characteristics of special machines leads to iterative processes for problem solving and thereby, increase lead times significantly. The more technologically complex a machine is, the more scattered the expert knowledge, meaning that many different experts need to be consulted before solving a problem. Up to now, in scientific literature, there has been little discussion about the challenges of special machinery and practical solutions regarding an implementation of technical intelligence in a special machinery environment. Therefore, the goal of this paper is to give an example of how an expert system can be applied to special machinery surroundings and thus, increases productivity. A Bayesian network forms the basis of the system as it allows efficient inference algorithms and reasoning under uncertainty, despite its ability to describe complex dependencies. The expert systems capability has been proven in industrial laser manufacturing.