Holistic methodology using computer simulation for optimisation of machine tools

A combination of optimisation algorithms is used to achieve a fast and accurate way of solving the complex task to optimise the complete machine tool. Genetic algorithms, gradient based algorithms and more traditional hands on engineering are used for solving the optimisation problem. Post processing and data mining is suggested as a way of extracting as much information as possible from optimisation results with the aim to increase the knowledge about the studied system. An important conclusion is that the virtual machine should support the decision making in product development, not replace the product developers as regards decision making. Virtual machine concepts supporting optimisation of machine tools have been developed in earlier work. The virtual machine concept is a tool that can describe the behaviour of a machine tool while considering the interaction between mechanics of the machines and the control system. Considerable amount of work has been done proving the concept and showing the potential of such a design tool in different contexts. Several studies have shown the potential of using the virtual machine concept, although, no work has been found that is exploring the potential of a full optimisation study. The aim of this work is to show the potential of the virtual machine concept in an optimisation study of the complete machine tool, including the mechanical system, parameters in the control system, the NC‐code as well as choice of servo and drive systems. An efficient optimisation strategy is presented, making it possible to solve the complex optimisation problem within a reasonable amount of time.