| Laws of mass, momentum, and energy conservation, in combination with the number of operations required to manufacture a component, fundamentally limit rates at which Computer Numerical Control (CNC) machine tools can perform material removal operations. To circumvent these limits, it is common practice to configure multiple machine tools in parallel for high volume applications.;In the automotive industry, as demands for inexpensive and affordable automobiles rise, production demands from existing manufacturing infrastructures also rise. Common practice suggests, as demand exceeds infrastructure supply capabilities, hardware infrastructure limitations must grow to accommodate increased demand. Typically, this involves large capital investments in additional hardware and floor space.;To avoid investing capital in excess hardware, in preparation of future demands, an investigation was launched to find methods of reducing non-value-adding time for computer numerical control machine tools as used in large scale internal combustion engine cylinder head production. Of primary focus was the development of algorithms used to model cycle times and machine tool control systems along with the development of an advanced genetic algorithm, with probabilistic mutation and selection, to be used for optimizing machine tool paths.;A description, derivation, and analysis of these algorithms is provided along with a comparison between productivity enhancements generated by a genetic algorithm based optimizer and existing tools used in industry. It was proven that a genetic algorithm based optimization solver can be used to improve tool paths such that existing infrastructures can be grown in place to meet both existing and future demand, as well as reduce overall costs. |
