Intelligent Grinding

Abstract

Grinding is a process that still today largely depends on the skill and experience of the operator. For grinding, no generalized computer-aided manufacturing tool exists as for milling, and the grinding machine manufacturers have their proprietary process planning tools for the path planning. The success of a grinding process furthermore depends on a suitable conditioning of the grinding wheel and not only on the appropriate selection of the process parameters. Skilled operators are, on the one hand, capable of setting up the process in shorter time than their less skilled colleagues and with immediate success. Artificial intelligence, driven by sufficiently increased computational performance, is increasingly capable of handling manufacturing processes, particularly as these processes become more complicated and experience-based. Therefore, extending the machine’s ability towards what today is the task of the operator, namely process planning feeding the vision of “operator integrated” is a breakthrough in zero defect manufacturing and first part right for grinding processes. The paper conceptualizes an intelligent grinding machine that uses ontologies, applies rule-based planning tools, makes use of physical as well as autonomous modeling, and is capable of learning. Moreover, the processes of grinding and dressing are fully monitored so that a self-learning ability is provided. Learning is fed from different sources, from monitoring, from other machines requiring filters built on physical models, and from the operator with the ability to deal with incomplete, unstructured, and unreliable data. From this the way, how such a machine communicates with operators must be completely different than today. Research results and literature are provided to discuss the different aspects like machine state monitoring, process monitoring, and parameter selection for an optimized grinding process.