Machine learning approach to machinability analysis

摘要

Optimisation and automation of determination of cutting conditions in operation planning depend significantly on availability of reliable machinability data and knowledge. In order to improve and automate the tool selection and determination ofcutting parameters in operation planning we have to re-formulate and generalise the existing machinability knowledge. In the paper the existent machinability data base was analysed by the use of machine learning methodology. A multi-stage experiment hasbeen carried out, comprising (1) preparatory phase in which manual construction of higher level attributes and grouping of similar learning examples to obtain more consistent decision trees was performed, and (2) learning relations between workpiecematerials to be machined, cutting tool features and cutting conditions. Within the learning process several decision trees have been synthesised predicting tool features, cutting geometry and cutting parameters from a set of attribute values. Theinvestigation has revealed the extended insight into the machinability domain, as well as the possible knowledge synthesis regarding workpiece material to be machined and cutting tool as a bottom-line in operation planning for NC-programming and automated process planning.