Health prediction of a Multi-point cutting tool using ML
· INTRODUCTION: Tool faults have attracted research interest. Tool wear is affected by several factors which influence monitoring cutting tools to track defects and avoid them. Several approaches are considered like ‘Online/Indirect’, where acquisition of parameters like Cutting Speed, Tool Temperature, Feed Rate, Forces, using suitable Transducers & ‘Off-line/ Direct’, where vision-based techniques like Laser Scatter Pattern, Scanning Electron Microscopy are seen. The online approach is suitable for knowledge-based ML learning schemes. Several investigations related to the study of Vibration Signals to review Tool Health were given. · METHODOLOGY: The diagram shows the methodology used; the tool conditions were categorized using 6 algorithms which are: 1. Decision tree (J48) classifier- A decision tree serves as an efficient tool in the domain of judgmental investigation. It is a special algorithm that performs a dual role i.e. while con...