A sensor framework for combined data streams and in-situ characterization of machining processes

Abstract Machining vibrations is a critical phenomenon in the industry as they negatively affect the quality and tool-life. One common avoidance strategy for machining vibrations is the fine-tuning of process parameters, thus leading to longer production time. Our research addresses this challenge and uses different streams of data to classify problematic processes. Data streams of machining parameters, tool position, loads, vibration sensors, together with process plan data and cutting tool usage information, are visualized. Experiments are performed to derive classification criteria. These results are then used to observe vibrations in a five-axis machining center for further process adjustment.