Online tool condition monitoring implementation using fractal analysis

Abstract

Milling is a high-performance technology, which makes it possible to efficiently machine complex shaped surfaces. The quality of the machined component is directly influenced by the condition of the milling cutter. It is essential to monitor the cutting tool condition during the machining process in order to control the quality of the machined part and avoid equipment downtime. Real time tool condition monitoring is a pillar of intelligent manufacturing, especially in the highly automated production lines. The objective of monitoring technique is to send a warning before tool wear reaches a certain threshold to avoid degrading the finished surface and losing the final part's dimensional accuracy. An integrated tool condition monitoring system with minimal processing time and expertise is one of the crucial areas that require further investigation. In the present study, indirect method of tool condition monitoring has been developed in order to monitor tool condition in real time and respond quickly as needed. This method is performed by correlating relevant sensor signals to the tool wear states. In this study, sensors that did not interfere with the cutting process were used. Motor-related parameters were also introduced as the ideal choice due to their high sensitivity to cutting conditions and the avoidance of a pause during machining. Cutting forces and electric current signals related to the spindle during machining were found to be highly responsive to cutting conditions and to properly represent changes in tool condition. In this study, the spindle electric current signal was acquired using the internal sensor of the machine tool through a static synchronized action programming. In order to extract significant characteristic features of the signal, probability statistics theory may not be accurate enough to study tool wear when the evolution process exhibits a chaotic characteristic. The Chaos theory addresses this unpredictability in a system, and it uses fractal parameters to forecast any change in signal shape. In the present study, fractal analysis was introduced as an effective decision-making strategy with less processing time and expertise to extract information from the signal. Different machining operations and materials were conducted to validate this tool condition monitoring method. Orbital drilling, trimming and contour milling were used to machine a multi-material stack (titanium alloys (Ti6Al04V)/ Carbon Fiber Reinforced Plastics (CFRP)), a CFRP material as well as the AISI 5140 steel material. Fractal analysis was applied to the cutting force and spindle electric current signal to predict any unexpected turbulence in the signal and to establish a single value in the machine tool as a warning before tool wear becomes severe. The effectiveness of fractal analysis as a decision-making method in tool condition monitoring was demonstrated in this study.

Date	6 Dec 2022
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Jean-François Chatelain (Supervisor) & Marek Balazinski (Co-supervisor)