An experimental approach to characterize the performance of PCD and PCBN tools in milling nano Al-8081-Zr/Mg/TiO2 metal matrix composites using multi-sensor data fusion

Abstract

To prevent the quality of the finished product from declining, precision manufacturing procedures need reliable cutting tool wear detection. Cutting tool material, workpiece attributes, cutting conditions and conditions, and excessive cutting forces creating vibrations causing chatter impacting tool wear finally leading to tool failure all have an impact on tool performance. This paper presents a multisensory data fusion approach that assigns sensors at nearfield sites during the machining process to monitor the tool condition. The study investigates the performance of polycrystalline diamond (PCD) and Polycrystalline Cubic Boron Nitride (PCBN) cutting tools during the machining of nano metal matrix composites reinforced with Zr/Mg/TiO2 (15%). The method correlates signal features with experimental results to provide a reliable empirical approach to monitor the cause of tool flank wear and displacement, leading to failure. The experimental investigation it is found the cutting forces showed significant effect on flank wear affecting surface finish and tool life. Tool performance was successful monitoring and predicted instantly based on the signature analysis of vibrations and forces during machining helped accurately analyzed factors affecting tool wear at uncertain cutting conditions using FDA analysis. The study provides insights into the PCD and PCBN tools’ performance characteristics.