Helical Milling of CFRP/Ti6Al4V Stacks Using Nano Fluid Based Minimum Quantity Lubrication (NF-MQL): Investigations on Process Performance and Hole Integrity

Materials (Basel). 2023 Jan 6;16(2):566. doi: 10.3390/ma16020566.

Abstract

The structural components in the aeronautical industry require CFRP/Ti6Al4V stacks to be processed together, which results in poor hole integrity due to the thermal properties of the materials and challenges related to processability. These challenges include quality variation of the machined holes because of the limitations in process properties. Therefore, a novel solution through helical milling is investigated in the study using nano fluid based minimum quantity lubrication (NF-MQL). The analysis of variance shows, for Ti6Al4V, eccentricity (PCR = 28.56%), spindle speed (Ti) (PCR = 42.84%), and tangential feed (PCR = 8.61%), and for CFRP, tangential feed (PCR = 40.16%), spindle speed (PCR = 28.75%), and eccentricity (PCR = 8.41%) are the most significant parameters for diametric error. Further on, the rise in the circularity error is observed because of prolonged tool engagement at a higher value of tangential feed. Moreover, the surface roughness of Ti was reduced with an increasing percentage of MoS2 in the lubricant. The spindle speed (37.37%) and lubricant (45.76%) have a potential influence on the processing temperature, as evident in the analysis of variance. Similarly, spindle speed Ti (61.16%), tangential feed (23.37%), and lubrication (11.32%) controlled flank wear, which is critical to tool life. Moreover, the concentration of MoS2 decreased edge wear from ~105 µm (0.5% concentration) to ~70 µm (1% concentration). Thorough analyses on process performance in terms of hole accuracy, surface roughness, processing temperature, and tool wear are carried out based on the physical science of the process for cleaner production. The NF-MQL has significantly improved process performance and hole integrity.

Keywords: CFRP; Ti6Al4V; milling; minimum quantity lubrication; sustainability.

Grants and funding

The authors acknowledge the University of Engineering and Technology, Lahore for providing funding, facilities, and related resources to carry out the work. The authors wish also to thank “Self-reconfiguration for Industrial Cyber-Physical Systems based on digital twins and Artificial Intelligence. Methods and application in Industry 4.0 pilot line (SELFRECO)” (Grant Number: PID2021-127763OB-100) supported by Ministry of Science and Innovation (MICINN) of Spain.