Towards intelligent CFRP composite machining: Surface analysis methods and statistical data analysis of machined fibre laminate surfaces

Abstract

Many carbon fibre reinforced polymer composite parts need to be edged trimmed before use to ensure both geometry and mechanical performance of the part edge matches the design intent. Measurement and control of machining induced surface damage of composite material is key to ensuring the part retains its strength and fatigue properties. Typically, the overall surface roughness of the machined face is taken to be an indicator of the amount of damage to the surface, and it is important that the measurement and prediction of surface roughness is completed reliably. It is known that the surface damage is heavily dependent on the fibre orientation of the composite and cutting tool edge condition. This research has developed a new ply-by-ply surface roughness measurement methods using optical focus variation surface analysis and image segmentation for calculating areal surface roughness parameters of a machined carbon fibre composite laminate. Machining experiments have been completed using a polycrystalline diamond edge trimming tool at increasing levels of cutting edge radius. Optical surface measurement and µ-CT scanning have been used to assess machining induced surface and sub-surface defects on individual fibre orientations. Statistical analysis has been used to assess the significance of machining parameters on Sa (arithmetic mean height of area) and Sv (areal magnitude of maximum valley depth) areal roughness parameters, on both overall roughness and ply-by-ply fibre orientations. Empirical models have been developed to predict surface roughness parameters using statistical methods. It has been shown that cutting edge degradation, fibre orientation and feed rate will significantly affect the cutting mechanism, machining induced surface defects and surface roughness parameters.