journal article·2019-05

Influence of machining parameters on surface quality during high speed edge trimming of carbon fiber reinforced polymers

Mohamed Slamani, Jean-François Chatelain, Hossein Hamedanianpour

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Abstract

CVD diamond-coated carbide tools could provide an economical alternative for trimming CFRPs components compared to their PCD tools counterpart. Nevertheless, there are still some technical issues to understand related to wear resistance and surface quality. In this work, a CVD tool with six straight flutes was used to investigate the relationship between surface roughness, surface damage, tool wear, cutting force and cutting parameters during the high speed trimming of CFRPs. Statistical techniques for identifying and selecting the best cutting conditions for CVD tool are developed. In terms of tool wear, results show that the best operational condition to minimize the tool wear is achieved at lower feed rates and higher cutting speeds. Experimental results show also that a 0° ply orientation represents the worst case and produces the maximum tool wear. Furthermore, a strong correlation between the feed force and the tool wear was observed. It was found that the surface roughness decreases as a reciprocal function of cutting length. This decrease was due to the matrix burning/sticking and the thermal damage related to the low thermal conductivity of CFRP. In such situation, Ra becomes inappropriate indicator for roughness evaluation. On the other hand, it wasn’t seen any type of delamination or fiber pull-out on the trimmed surface of all coupons for the three tool life tests. Accordingly, delamination can be avoided using high fixture rigidity, high quality of CFRP laminates, a suitable cutting tool and stable operational conditions.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced machining processes and optimization · Tribology and Wear Analysis · Advanced Machining and Optimization Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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