journal article·2018-08

Heat partition in dry orthogonal cutting of unidirectional CFRP composite laminates

Fu-ji Wang, Jun-wei Yin, Jian-wei Ma, Bin Niu

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Abstract

Abstract Carbon fiber reinforced polymer (CFRP) components can be generally prepared near-net-shape, however, they still need machining after manufacturing to meet the geometrical accuracy with excellent surface quality required for assembly. Due to the cutting temperature is prone to exceed the glass-transition temperature of the resin, the irreversible chemical and mechanical degradation are therefore difficult to avoid. It becomes a crucial challenge to eliminate the particular thermal effect on the composite machining process. In comparison with the temperature measurement, the analytical model not only can reveal the physical essence of thermal effect, but also can predict the temperature field distribution to provide the reasonable cutting parameters. In particular, a key parameter for calculating the cutting temperatures is the heat partition ratio. However, the research work on this issue was rarely found. In this paper, a fiber orientation-based analytical model was developed to predict the heat partition ratio based on the classical Hertz contact theory. The finite element model was also built with the validation of the experimental measurement from the thermal imaging tests. The results suggest the heat partition ratio is mainly determined by the cutting parameters. Moreover, the fiber orientations have a remarkable impact on this ratio. Due to the heat partition ratio is considerably larger in a CFRP workpiece than the cutting tool, more heat energy was transferred to the CFRP during machining. Therefore, a small depth of cut leads to a reduced tendency for thermal effect on the CFRP composites.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced machining processes and optimization · Mechanical Behavior of Composites · Laser Material Processing Techniques

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

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