Abstract
Carbon fiber-reinforced plastics (CFRPs) find many applications given their superior properties. These materials are usually formed using a near-net-shape method that requires secondary machining, such as drilling and trimming, after molding. Industrial robots are becoming increasingly popular machining tools in industries exhibiting high demand for CFRPs. However, it remains challenging to achieve high dimensional accuracy when using such robots and dynamic performance is poor. We experimentally investigated the dynamic properties of the tool tip according to the dominant robot posture during CFRP secondary machining. Based on the results, multi-layer perceptron models were developed to predict the dominant natural frequency and dynamic stiffness of the tool tip. The minimum and maximum mean absolute percentage errors were 1.99 and 7.94, respectively; the error changed markedly with robot posture. Our models improved CFRP robotic machinability. Experimentally, the delamination rates of drilled holes decreased by 15 % and 75 % in terms of length and area, respectively, and the trimmed surface roughness improved by 27 %.
Published Version
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