Polyimide (PI) and polyetheretherketone (PEEK) are superior high‐performance thermoplastics being extensively used in the fields of fiber‐reinforced polymer composites. However, there is very limited literature addressing the machining behavior of the PI and PEEK composites. The present paper aims to conduct a comparative study into the machining characteristics of these two representative high‐performance thermoplastic matrix composites under varying cutting conditions. Machinability aspects of the carbon/PI and carbon/PEEK thermoplastic composites were evaluated in terms of drilling forces, machining temperatures, delamination damage, surface morphologies, hole dimensional accuracy and tool wear. The results indicate that the carbon/PEEK composites generally show a much poorer machinability than the carbon/PI composites in terms of higher drilling forces, higher cutting temperatures, larger delamination extents and excessive tool wear. Since the carbon/PEEK composites exhibit certain ductility leading to the continuous chip formation, the cut hole surface morphologies and dimensional accuracy are much better than those gained in the carbon/PI composites. Both the cutting speed and the feed rate affect significantly the drilling forces and the resulting delamination damage. The fundamental wear mechanisms of drilling carbon/PI composites are abrasion wear in the form of edge rounding and slight chip adhesion, while for the carbon/PEEK composites, they are abrasion, serious chip adhesion because of the high drilling temperatures promoted at the drill‐work interface and catastrophic failures of coating peeling and edge fracture.

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On buffer zone construction, the rasterization-based dilation method inevitably introduces errors, and the double-sided parallel line method involves a series of complex operations. In this paper, we proposed a parallel buffer algorithm based on area merging and MPI (Message Passing Interface) to improve the performances of buffer analyses on processing large datasets. Experimental results reveal that there are three major performance bottlenecks which significantly impact the serial and parallel buffer construction efficiencies, including the area merging strategy, the task load balance method and the MPI inter-process results merging strategy. Corresponding optimization approaches involving tree-like area merging strategy, the vertex number oriented parallel task partition method and the inter-process results merging strategy were suggested to overcome these bottlenecks. Experiments were carried out to examine the performance efficiency of the optimized parallel algorithm. The estimation results suggested that the optimization approaches could provide high performance and processing ability for buffer construction in a cluster parallel environment. Our method could provide insights into the parallelization of spatial analysis algorithm.

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