Abstract
The objective of this study is to develop and optimize WC–Co cutting inserts with enhanced hardness–toughness balance and improved machining performance during turning of Inconel 718. Artificial Neural Networks (ANN) were employed to identify an optimal WC–Co composition and sintering temperature by correlating material composition with mechanical properties. The ANN-suggested composition was further reinforced with a small fraction (≤1 wt.%) of nanosized WC and consolidated using the Sinter-HIP process. The resulting cutting inserts were subjected to detailed microstructural characterization, mechanical property evaluation, and machining trials. The results show that WC–Co inserts reinforced with 0.5 wt.% nano-WC exhibit a refined microstructure and an improved hardness–fracture toughness combination, leading to a significant enhancement in machining performance. In turning experiments on Inconel 718, these inserts achieved nearly 180% longer tool life compared to the conventional WC–Co insert. The improved performance is attributed to effective ANN-guided material optimization combined with nano-scale reinforcement, which suppresses grain growth and enhances load-bearing capability. This study demonstrates that integrating data-driven material design with nano-reinforcement is an effective strategy for developing high-performance cutting tools for machining hard-to-cut high-temperature alloys.
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