Grain size optimization for nanoscale tungsten carbide cobalt using master sintering curve model

Sintering behavior of nanoscale WC-Co was investigated based on master sintering curve model. Densification and grain growth were mapped for grain size assessment and optimization. Parameters for model fitting were acquired by experimental approaches. Using the sensitivity analysis with respect to processing parameters, effects of key conditions on final properties were investigated. Based on the derived objective function, an optimization algorithm to minimize grain size under the given target density was developed. For example using our nanoscale WC-Co, grain size of 332 nm was the minimum size when the compaction pressure of 565 MPa and 97% of sintered density were the processing constraints. Using the constructed mapping function, the achievable minimum grain size under constrained processing condition is predicted and the further requirements for the powder are suggested to achieve the specific target grain size. In this study, we use the master sintering curve model and optimization methods to identify process conditions, such as compaction pressure and sintering temperature, to tailor specified tolerances of the fabricated components.