LAUSR

The bone plates used in surgery to assist in fracture healing are often manufactured by metal injection molding (MIM) using a feedstock material consisting of metal powder and polymer binder. However, if the local powder concentration is too low or uneven, black lines may be formed, which impair the product appearance. Furthermore, if the melding temperature is too low, it can lead to meld lines and reduced mechanical properties. Accordingly, this study combines mold flow analysis simulations with the single-objective Taguchi robust design method to determine the MIM processing conditions that optimize the powder concentration and melding temperature. Grey relational analysis (GRA) is then used to establish the processing conditions that simultaneously optimize both MIM objectives. It is found that the processing conditions determined through GRA provide a significant improvement over the original design; however, the experimental outcomes are poorer than those achieved through the single-objective Taguchi experiments since the melt temperature effect suppresses that of all the other processing conditions. Consequently, a robust multi-criteria optimization (RMCO) technique is employed to improve the optimization outcome by identifying the dominant factors in the MIM process and fixing them at optimal levels to redesign the Taguchi experiments to optimize the non-primary factors. It is shown that the RMCO method eliminates interference between the multiple factors and hence provides an improved multi-objective optimization outcome. Overall, the integrated framework proposed in this study advances the optimization of the MIM process for bone plates and leads to improved product quality and performance.