Nanopowders provide better details for micro features and surface finish in powder injection molding processes. However, the small size of such powders induces processing challenges, such as low solid loading, high feedstock viscosity, difficulty in debinding, and distinctive sintering behavior. Therefore, the optimization of process conditions for nanopowder injection molding is essential, and it should be carefully performed. In this study, the powder injection molding process for Fe nanopowder has been optimized. The feedstock has been formulated using commercially available Fe nanopowder and a wax-based binder system. The optimal solid loading has been determined from the critical solid loading, measured by a torque rheometer. The homogeneously mixed feedstock is injected as a cylindrical green body, and solvent and thermal debinding conditions are determined by observing the weight change of the sample. The influence of the sintering temperature and holding time on the density has also been investigated. Thereafter, the Vickers hardness and grain size of the sintered samples have been measured to optimize the sintering conditions.

High speed steels (HSS) were used as cutting tools and wear parts, because of high strength, wear resistance, and hardness together with an appreciable toughness and fatigue resistance. Conventional manufacturing process for production of components with HSS was used by casting. The powder metallurgy techniques were currently developed due to second phase segregation of conventional process. The powder injection molding method (PIM) was received attention owing to shape without additional processes. The experimental specimens were manufactured with T42 HSS powders (59 vol%) and polymer (41 vol%). The metal powders were prealloyed water-atomised T42 HSS. The green parts were solvent debinded in normal n-Hexane at for 24 hours and thermal debinded at mixed gas atmosphere for 14 hours. Specimens were sintered in , gas atmosphere and vacuum condition between 1200 and . In result, polymer degradation temperatures about optimum conditions were found at and . After sintering at gas atmosphere, maximum hardness of 310Hv was observed at . Fine and well dispersed carbide were observed at this condition. But relative density was under 90%. When sintering at gas atmosphere, relative density was observed to 94.5% at . However, the low hardness was obtained due to decarbonization by hydrogen. In case of sintering at the vacuum of torr at temperature of , full density and 550Hv hardness were obtained without precipitation of MC and in grain boundary.

Defining the relationship between the quality of injection molded parts and the process condition is very complicate because of lots of factor are involved and each factor has a non-linearity. With the development of CAE(Computer Aided Engineering) technology, the estimation of volumetric shrinkage of injection mold parts is possible by computer simulation even though restricted application. In this research, the Taguchi method and Neural Network applied for finding optimal processing condition. The percent of volumetric shrinkage compared on each case and show neural network can be successfully applied.