TY - JOUR
AU - Zailani, Zainal Abidin
AB - Manufacturing turbine blades using metal injection molding (MIM) is a complex process that requires precise control over parameters to achieve high dimensional accuracy. Inadequate management of shrinkage, frozen volume, and volume filled leads to dimensional deviations, resulting in defects or reduced performance of turbine blades in operation. Optimizing these response factors ensures reliable production and high-quality turbine blades. This study investigates the influence of process parameters in metal injection molding by evaluating their significance and interaction. A three-level central composite design (CCD) approach-based response surface methodology analysis was applied to statistically specify the effect of important numerical and categorical process variables: mold temperature, melt temperature, injection time, flow rate on the critical response process output variables concerning product quality, namely shrinkage, frozen volume, and volume filled. By using a face-centered design, a total of 30 simulation data was fitted. Analysis of variance (ANOVA) was then performed to assess the significance of factors and their interactions at a 95% confidence level (p < 0.05). Subsequently, empirical models were developed and rigorously validated against the simulation results. The optimum process parameters of the metal part were characterized as follows: mold temperature of 15 °C, 138 °C of melt temperature, 2.5 s of injection time, and 94 cm3/s flow rate. The results are expected to advance the metal injection molding industry by providing valuable references and enhancing the understanding of the optimization process.
TI - Optimization of process parameters in metal injection molding for turbine blade using response surface methodology
JF - The International Journal of Advanced Manufacturing Technology
DO - 10.1007/s00170-025-15496-w
DA - 2025-04-01
UR - https://www.deepdyve.com/lp/springer-journals/optimization-of-process-parameters-in-metal-injection-molding-for-3o2Uhxrw26
SP - 5899
EP - 5912
VL - 137
IS - 11
DP - DeepDyve
ER -