Lin Chao-Ming, Yen Po-Yu, Tan Chung-Ming
Department of Mechanical and Energy Engineering, National Chiayi University, Chia-Yi 600355, Taiwan.
Department of Vehicle Engineering, WuFeng University, Chia-Yi 621303, Taiwan.
Polymers (Basel). 2024 Nov 22;16(23):3241. doi: 10.3390/polym16233241.
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.
手术中用于辅助骨折愈合的骨板通常采用金属注射成型（MIM）工艺制造，使用由金属粉末和聚合物粘结剂组成的原料。然而，如果局部粉末浓度过低或不均匀，可能会形成黑线，影响产品外观。此外，如果熔合温度过低，会导致熔合线并降低机械性能。因此，本研究将模流分析模拟与单目标田口稳健设计方法相结合，以确定优化粉末浓度和熔合温度的MIM加工条件。然后使用灰色关联分析（GRA）来确定同时优化MIM两个目标的加工条件。研究发现，通过GRA确定的加工条件相比原始设计有显著改进；然而，实验结果比单目标田口实验的结果要差，因为熔体温度效应抑制了所有其他加工条件的效应。因此，采用稳健多准则优化（RMCO）技术，通过识别MIM过程中的主导因素并将其固定在最佳水平，重新设计田口实验以优化非主要因素，从而改善优化结果。结果表明，RMCO方法消除了多个因素之间的干扰，从而提供了改进的多目标优化结果。总体而言，本研究提出的综合框架推进了骨板MIM工艺的优化，并提高了产品质量和性能。
