State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China.
School of Mechanical and Electrical Engineering, Central South University, Changsha, 410083, China.
Sci Rep. 2022 Aug 1;12(1):13159. doi: 10.1038/s41598-022-15935-8.
Injection molding is one of the most promising technologies for the large-scale production and application of polymeric microfluidic chips. The multi-objective optimization of injection molding process for substrate and cover plate on protein electrophoresis microfluidic chip is performed to solve the problem that the forming precision is difficult to coordinate because of the cross-scale structure characteristics for chip in this paper. The innovation for this research is that an optimization approach and a detailed fuzzy rule determination method are proposed in multi-objective optimization for protein electrophoresis microfluidic chip. In more detail, firstly, according to the number and level of process parameters, the orthogonal experimental design is carried out. Then, the experiments are performed. Secondly, the grey relational analysis (GRA) approach is employed to process the response data to gain the grey relational coefficient (GRC). Thirdly, the grey fuzzy decision making method which combines triangular membership function and gaussian membership function is adopted to obtain the grey fuzzy grade (GFG). After that, the optimal scheme of process parameters was predicted by the grey fuzzy grade analysis. Finally, the superiority of Taguchi grey fuzzy decision making method are verified by comparing the results of original scheme, optimal scheme and prediction scheme. As a result, compared with the original design, the residual stress of substrate plate (RSS), residual stress of cover plate (RSC), warpage of substrate plate (WS), warpage of cover plate (WC) and replication fidelity of microchannel for substrate plate (RFM) on the prediction scheme for Taguchi grey fuzzy decision making method were reduced by 32.816%, 29.977%, 88.571%, 74.390% and 46.453%, respectively.
注塑成型是大规模生产和应用聚合物微流控芯片最有前途的技术之一。针对蛋白质电泳微流控芯片基板和盖板的注塑成型过程进行多目标优化，解决了芯片跨尺度结构特征导致的成型精度难以协调的问题。本研究的创新之处在于提出了一种优化方法和详细的模糊规则确定方法，用于蛋白质电泳微流控芯片的多目标优化。更详细地说，首先根据工艺参数的数量和级别进行正交试验设计，然后进行实验。其次，采用灰色关联分析（GRA）方法对响应数据进行处理，得到灰色关联系数（GRC）。第三，采用结合三角形隶属度函数和高斯隶属度函数的灰色模糊决策方法来获得灰色模糊等级（GFG）。然后，通过灰色模糊等级分析预测工艺参数的最优方案。最后，通过比较原始方案、最优方案和预测方案的结果，验证了田口灰色模糊决策方法的优越性。结果表明，与原始设计相比，预测方案中基板的残余应力（RSS）、盖板的残余应力（RSC）、基板的翘曲（WS）、盖板的翘曲（WC）和基板微通道的复制保真度（RFM）分别降低了 32.816%、29.977%、88.571%、74.390%和 46.453%。
