Expert-guided optimization for 3D printing of soft and liquid materials

Additive manufacturing (AM) has rapidly emerged as a disruptive technology to build mechanical parts, enabling increased design complexity, low-cost customization and an ever-increasing range of materials. Yet these capabilities have also created an immense challenge in optimizing the large number of process parameters in order achieve a high-performance part. This is especially true for AM of soft, deformable materials and for liquid-like resins that require experimental printing methods. Here, we developed an expert-guided optimization (EGO) strategy to provide structure in exploring and improving the 3D printing of liquid polydimethylsiloxane (PDMS) elastomer resin. EGO uses three steps, starting first with expert screening to select the parameter space, factors, and factor levels. Second is a hill-climbing algorithm to search the parameter space defined by the expert for the best set of parameters. Third is expert decision making to try new factors or a new parameter space to improve on the best current solution. We applied the algorithm to two calibration objects, a hollow cylinder and a five-sided hollow cube that were evaluated based on a multi-factor scoring system. The optimum print settings were then used to print complex PDMS and epoxy 3D objects, including a twisted vase, water drop, toe, and ear, at a level of detail and fidelity previously not obtained.增材制造（Additive Manufacturing，AM）已迅速崛起为一种颠覆性机械零件制造技术，可实现更高的设计复杂度、低成本定制化，且可使用的材料种类持续扩充。然而这些优势也带来了巨大挑战：需优化海量工艺参数以制备高性能零件。针对软质可变形材料，以及需采用实验性打印工艺的类液态树脂的增材制造而言，这一挑战尤为突出。为此，本研究开发了一种专家引导优化（Expert-guided Optimization，EGO）策略，为液态聚二甲基硅氧烷（Polydimethylsiloxane，PDMS）弹性体树脂的3D打印探索与优化工作提供系统性框架。该策略包含三个步骤：第一步为专家筛选环节，用于确定参数空间、影响因子及因子水平；第二步为爬山算法搜索环节，在专家划定的参数空间内搜寻最优参数组合；第三步为专家决策环节，通过引入新影响因子或更新参数空间来进一步优化当前最优解。研究将该算法应用于两个校准件——空心圆柱与五面空心立方体，并基于多因子评分体系对其进行性能评估。随后将最优打印参数用于打印复杂的PDMS与环氧树脂3D构件，包括扭曲花瓶、水滴模型、脚趾及耳朵模型，实现了此前未达成的细节还原度与保真度。