A Machine Learning Framework to Analyze and Optimize the Print Parameters of Direct Ink Writing (DIW) Systems

Direct ink writing (DIW) is an extrusion based additive manufacturing technique that has gained significant popularity in a number of application areas due to its versatility in printable materials and part designs. However, analyzing and optimizing the print parameters of DIW systems remains a laborious, tedious, and resource intensive process. To further compound this challenge, the printability regime is intricate, complex, and depends on both the printing parameters and material/ink formulation. As a result, the criterion for assessing printability of DIW materials remains through extensive experimentation. Here, we present a data-driven machine learning (ML) framework for rapidly analyzing and optimizing the print parameters to meet a designer's desired printability metrics. The data-driven ML framework leverages techniques in image analysis, transfer learning, and probabilistic modeling for generating printability maps and uses Gaussian process regression to predict printability in the parameter space. This work represents a major advancement in accelerating the materials development and design cycle for DIW and offers transferable lessons for other manufacturing technologies. Prepared by LLNL under Contract DE-AC52-07NA27344.