A data-driven modeling approach for digital material additive manufacturing process planning

Current analytic approaches for additive manufacturing (AM) of digital materials, which are also known as multi-material composites, are either based on physical modeling or computer simulations, which limit the accuracy and efficiency, and do not allow for the new material exploitation. Moreover, decisions on material choices and process settings are made based on trial-and-error experiments. Extensive labor process planning and unpredictable manufacturing results remain to be significant challenges and hinder the advancement of digital material AM technology greatly. Hence, to enable reliable online manufacturing process planning for existing and new digital materials, a data-driven modeling approach is presented in this paper. The manufacturing process planning for digital material AM on a diverse set of materials with various properties is automated by using a data-driven approach. To illustrate the effectiveness of the approach, the drop-on-demand jetting process is studied. Effects of various process parameters on printing results are analyzed.