Keynote Speaker (by invitation only) at Digital Twin Symposium 2019, presented by T. DebRoy
The practice of qualifying metallic parts by trial and error with expensive printing equipment and feed stock material currently confine the products to a niche market where the high product cost and the delay in the qualification process are tolerated by the business plan. A digital twin of a 3D printing machine consisting of mechanistic, control and statistical models of 3D printing, machine learning and big data can reduce the volume of trial and error testing, reduce defects and shorten time between the design and production. Key building blocks of a digital twin of additive manufacturing include a set of mechanistic models that calculate temperature and velocity fields, cooling rates, solidification parameters, deposit geometry, defects, thermomechanical variables and printability. They are important because the scales of variation of cooling rates and other important parameters vary widely, and their values are highly sensitive to the selection of printing process variants and process variables. This presentation will provide a roadmap for the development of a first-generation digital twin that will make the expanding knowledge base of additive manufacturing usable in a practical way by scientists, technologists and business leaders to solve many of the current scientific, technological and commercial problems.