  We present a novel computational paradigm for process design in manufacturing
processes that incorporates simulation responses to optimize manufacturing
process parameters in high-dimensional temporal and spatial design spaces. We
developed a differentiable finite element analysis framework using automatic
differentiation which allows accurate optimization of challenging process
parameters such as time-series laser power. We demonstrate the capability of
our proposed method through three illustrative case studies in additive
manufacturing for: (i) material and process parameter inference using partial
observable data, (ii) controlling time-series thermal behavior, and (iii)
stabilizing melt pool depth. This research opens new avenues for
high-dimensional manufacturing design using solid mechanics simulation tools
such as finite element methods. Our codes are made publicly available for the
research community at
https://github.com/mojtabamozaffar/differentiable-simulation-am.
