Additive manufacturing process design with differentiable simulations

TL;DR

This paper introduces a differentiable finite element analysis framework enabling optimized additive manufacturing process design through simulation responses, demonstrated in three case studies involving material inference, thermal control, and melt pool stabilization.

Contribution

It presents a novel differentiable simulation approach for manufacturing process optimization, allowing high-dimensional parameter tuning using automatic differentiation.

Findings

Successful material and process parameter inference from partial data

Effective control of thermal behavior over time

Stabilization of melt pool depth in additive manufacturing

Abstract

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.