# Additive manufacturing process design with differentiable simulations

arXiv: https://arxiv.org/abs/2107.10919 · HTML: https://mlsanity.com/paper/2107.10919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity](https://mlsanity.com/paper/2302.00032/) (2023)

## Abstract (arXiv)

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.