# Unveiling Processing--Property Relationships in Laser Powder Bed Fusion: The Synergy of Machine Learning and High-throughput Experiments

arXiv: https://arxiv.org/abs/2409.00248 · HTML: https://mlsanity.com/paper/2409.00248/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 84 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Latent Map Gaussian Processes for Mixed Variable Metamodeling](https://mlsanity.com/paper/2102.03935/) (2021)
- [Multi-Fidelity Cost-Aware Bayesian Optimization](https://mlsanity.com/paper/2211.02732/) (2022)
- [Nested cross-validation when selecting classifiers is overzealous for most practical applications](https://mlsanity.com/paper/1809.09446/) (2018)
- [GP+: A Python Library for Kernel-based learning via Gaussian Processes](https://mlsanity.com/paper/2312.07694/) (2023)
- [Markov Chain Monte Carlo with Gaussian Process Emulation for a 1D Hemodynamics Model of CTEPH](https://mlsanity.com/paper/2406.01599/) (2024)

## Built on by (most cited first, 3 of 3)

- [A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion](https://mlsanity.com/paper/2503.16667/) (2025)
- [Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining](https://mlsanity.com/paper/2506.12516/) (2025)
- [On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization](https://mlsanity.com/paper/2608.13793/) (2026)

## Abstract (arXiv)

Achieving desired mechanical properties in additive manufacturing requires many experiments and a well-defined design framework becomes crucial in reducing trials and conserving resources. Here, we propose a methodology embracing the synergy between high-throughput (HT) experimentation and hierarchical machine learning (ML) to unveil the complex relationships between a large set of process parameters in Laser Powder Bed Fusion (LPBF) and selected mechanical properties (tensile strength and ductility). The HT method envisions the fabrication of small samples for rapid automated hardness and porosity characterization, and a smaller set of tensile specimens for more labor-intensive direct measurement of yield strength and ductility. The ML approach is based on a sequential application of Gaussian processes (GPs) where the correlations between process parameters and hardness/porosity are first learnt and subsequently adopted by the GPs that relate strength and ductility to process parameters. Finally, an optimization scheme is devised that leverages these GPs to identify the processing parameters that maximize combinations of strength and ductility. By founding the learning on larger easy-to-collect and smaller labor-intensive data, we reduce the reliance on expensive characterization and enable exploration of a large processing space. Our approach is material-agnostic and herein we demonstrate its application on 17-4PH stainless steel.