Accelerated Additive Manufacturing: Digital Discovery of a New Process Generation

Introduction

Additive Manufacturing (AM) by Laser Powder Bed Fusion (LPBF) has the potential to revolutionize future product development, design, and supply chains. Since the underlying multi-scale physics are not well understood, its potential cannot presently be exploited.

Challenges in LPBF

Sub-optimal process conditions lead to severe defects on different scales, rendering parts unsuitable for use. Critically, known regimes of stable processing go along with very low build rates, i.e., very high costs compared to other processes. This limits LPBF to selected high-value applications such as medical devices but prohibits applications in mass production where it otherwise could allow for entirely new technologies.

Project Goals

ExcelAM aims at the digital discovery of novel high-throughput process regimes in LPBF, to increase build rates by at least one order of magnitude.

Computational Modeling

Computational modeling would be perfectly suited for this purpose since it allows for:

1. Observing physics that are not accessible to measurement.
2. Studying novel process technologies that are not feasible with existing hardware.

Unfortunately, existing computational tools are by far not powerful enough, given the complexity of LPBF.

Methodologies Development

Therefore, ExcelAM will develop novel game-changing methodologies, grouped into two main classes:

1. High-Fidelity Multi-Physics Models: Novel high-fidelity multi-physics models will be developed, capturing the complex multi-scale nature of LPBF. These are combined with cutting-edge high-performance computing schemes, allowing for predictions on unprecedented time spans and system sizes.

2. Data-Based Learning Approaches: Novel data-based learning approaches will be developed to enrich the physical models with process data, while exploiting the manifold of existing data as effectively as possible.

Impact

Based on these cutting-edge tools, ExcelAM will push the limits of LPBF. Moreover, by making them publicly available, ExcelAM will help scientists and practitioners in the field of production engineering and beyond to face the technological challenges of the 21st century.