Quantifying Thermal Model Accuracy in PBF-LB/M Using Statistical Similarity Tests Against Thermographic Measurements

Published

December 12, 2025

Author(s)

Vijaya Holla, Brandon Lane, Jesse Redford, Philipp Kopp, Stefan Kollmannsberger

Abstract

Numerical simulation models for laser powder bed fusion of metals (PBF-LB/M) vary in complexity and fidelity, ranging from high-fidelity models that capture melt pool dynamics to simplified models suited for part-scale temperature predictions and process optimizations. Validation against experimental data is essential to build confidence in their predictive capabilities. However, for in-situ thermographic measurements, a direct comparison is challenging due to the data's size and the multi-scale nature of the process. Similarities must be analyzed at different spatial and temporal scales based on the model's fidelity and its intended application. For example, agreement between a thermal simulation and measurement in a steady-state scenario does not guarantee accuracy during transient phases. Statistical similarity measures provide a quantitative means to assess model-measurement agreement, highlighting regions of high and low similarity. In this work, we validate a thermal simulation model, discretized using the space-time finite element method, against thermographic camera measurements using various similarity measures and evaluate their applicability to PBF-LB/M. We also propose a multi-scale similarity assessment approach tailored to model fidelity and application.

Citation

Integrating Materials and Manufacturing Innovation

Pub Type

Journals

Download Paper

https://doi.org/10.1007/s40192-025-00430-5

Local Download

Keywords

thermal simulation, Model-measurement comparison, Similarity tests, Space-time finite element method, thermography, additive manufacturing, laser powder bed fusion

Citation

Holla, V. , Lane, B. , Redford, J. , Kopp, P. and Kollmannsberger, S. (2025), Quantifying Thermal Model Accuracy in PBF-LB/M Using Statistical Similarity Tests Against Thermographic Measurements, Integrating Materials and Manufacturing Innovation, [online], https://doi.org/10.1007/s40192-025-00430-5, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960461 (Accessed December 13, 2025)