FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition

2026-04-17

AI Summary

This work addresses the challenge of part distortion in directed energy deposition (DED) manufacturing, which arises from thermomechanical coupling effects and is difficult to mitigate due to the high computational cost of conventional finite element simulations that hinder rapid design iteration. To overcome this limitation, the authors propose FLARE, a novel framework that constructs an affine structure in the weight space of an implicit neural field, enabling efficient prediction of post-cooling displacement fields by mapping geometric and process parameters to affine combinations of network weights. Requiring only a small number of simulation samples, FLARE achieves high data efficiency and demonstrates strong extrapolation capabilities. Experimental results show that FLARE significantly outperforms existing baselines in both in-distribution and extrapolation scenarios, confirming its accuracy and generalization performance.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part. Thermo-mechanical finite element simulations are widely used to estimate these effects, but their computational cost and the complexity of accurately capturing DED physics limit their use in design iteration and process optimization. This paper introduces FLARE (Field Prediction via Linear Affine Reconstruction in wEight-space), a data-efficient surrogate modeling framework for predicting post-cooling displacement fields in DED from geometric and process parameters. We develop a predefined-geometry DED simulation workflow using an open-source finite element framework and generate a dataset of simulations with varying geometry, laser power, and deposition velocity. Each simulation provides full-field displacement, stress, strain, and temperature data throughout the manufacturing process. FLARE encodes each simulation as an implicit neural field and regularizes the corresponding neural-network weights so that they follow the affine structure of the input parameter space. This enables prediction of unseen parameter combinations by reconstructing network weights through affine mixing of training examples. On this DED benchmark, the method shows improved accuracy compared to baseline methods in both in-distribution and extrapolation settings. Although the present study focuses on DED displacement prediction, the proposed affine weight-space reconstruction framework offers a promising approach for data-efficient surrogate modeling of physical fields.

Problem

• Directed Energy Deposition
• Thermo-mechanical simulation
• Displacement field prediction
• Surrogate modeling
• Dimensional accuracy

Innovation

• surrogate modeling
• implicit neural field
• affine weight-space reconstruction
• directed energy deposition
• data-efficient learning