aiqbench analysis
This research applies generative world models to predict and control laser melt pool dynamics in manufacturing, offering a solution where high-fidelity simulation is too slow for real-time decision-making. By leveraging differentiable latent dynamics, the model functions as a predictive control plant that optimizes laser schedules for unseen geometries without requiring extensive physical trials. This approach demonstrates how advanced machine learning can bridge the gap between planning through imagined futures and direct industrial deployment on heavy machinery.
Generative World Models Enable Predictive Control of Laser Melt Pool Dynamics
arXiv:2610.06250v1 Announce Type: new Abstract: World models, which learn how environments respond to actions, are emerging as a powerful paradigm for planning through imagined futures, transforming decision-making across games, robotics and autonomous driving. Bringing this capability to manufacturing could enable process decisions on timescales inaccessible to high-fidelity simulation. Here we introduce a generative world model for localized highly dynamic laser melt pool that predicts evolution from histories of temperature and phase morphology under candidate actions. Its generative latent dynamics capture the effects of unresolved melt flow, enabling more accurate recursive rollouts than deterministic regressors under transient laser inputs. Because the learned dynamics are differentiable, the model can serve directly as a predictive control plant. Gradients through imagined futures optimize laser schedules that regulate melt-pool depth over previously unseen geometry, path, initialization. We further distil this optimization into an amortized policy that produces control actions in a single forward pass, providing a proof of concept for real deployment on machines.
Abstract:World models, which learn how environments respond to actions, are emerging as a powerful paradigm for planning through imagined futures, transforming decision-making across games, robotics and autonomous driving. Bringing this capability to manufacturing could enable process decisions on timescales inaccessible to high-fidelity simulation. Here we introduce a generative world model for localized highly dynamic laser melt pool that predicts evolution from histories of temperature and phase morphology under candidate actions. Its generative latent dynamics capture the effects of unresolved melt flow, enabling more accurate recursive rollouts than deterministic regressors under transient laser inputs. Because the learned dynamics are differentiable, the model can serve directly as a predictive control plant. Gradients through imagined futures optimize laser schedules that regulate melt-pool depth over previously unseen geometry, path, initialization. We further distil this optimization into an amortized policy that produces control actions in a single forward pass, providing a proof of concept for real deployment on machines.
Machine Learning (cs.LG)
arXiv:2610.06250 [cs.LG]
(or arXiv:2610.06250v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.06250 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 248 words
