arXiv subjects

Elia Iseli

Publications and source records attributed to Elia Iseli.

3 recordsLinked to original sources

arXiv2026

Drill-bit-inspired dynamic focal fields for augmented laser materials processing

Laser manufacturing has advanced through increasingly precise control of power, pulse duration, repetition rate and scan trajectory, yet the spatial intensity profile of the beam is still usually fixed during light-matter interaction. This constraint limits how energy can be delivered to matter, particularly in processes where melt flow, material removal and surface morphology evolve on comparable time and length scales. Here we introduce drill-bit-inspired laser beams that convert the focal intensity distribution from a passive, static spot into an active, programmable processing tool. By combining cylindrical vector beams with rotational vectorial polarization filtering, we create a near diffraction limited two lobe Hermite-Gaussian focus that continuously spins about the propagation axis and can be reconfigured on demand. We establish two operating regimes, dynamic beam spinning and instantaneous beam-profile shifting, and derive closed-form descriptions of the accumulated fluence and effective pulse number governed by the along-scan pitch l = u/f, where u is the scan speed and f is the spin frequency. Across continuous-wave and ultrashort-pulse regimes, this dynamic energy deposition enables low-power metal machining with drilling efficiencies about four times higher than static Gaussian, enhances convective melt flow, promotes pore resorption and reduces retained porosity in keyhole welding, as visualized by in situ X-ray imaging, and turns simple linear scans into programmable surface textures. These results show that dynamic focal-profile control can extend laser processing beyond static beam shaping, opening a broadly applicable route to programmable energy deposition in manufacturing.

physics.optics↗

arXiv2026

Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer

Data-driven surrogates can replace expensive multiphysics solvers for parametric PDEs, yet building compact, accurate neural operators for three-dimensional problems remains challenging: in Fourier Neural Operators, dense mode-wise spectral channel mixing scales linearly with the number of retained Fourier modes, inflating parameter counts and limiting real-time deployability. We introduce HQ-LP-FNO, a hybrid quantum-classical FNO that replaces a configurable fraction of these dense spectral blocks with a compact, mode-shared variational quantum circuit mixer whose parameter count is independent of the Fourier mode budget. A parameter-matched classical bottleneck control is co-designed to provide a rigorous evaluation framework. Evaluated on three-dimensional surrogate modeling of high-energy laser processing, coupling heat transfer, melt-pool convection, free-surface deformation, and phase change, HQ-LP-FNO reduces trainable parameters by 15.6% relative to a classical baseline while lowering phase-fraction mean absolute error by 26% and relative temperature MAE from 2.89% to 2.56%. A sweep over the quantum-channel budget reveals that a moderate VQC allocation yields the best temperature metrics across all tested configurations, including the fully classical baseline, pointing toward an optimal classical-quantum partitioning. The ablation confirms that mode-shared mixing, naturally implemented by the VQC through its compact circuit structure, is the dominant contributor to these improvements. A noisy-simulator study under backend-calibrated noise from ibm-torino confirms numerical stability of the quantum mixer across the tested shot range. These results demonstrate that VQC-based parameter-efficient spectral mixing can improve neural operator surrogates for complex multiphysics problems and establish a controlled evaluation protocol for hybrid quantum operator learning in practice.

quant-ph↗

arXiv2026

A Fast and Generalizable Fourier Neural Operator-Based Surrogate for Melt-Pool Prediction in Laser Processing

High-fidelity simulations of laser welding capture complex thermo-fluid phenomena, including phase change, free-surface deformation, and keyhole dynamics, however their computational cost limits large-scale process exploration and real-time use. In this work we present the Laser Processing Fourier Neural Operator (LP-FNO), a Fourier Neural Operator (FNO) based surrogate model that learns the parametric solution operator of various laser processes from multiphysics simulations generated with FLOW-3D WELD (registered trademark). Through a novel approach of reformulating the transient problem in the moving laser frame and applying temporal averaging, the system results in a quasi-steady state setting suitable for operator learning, even in the keyhole welding regime. The proposed LP-FNO maps process parameters to three-dimensional temperature fields and melt-pool boundaries across a broad process window spanning conduction and keyhole regimes using the non-dimensional normalized enthalpy formulation. The model achieves temperature prediction errors on the order of 1% and intersection-over-union scores for melt-pool segmentation over 0.9. We demonstrate that a LP-FNO model trained on coarse-resolution data can be evaluated on finer grids, yielding accurate super-resolved predictions in mesh-converged conduction regimes, whereas discrepancies in keyhole regimes reflect unresolved dynamics in the coarse-mesh training data. These results indicate that the LP-FNO provides an efficient surrogate modeling framework for laser welding, enabling prediction of full three-dimensional fields and phase interfaces over wide parameter ranges in just tens of milliseconds, up to a hundred thousand times faster than traditional Finite Volume multi-physics software.

cs.LG↗