arXiv subjects

Patrik Hoffmann

Publications and source records attributed to Patrik Hoffmann.

4 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

Chaotic Oscillator Networks for Classification Tasks

Chaotic oscillators have gained significant attention in the research community because of their ability to reproduce and investigate the complex dynamics of real-world phenomena. Recent advances in the design of chaotic oscillator ensembles have led to the development of efficient signal processing frameworks that surpass traditional approaches. However, scaling such systems remains challenging due to the significant increase of computational resources and issues with training convergence. This study advances the state of the art by addressing the problem of data processing with ensembles of nonlinear oscillators that can be scaled up. In our approach, the processing is achieved as an anticipated local resonance or echo in a group of coupled chaotic oscillators, driven by external data input. Local resonance is enabled by tuning the coupling terms between the oscillators, which are approximated using the traditional artificial neural network and adapted to match the input feature distributions. Training the framework entails training this neural network to capture the dynamics of the entire oscillator system. The framework is evaluated using synthetic data and demonstrates an accuracy in machine learning classification task, while patterns recognition and dynamic system identification are also presented here as an extension of the functionality that involves additional modifications. Additionally, the universality of this approach is demonstrated by tests with different connections configurations between the oscillators and their types. The main advantage of the proposed framework is that it avoids hand-crafting explicit coupling terms, which requires expert knowledge and does not scale for large problems. Leveraging standard machine learning components simplifies both training and deployment of oscillator networks, enabling gradient-based optimization.

nlin.CD↗

arXiv2025

Reinforcement Learning on Reconfigurable Hardware: Overcoming Material Variability in Laser Material Processing

Ensuring consistent processing quality is challenging in laser processes due to varying material properties and surface conditions. Although some approaches have shown promise in solving this problem via automation, they often rely on predetermined targets or are limited to simulated environments. To address these shortcomings, we propose a novel real-time reinforcement learning approach for laser process control, implemented on a Field Programmable Gate Array to achieve real-time execution. Our experimental results from laser welding tests on stainless steel samples with a range of surface roughnesses validated the method's ability to adapt autonomously, without relying on reward engineering or prior setup information. Specifically, the algorithm learned the correct power profile for each unique surface characteristic, demonstrating significant improvements over hand-engineered optimal constant power strategies -- up to 23% better performance on rougher surfaces and 7% on mixed surfaces. This approach represents a significant advancement in automating and optimizing laser processes, with potential applications across multiple industries.

cs.LG↗

arXiv2022

A Novel Single-Mode Microwave Assisted Synthesis of Metal Oxide as Visible-light Photocatalyst

Visible-light photocatalyst titanium dioxide (TiO2) was successfully prepared via a novel and facile single-mode microwave assisted synthesis process. In this one-step synthesis, Ti as target material selectively oxides in magnetic field throughout rapid heating, whose process requires less energy consumption and short time. In obtained TiO2, self-doping of Ti3+ was confirmed, which makes TiO2 performed sufficient light absorption in visible region with wavelength above 400 nm. Such Ti3+ self-doped TiO2 exhibits much narrower optical bandgap (2.14 eV) to compare with stoichiometric TiO2 (3.0-3.2 eV). The synthesized TiO2 also shows superior photocatalytic activity to commercially available TiO2 towards the degradation of Rhodamine B under visible light irradiation.

physics.app-ph↗