arXiv subjects

Markus Bambach

Publications and source records attributed to Markus Bambach.

14 recordsLinked to original sources

arXiv2026

Multi-scale closed-loop melt pool control for LPBF via policy optimization

Laser powder bed fusion (LPBF) is a metal additive manufacturing process where temperature stabilization is of vital importance to avoid defects such as distortion and cracking. Existing control methods require manual tuning, increasing the risk of part failure when printing complex geometries. This paper introduces a dual-loop, data-driven control strategy to stabilize the surface temperature, ensuring robustness and near-optimal performance in the presence of disturbances. The proposed method integrates (i) an in-layer linear output feedback control with gains optimized through policy gradient, and (ii) a layer-to-layer feedforward control combining temperature trajectory optimization and iterative learning control. Simulation results show that the multi-scale controller effectively stabilizes the temperature even under significant model mismatch and measurement noise. Experimental results demonstrate that a simplified, hardware-constrained version of this method matches the state-of-the-art performance of in-situ data-driven methods, reducing mean tracking error by 3.4% and mean input-constraint violation by 47.5% relative to a Bayesian Optimization-tuned baseline. For this physical LPBF validation, the controller is tuned entirely offline using uncontrolled print data from a single calibration layer. Our experiments also demonstrate a new class of high-frequency excitation dynamics that result in reduced vector head swelling, opening up new avenues of research in the additive manufacturing community. This work marks one of the first successful applications of sim-to-real policy optimization in LPBF processes.

eess.SY↗

arXiv2026

Microstructure engineering of Ti-6Al-4V in laser powder bed fusion via 1D thermal modeling and supporting experiments

The microstructure of Ti-6Al-4V has a decisive impact on its mechanical performance; however, controlling phase composition during Laser Powder Bed Fusion (LPBF) remains difficult because of the inherent localized and cyclic thermal history. To fully leverage the design flexibility of LPBF while maintaining an efficient process, it is desirable to tailor the microstructure directly through process-parameter optimization rather than relying on post-processing or in-situ heat treatments. Nevertheless, the large and multidimensional parameter space, combined with the limited availability of experimental data, makes this task particularly challenging. In this work, we develop an efficient computational framework that links process conditions to microstructure evolution by coupling a phase transformation model with a fast 1D finite-difference thermal model, enabling comprehensive insights into process-microstructure relations. The framework predicts the fractions of stable $\alpha_s$, martensitic $\alpha_m$, and $\beta$ phases and is validated experimentally. A broad design of experiments covering 2,000 parameter combinations (spanning volumetric energy density, layer thickness, interlayer time, and build plate temperature) demonstrates how these parameters influence phase evolution and provides systematic practical guidelines for process design. The framework reproduces experimental trends with sufficient accuracy while being orders of magnitude faster than high-fidelity simulations, enabling rapid exploration of process-structure relationships in LPBF of Ti-6Al-4V.

physics.comp-ph↗

arXiv2026

4D reconstruction of alumina laser melt pools at 25 kHz via operando X-ray multi-projection imaging

Advancing additive manufacturing, e.g., laser powder-bed fusion (LPBF), requires resolving rapid processes such as melt-pool dynamics and keyhole evolution in 4D (3D + time). Operando X-ray tomography is a state-of-the-art approach for 4D characterization, but its temporal resolution is fundamentally constrained by the sample rotation speed, limiting achievable 4D imaging rates and preventing the resolution of these fast phenomena. Here we present rotation-enabled X-ray Multi-Projection Imaging (rotation-XMPI), which captures three angularly resolved projections per time step and thereby decouples temporal resolution from the sample rotation speed. Combined with a self-supervised deep-learning reconstruction framework for multi-angle inputs, rotation-XMPI enables high-fidelity 4D imaging at unprecedented speed. We demonstrate the approach in an operando alumina laser-remelting experiment at MAX IV using three beamlets combined with 25 Hz sample rotation. Rotation-XMPI resolves melt-pool morphology and keyhole evolution; in contrast, conventional and limited-angle tomography remain rotation-limited, and motion blur prevents resolving these dynamics. Overall, rotation-XMPI delivers a 250-fold increase relative to state-of-the-art melt-pool imaging, effectively achieving 25,000 reconstructed volumes per second. This method establishes a practical route to scalable ultrafast 4D imaging for additive manufacturing and other materials processes.

physics.optics↗

arXiv2026

A Bayesian Approach to Feedback Control for Hyperbolic Balance Laws

We propose a Bayesian framework for feedback boundary control of hyperbolic balance laws. The method propagates a probability distribution over feedback parameters using Lyapunov decay estimates as a likelihood. For linear models, it recovers available analytical stability results and extends to nonlinear regimes where theory is limited. Using first-order local Lax-Friedrichs (LLF) discretizations, we validate the approach on the decoupled wave system and the linearized Saint-Venant equations, reproducing known stability intervals and mixed boundary couplings. We then treat nonlinear and stochastic problems, including the nonlinear Saint-Venant system, one- and two-dimensional Burgers equations, Burgers equation with random initial data, and nonconservative perturbations with source terms, and show that the inferred stability domains are robust with respect to the indicator and the prior. Finally, we demonstrate transfer to a second-order semi-discrete LLF scheme and to a two-parameter feedback model for laser powder bed fusion with power regulation.

math.NA↗

arXiv2026

A Unified Calibration Framework for High-Accuracy Articulated Robot Kinematics

Researchers have identified various sources of tool positioning errors for articulated industrial robots and have proposed dedicated compensation strategies. However, these typically require individual, specialized experiments with separate models and identification procedures. This article presents a unified approach to the static calibration of industrial robots that identifies a robot model, including geometric and non-geometric effects (compliant bending, thermal deformation, gear transmission errors), using only a single, straightforward experiment for data collection. The model augments the kinematic chain with virtual joints for each modeled effect and realizes the identification using Gauss-Newton optimization with analytic gradients. Fisher information spectra show that the estimation is well-conditioned and the parameterization near-minimal, whereas systematic temporal cross-validation and model ablations demonstrate robustness of the model identification. The resulting model is very accurate and its identification robust, achieving a mean position error of 26.8 $\mu m$ on a KUKA KR30 industrial robot compared to 102.3 $\mu m$ for purely geometric calibration.

cs.RO↗

arXiv2025

Layer-to-layer Closed-loop Switched Heating and Cooling Control of the Laser Powder Bed Fusion Process

This study investigates the stabilization of interlayer temperature in the laser powder bed fusion process through a novel switched layer-to-layer closed-loop feedback controller. The controller architecture aims to measure the interlayer temperature by a laterally positioned thermal camera and maintain a preset reference temperature by switching between the heating mode through dynamic laser power adjustment and the cooling mode by assigning interlayer dwell time to allow cooling between layers. The switching controller employs a feedback optimization control algorithm for the heating mode to adjust the laser power, and a triggering algorithm that increases the interlayer dwell time until the interlayer temperature reaches the reference value. Additionally, the study compares the performance of the proposed controller in both supported and unsupported overhanging parts to evaluate the effect of support structures on the controller performance as well as the thermal behavior of overhanging parts. Results demonstrate the controller's effectiveness in stabilizing interlayer temperature across varying cross-sectional areas while remaining within the material's stable processing zone. In the heating mode, the controller efficiently stabilizes temperature, even in geometries with significant cross-section variation. The study also identifies trade-offs among process efficiency, energy consumption, and build time. Supported parts exhibit reduced overheating but consume more energy and material, while unsupported parts stabilize interlayer temperature faster but with longer build times due to increased dwell time assignments. The research highlights notable improvements in interlayer temperature control for geometries prone to excessive thermal stresses. Moreover, the introduction of interlayer dwell time offers a practical solution to maintaining thermal stability in complex geometries.

eess.SY↗

arXiv2025

Differentiable Material Point Method for the Control of Deformable Objects

Controlling the deformation of flexible objects is challenging due to their non-linear dynamics and high-dimensional configuration space. This work presents a differentiable Material Point Method (MPM) simulator targeted at control applications. We exploit the differentiability of the simulator to optimize a control trajectory in an active damping problem for a hyperelastic rope. The simulator effectively minimizes the kinetic energy of the rope around 2$\times$ faster than a baseline MPPI method and to a 20% lower energy level, while using about 3% of the computation time.

cs.RO↗

arXiv2024

MeltpoolINR: Predicting temperature field, melt pool geometry, and their rate of change in laser powder bed fusion

We present a data-driven, differentiable neural network model designed to learn the temperature field, its gradient, and the cooling rate, while implicitly representing the melt pool boundary as a level set in laser powder bed fusion. The physics-guided model combines fully connected feed-forward neural networks with Fourier feature encoding of the spatial coordinates and laser position. Notably, our differentiable model allows for the computation of temperature derivatives with respect to position, time, and process parameters using autodifferentiation. Moreover, the implicit neural representation of the melt pool boundary as a level set enables the inference of the solidification rate and the rate of change in melt pool geometry relative to process parameters. The model is trained to learn the top view of the temperature field and its spatiotemporal derivatives during a single-track laser powder bed fusion process, as a function of three process parameters, using data from high-fidelity thermo-fluid simulations. The model accuracy is evaluated and compared to a state-of-the-art convolutional neural network model, demonstrating strong generalization ability and close agreement with high-fidelity data.

physics.app-ph↗

arXiv2024

In-situ Controller Autotuning by Bayesian Optimization for Closed-loop Feedback Control of Laser Powder Bed Fusion Process

Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the production of complex, high-criticality parts for various industries. This method relies on static parameter sets from extensive experimentation and simulations, hoping they remain stable and defect-free in production. Closed-loop control of LPBF can further enhance process stability and reduce defects despite complex thermal histories, process noise, hardware drift, and unexpected perturbations. Controller performance depends on parameter tuning, traditionally a manual, expertise-driven process with no guarantee of optimal performance and limited transferability between systems. This study proposes Bayesian Optimization (BO) to automate in-layer controller tuning by leveraging LPBF's layer-to-layer repetitive nature. Two approaches are introduced: online tuning, adjusting parameters iteratively during the process, and offline tuning, conducted in a setup such as laser exposures on a bare metal plate. These methods are experimentally implemented on an in-layer PI controller, and the performance is investigated on two wedge geometries prone to overheating. Results show that BO effectively tunes controllers using either method, significantly reducing overheating in controlled wedge specimens compared to uncontrolled ones. This study presents the first printed parts controlled by an in-layer controller subjected to microstructural analysis. Findings reveal partial presence of lack-of-fusion porosities due to insufficient laser power assigned by the controller, highlighting a significant challenge for utilizing laser power controllers. In summary, BO presents a promising method for automatic in-layer controller tuning in LPBF, enhancing control precision and mitigating overheating in production parts.

eess.SY↗

arXiv2024

DeepForge: Leveraging AI for Microstructural Control in Metal Forming via Model Predictive Control

This study presents a novel method for microstructure control in closed die hot forging that combines Model Predictive Control (MPC) with a developed machine learning model called DeepForge. DeepForge uses an architecture that combines 1D convolutional neural networks and gated recurrent units. It uses surface temperature measurements of a workpiece as input to predict microstructure changes during forging. The paper also details DeepForge's architecture and the finite element simulation model used to generate the data set, using a three-stroke forging process. The results demonstrate DeepForge's ability to predict microstructure with a mean absolute error of 0.4$\pm$0.3%. In addition, the study explores the use of MPC to adjust inter-stroke wait times, effectively counteracting temperature disturbances to achieve a target grain size of less than 35 microns within a specific 2D region of the workpiece. These results are then verified experimentally, demonstrating a significant step towards improved control and quality in forging processes where temperature can be used as an additional degree of freedom in the process.

cs.LG↗

arXiv2023

Layer-to-Layer Melt Pool Control in Laser Powder Bed Fusion

Additive manufacturing processes are flexible and efficient technologies for producing complex geometries. However, ensuring reliability and repeatability is challenging due to the complex physics and various sources of uncertainty in the process. In this work, we investigate closed-loop control of the melt pool dimensions in a laser powder bed fusion (LPBF) process. We propose a trajectory optimization-based layer-to-layer controller that adjusts the laser power input to the next layer to track a desired melt pool depth and validate our controller by placing it in closed-loop high-fidelity multi-layer smoothed particle hydrodynamics simulator of a 2D LPBF process. Detailed numerical case studies demonstrate successful regulation of the melt pool depth on brick and overhang geometries and provide first of its kind results on the effectiveness of layer-to-layer input optimization for the LPBF process as well as detailed insight into the physics of the controlled process. Computational complexity and process performance results illustrate the method's effectiveness and provide an outlook for its implementation onto real systems.

eess.SY↗

arXiv2021

Description of random level sets by polynomial chaos expansions

We present a novel approach to determine the evolution of level sets under uncertainties in the velocity fields. This leads to a stochastic description of the level sets. To compute the quantiles of random level sets, we use the stochastic Galerkin method for a hyperbolic reformulation of the level-set equations. A novel intrusive Galerkin formulation is presented and proven hyperbolic. It induces a corresponding finite-volume scheme that is specifically taylored for uncertain velocities.

math.PR↗

arXiv2021

Feedback control for random, linear hyperbolic balance laws

We design the controls of physical systems that are faced by uncertainties. The system dynamics are described by random hyperbolic balance laws. The control aims to steer the system to a desired state under uncertainties. We propose a control based on Lyapunov stability analysis of a suitable series expansion of the random dynamics. The control damps the impact of uncertainties exponentially fast in time. The presented approach can be applied to a large class of physical systems and random perturbations, as e.g. Gaussian processes. We illustrate the control effect on a stochastic viscoplastic material model.

math.OC↗

arXiv2021

Feedback stabilization of forming processes

We are interested in the control of forming processes for nonlinear material models. To develop an online control we derive a novel feedback law and prove a stabilization result. The derivation of the feedback control law is based on a Laypunov analysis of the time-dependent viscoplastic material models. The derivation uses the structure of the underlying partial differential equation for the design of the feedback control. Analytically, exponential decay of the time evolution of perturbations to desired stress--strain states is shown. We test the new control law numerically by coupling it to a finite element simulation of a deformation process.

math.OC↗