Daniel Berglund

1. Viscoelastic model with complex rheological behavior (VisCoR): incremental formulation

   Saseendran, S.; Berglund, D.; Varna, J.

   Advanced Manufacturing: Polymer & Composites Science 6, 1–16 · 2020 · doi:10.1080/20550340.2019.1709010

2. Numerical failure analysis of steel sheets using a localization enhanced element and a stress based fracture criterion

   Östlund, R.; Oldenburg, M.; Häggblad, H.-Å.; Berglund, D.

   International Journal of Solids and Structures 56–57, 1–10 · 2015 · doi:10.1016/j.ijsolstr.2014.12.010

3. Evaluation of localization and failure of boron alloyed steels with different microstructure compositions

   Östlund, R.; Oldenburg, M.; Häggblad, H.-Å.; Berglund, D.

   Journal of Materials Processing Technology 214, 592–598 · 2014 · doi:10.1016/j.jmatprotec.2013.09.022

4. A two stage approach for the validation of welding and heat treatment models used in product development

   Berglund, D.; Tersing, H.

   Science and Technology of Welding and Joining 10, 653–665 · 2005 · doi:10.1179/174329305x57464

5. Comparison of plastic, viscoplastic, and creep models when modelling welding and stress relief heat treatment

   Tersing, H.; Berglund, D.

   Computer Methods in Applied Mechanics and Engineering 192, 5189–5208 · 2003 · doi:10.1016/j.cma.2003.07.010

Conference contributions

1. Virtual Process Chain for Recycled Steel: Effect of Chemical Composition on Forming, Joining, and Crash Performance

   Berglund, D.; Babu, B.; Tersing, H.

   CHS² · 2026

   +−Abstract

   The increasing use of recycled electric-arc-furnace (EAF) steels in automotive body structures introduces variability in chemical composition, particularly in elements such as Mn, Si, Cr, Cu, and Sn. These compositional fluctuations directly affect phase transformations during hot stamping, weld quality during resistance spot welding (RSW), and ultimately crashworthiness. Within the EU-funded CiSMA project, Aerobase Innovations AB has developed an integrated virtual process chain using LS-Dyna to evaluate the effect of chemical composition variability on the complete manufacturing and performance sequence of press-hardened steel components: hot forming with tailored properties (soft-zones), resistance spot welding, and axial crushing. The demonstrator component is a hat profile in 22MnB5 joined with a cover plate in DP780 by RSW. The PHASES model, a composition-dependent phase evolution model developed by Aerobase, predicts microstructure evolution and resulting mechanical properties throughout the process chain (www.copilot.aerobase.se). Coupled thermo-mechanical forming simulations capture the effect of chemistry on phase fractions and soft-zone properties, while electro-thermo-mechanical-metallurgical RSW simulations predict weld nugget and heat-affected zone (HAZ) characteristics. The complete material state is then transferred to the axial crush model. This work presents simulation results for multiple chemical compositions, comparing forming outcomes, weld characteristics, and force-displacement responses in axial crush, thereby quantifying the sensitivity of crash performance to material variability in recycled steels.

   Abstract PDF↗

2. PHASES Agent: an AI agent for physics-based digital metallurgy

   Berglund, D.; Babu, B.; Xavier, M.; Berglund, E.

   ESTEP Annual Event · 2026

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   Predictive metallurgy is often promised and rarely delivered. Most tools stop at a property lookup or a single calibrated model, and the few that reach further tend to stay inside a specialist’s script. Phases (https://phases.aerobase.se), built by Aerobase Innovations AB, works differently. It is an AI agent that runs physics-based phase-transformation models from a plain-language request and returns a finished simulation with plots, and it already drives a validated automotive process chain from hot forming through welding to crash. This abstract describes the platform and the physics behind it, then shows the result that makes the case: a single composition-dependent model, applied across a full press-hardening chain, predicts how the chemistry of recycled steel changes crash performance.

   Abstract PDF↗

3. It Runs — but Is It Right? Ontology-Constrained Synthesis of Physics-Valid Simulation Tools for AI Agents

   Xavier, M.; Berglund, D.; Babu, B.; Berglund, E.

   MSE · 2026

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   A simulation tool can run flawlessly and still be wrong. For AI agents that synthesize tools for other AI agents [4], this is a particularly dangerous failure: inconsistent units, incompatible material states, or use of a solver outside its validated domain can produce convincing outputs that are physically meaningless. We present an ontology-constrained approach for PHASES [1], aligned with EMMO and OSMO [2,3], in which AI agents synthesize new application-level simulation tools from existing validated solvers and approved external physics components. Given a request such as “build a laser-hardening tool,” the agent first constructs a semantic blueprint that specifies the required quantities, units, material states, physical capabilities, solver dependencies, and operating envelopes. It then assembles a bounded workflow, namely laser energy deposition, transient heat transfer, PHASES transformation kinetics, and hardness and case-depth post-processing. The agent generates the interfaces, coupling logic, parameterization, and post-processing, but does not invent new governing physics. Before registration, each candidate undergoes dimensional, semantic, state-transfer, and applicability checks, followed by sandboxed execution and physics-based validation. If a required capability is missing or incompatible, synthesis stops safely. An accepted tool carries a machine-readable validity manifest that records its operating limits, verification evidence, and ontology, material, and solver versions. The tool can then be reused by PHASES AI or other LLM agents through a tool registry and MCP. We will evaluate the approach on custom metallurgical simulation requests that require different combinations of PHASES and approved external solvers, measuring task success, correct capability selection, safe rejection, envelope compliance, and silent physical errors. The aim is to create AI-generated simulation tools that do more than run: they know and expose the limits within which they are right.

   Abstract PDF↗

4. AeroCRAFT: AI-Driven Closed-Loop Toolpath Planning and Thermal Simulation for DED

   Xavier, M.; Berglund, D.

   VMAP User Meeting · 2026

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   Directed Energy Deposition (DED) enables near-net-shape fabrication of complex metallic geometries; however, the process is highly sensitive to thermally induced defects, including porosity, delamination, residual stress, and microstructural inhomogeneity. These defects arise from the interaction between heat-source dynamics and the scan strategy, yet conventional toolpath planners generate trajectories based solely on geometric considerations, without accounting for the cumulative thermal history. This work presents AeroCRAFT, an end-to-end AI-driven toolpath planning and thermal simulation platform that closes the loop between scan strategy, thermal response, and defect prediction in DED. The framework integrates deterministic geometric toolpath generation with a voxel-based thermal finite element (FEM) module to enable automatic preprocessing and transient temperature-field evaluation. AeroCRAFT addresses the limitations of traditional scanning methods - including unidirectional, bidirectional, serpentine, and concentric strategies - by introducing a transformer-based attention model that is trained using reinforcement learning to optimize scan sequencing. The AI agent determines toolpaths by considering both geometric factors and user-defined goals such as hotspot reduction, defect minimization, or achieving specific microstructures. Real-time thermal evaluation is performed using process- and temperature-based quality metrics, including Energy per Unit Length (EUL) and HAI₁₀ thermal exposure indicators, enabling quantitative assessment of build integrity during process planning. Toolpaths, process parameters (laser power, scan speed, dwell time, surface normal), orientation data, and full temporal temperature fields are exported in the VMAP HDF5 standard format, enabling seamless interoperability with downstream FEA solvers and robotic systems such as LS-DYNA (*BOUNDARY_THERMAL_WELD_TRAJECTORY), Abaqus, CalculiX, and KUKA KRL programs. The integrated deterministic and AI-driven planning framework, combined with VMAP-compliant digital thread export, demonstrates a scalable pathway toward closed-loop, defect-aware DED process planning, reducing empirical trial-and-error and enabling systematic optimisation of complex geometries prior to physical deposition.

5. Advanced Laser-Based Manufacturing: Multiphysics Modelling and Interoperability with VMAP Standards

   Babu, B.; Berglund, D.

   VMAP User Meeting · 2025

   +−Abstract

   The ALABAMA, RESTORE, and GEAR-UP projects are revolutionizing manufacturing technologies through multi-physics modeling. These initiatives tackle urgent challenges, including process optimization, sustainability, and material efficiency in the aerospace, automotive, marine, and manufacturing industries. However, a lack of standards poses a significant challenge to integrating, structuring, and exchanging simulation data across disciplines. This ensures efficiency, interoperability, and AI readiness in multi-physics modeling workflows. ALABAMA: The ALABAMA project innovates laser-based manufacturing processes by moving beyond conventional Gaussian laser profiles. These standard profiles can induce steep thermal gradients, leading to vaporization, instability, and defects. ALABAMA focuses on adaptive laser beam shaping (e.g., flat-top, ring, and saddle beams) to optimize melt pool geometry and minimize defects like porosity or spatter. Multi-physics simulations are instrumental in understanding these laser-material interactions' thermal, fluid, and mechanical dynamics. This enables real-time control of laser systems, ensuring defect-free and high-productivity processes. RESTORE: RESTORE promotes remanufacturing to extend product life, reduce waste, and foster a circular economy. This project revitalizes high-value components by integrating subtractive machining with additive techniques (e.g., laser cladding) and post-processing heat treatment using Laser. Multi-physics modeling supports this hybrid approach by simulating thermal and mechanical stresses during material deposition. These insights are essential for optimizing microstructural evolution and mechanical integrity in repaired components, ensuring cost-effective and environmentally sustainable solutions for industries. GEAR-UP: The GEAR-UP project tackles sustainable manufacturing by developing advanced material models and multi-physics frameworks. A central focus is understanding the effects of trace elements in recycled materials, such as steel, on their performance during deformation or joining. The project enables predictive modeling of manufacturing processes by integrating phase evolution kinetics, deformation-induced failure mechanisms, and thermal simulations. This allows industries to adopt recycled materials without compromising performance or safety, paving the way for sustainable usage. Across all three projects, multi-physics modeling is a backbone, bridging complex physical phenomena—thermal, mechanical, metallurgical, and fluid mechanics. These simulations are tools for understanding and guiding optimization, ensuring that manufacturing processes meet stringent quality and environmental standards. The VMAP wrapper is envisioned as a critical enabler for seamless data exchange across various simulation software environments. Adopting the VMAP standard ensures interoperability, efficiency, and usability in multi-physics modeling workflows. Let's look at the crucial requirements for the VMAP wrapper. Standardized Data Exchange: The VMAP wrapper is expected to enable seamless data transfer between simulation tools. It should facilitate the export/import of material properties, boundary conditions, load cases, and results in a consistent format. This ensures that thermo-mechanical-metallurgical and fluid mechanics software can communicate without data loss or compatibility issues. Interoperability with Multi-Physics Software: Modern engineering workflows require multi-physics simulations, integrating structural, thermal, and fluid mechanics analyses. The VMAP wrapper must ensure interoperability between these environments, allowing efficient coupling of CFD, structural, thermal, and other simulation methods. By doing so, engineers can streamline workflows, reducing manual reconfiguration and improving simulation accuracy. Material Property Transfer: Material modeling often involves complex, non-linear behaviors requiring accurate data transfer. The VMAP wrapper must standardize material property exchange, ensuring that advanced material models (such as anisotropic composites or phase-changing alloys) can seamlessly integrate into various solvers. Efficient Data Management: Large-scale simulations generate extensive datasets, requiring efficient storage and retrieval mechanisms. The VMAP wrapper should offer optimized data structuring, enabling users to manage high-fidelity simulation data locally and in cloud environments. This is crucial for iterative design processes where multiple simulations are conducted to refine models. User-Friendly Interface for Engineers: Engineers, particularly those using commercial solvers like MSC Marc, might not be familiar with complex data formats. A user-friendly VMAP interface should provide an intuitive workflow, minimizing the learning curve and reducing data transfer errors. Ideally, the wrapper should include automated data validation to detect inconsistencies before import/export. Data Structuring and Labeling: Structured simulation data is essential for machine learning (ML) applications. The VMAP wrapper must support variable tagging (e.g., material properties, boundary conditions) and metadata labeling, allowing AI/ML models to process simulation results without requiring manual reformatting. Bidirectional Data Flow: The wrapper should enable two-way communication between simulations and ML models. Simulation outputs can inform ML models, while ML-driven predictions can be reintegrated into simulations, allowing iterative design improvements and AI-assisted process optimization. The VMAP wrapper can be an integrator that facilitates the exchange of simulation data, ensuring efficiency and interoperability in multi-physics workflows.

6. AI-Driven Multi-Physics Modelling: Advancing Additive Manufacturing for Accuracy, Efficiency, and Sustainability

   Babu, B.; Berglund, D.

   ISAM · 2025

   +−Abstract

   Multi-physics modeling in additive manufacturing (AM) integrates thermal, mechanical, metallurgical, and fluid dynamics to optimize process control and material performance. High-fidelity data handling, real-time process adaptation, and cross-disciplinary integration are key challenges. AI-driven agents enhance simulations by automating parameter tuning, predicting defects, and enabling adaptive control. These intelligent systems streamline workflows, improve accuracy, and drive efficient, sustainable AM innovations.