Presentation

The VMAP IO library is a reference implementation of the VMAP standard in C++ with interfaces to other programming languages. Its further development is managed and organized by Fraunhofer SCAI. This presentation provides an overview of the ongoing development and shows results of the improved compression of data in the VMAP standard on selected sample data.
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ParaView is an open source, multiplatform data analysis and visualization application, used to post-process simulation data at scale. We will learn how to use ParaView efficiently to analyse VMAP data using the VMAP ParaView plugin and explore recent additions to the application that help you gain insight on your data and craft stunning visualizations for your presentations and publications.
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Synera is a low-code software for connected engineering used to automate recurring tasks in the product development process. It standardizes workflows and formats across multiple industries, domains, and teams which usually work in disconnected silos using different software. Although there are some loose standards, most commercial packages, for example from Dassault, Siemens or Autodesk, rely on individual input and output formats.
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The digitalization of Materials Science and Engineering (MSE) demands standardized, interoperable approaches for representing complex experimental and simulation data. To address this challenge, the Platform MaterialDigital Core Ontology (PMDco) provides a mid-level semantic framework that bridges high-level ontologies and domain-specific terminologies. PMDco version 3.0, recently released, introduces significant enhancements based on a full alignment with the Basic Formal Ontology (BFO) as standardized in ISO/IEC 21838-2. This alignment ensures conceptual consistency and facilitates integration across heterogeneous data sources while enabling FAIR-compliant workflows.
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As simulation-driven engineering evolves, demand grows for semantically rich, traceable, and interoperable material data. This paper presents a CAE-neutral Material Master System, developed by Matplus and deployed at leading industrial clients. Built on JSON-based material cards, JSON Schema validation (aligned with VDA231-301), and knowledge graphs, the system links raw test data—ingested directly from lab equipment—to processing history and constitutive models, ensuring end-to-end provenance.

The increasing complexity of computational engineering processes—spanning CAE simulations, CAD-based analyses, and multi-scale modelling—demands standardized, interoperable approaches for data exchange and semantic representation. This presentation explores two strategic opportunities for the VMAP Consortium to advance standardization efforts in this domain.

This presentation introduces the VMAP Ontology, an OWL-based semantic framework that formalizes the conceptual relationships within VMAP data structures. The ontology extends beyond hierarchical classification to encode explicit relationships between entities—such as linking material properties to specific geometric regions, connecting discretization schemes to result variables, and capturing provenance across simulation workflows. By representing VMAP’s core groups (SIMULATION & MEASUREMENT) as interconnected semantic concepts, the ontology enables advanced reasoning and intelligent querying capabilities impossible with traditional file-based access.

The increasing complexity and demands on modern composite aerospace structures necessitate the integration of multiple assessment steps into a cohesive digital workflow during virtual product development. Traditional approaches often result in fragmented solver-specific data, duplicated efforts for various simulation tools with the resulting inconsistencies or recurring implementations of the same functionalities for different software products.
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Extrusion blow molding represents the standard manufacturing process for hollow plastic components used in industrial packaging and technical applications, such as canisters and intermediate bulk containers (IBCs), as well as large-format rainwater tanks. During the production process, a hollow tube, known as a parison, is extruded and expanded against the surfaces of a cooled mold. Residual stresses induced during the solidification phase partially relax after demolding, causing shrinkage and warpage in the final article.

The EU-project BASE is a three-year initiative with 19 international partners to develop a trusted, interoperable Digital Battery Passport (DBP). Four pilot use cases will demonstrate applicability and performance. The solution will also deliver AI tools for predicting metrics such as State of Health (SoH) and Remaining Useful Life (RUL).
Drawing on training data collected for these AI models, we outline the abstracted concepts and present a roadmap for building an ontology as an integral part of the BASE-DBP solution.
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Advancing industrial efficiency in high-mix/low-volume production requires innovative digital solutions that seamlessly integrate simulation workflows, process control strategies and data-driven optimisation algorithms. The development of an open innovation platform and an interoperable digital pipeline is designed to enable a design-by-simulation optimization framework. The approach integrates feedback and inline feedforward control strategies to enhance the performance of industrial systems by linking materials modelling, materials characterization, simulation-based digital twins, and data-driven models. The framework dynamically updates through distributed production data collected from embedded IoT edge devices and product quality metrics. This unified methodology is being presented for the topology optimized structural elements via Wire-Arc Additive Manufacturing (WAAM) in the construction sector, exemplifying a low-volume production scheme.

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.

Powder bed fusion (PBF) and directed energy deposition (DED) are two of the most important methods of additive manufacturing (AM), with a combined market share of around ¾ in the AM domain. At present, parts manufactured by both processes see industrial applications in aerospace, energy and automotive industries. The widespread application, however, is hindered by strict certification requirements, lack of in-process quality assurance and models to predict material variability.

Learning from previous designs in product development is a very appealing objective in engineering. Ideally an assistant system can be designed that, considering engineering requirements and functional constraints, identify from previous designs, the most similar ones. This can speed up product design significantly. This type of analysis is called Smart CAE Data Analysis. We present an approach where engineering geometric based design changes or materials ones, and their corresponding outcomes are learned from a bundle of several model variations and simulations. A combination of this input-out-put information is represented as a knowledge graph, and this is the information used to describe it and compare it with other similar ones.

Training data in the form of high-fidelity physics-based simulation results is an enabler for data-driven engineering, but proprietary binary output file formats can limit the flow. As part of the NAFEMS ASSESS Initiative’s focus on Machine Learning, this presentation demonstrates the use of VMAP to create vendor-agnostic training datasets that ensure interoperability and preserve neutrality.
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