Data-driven materials intelligence – AI for materials and process development

Many industries face the same fundamental problem. There are too many variables, interactions that are too complex, and decisions that must be made based on incomplete data. Tasks are often too complex and time-consuming for traditional physics-based models or manual analyses.

Fraunhofer IWM combines physical materials science with modern AI methods to create robust, practical solutions for tackling large search spaces, accelerating simulations, and harnessing distributed knowledge. Transparency is central to this approach. Our concepts deliver explainable results rather than “black box” predictions for the individual challenges faced by our industry partners. We view data-driven materials intelligence as a bridge between scientific excellence and industrial applicability.

Application areas – Business challenges where we support you with AI.

Large search spaces and conflicting
objectives in material design and
process optimization

Finding new materials and robust process windows often resembles an endless cycle of simulation, testing, and prototyping. The
relevant parameter spaces—encompassing material compositions, process settings, component geometry, and loading conditions—are vast. Trial-and-error cannot cover this space. Simulations are often too computationally intensive. This is where AI comes in: it narrows down the search space based on data, makes conflicting objectives transparent, and reduces the need for expensive simulations and physical tests.

Prediction accuracy regarding
component behavior and service life

Reliable prediction of the remaining service life of real components is often hampered by the data available. Load histories are
uncertain, material batches vary, and
inspection data is stored in different formats. This is exacerbated by questions regarding component design: How does element X
affect notch impact toughness? How high are the actual residual welding stresses?

This is where AI comes in, learning process-structure-property relationships to replace conservative blanket assumptions with
robust predictions.

Error-prone and time-consuming quality assurance

The quantitative evaluation of micro-
structures, fracture surfaces, micrographs, or CT scans is often based on manual analysis. This results in subjective assessments that are difficult to reproduce. The creation of test reports is complicated by the need to competently integrate measurement data and images.

Fraunhofer IWM is developing AI solutions to automate and accelerate image analysis and report generation.

Material data: costs, access, availability

Determining certain material properties is costly and time-consuming and involves a multitude of variables (e.g., fatigue life). Often, these material data cannot be determined, nor are they otherwise available.
This limits companies in the design of
components and processes or in the
calibration of simulations. With AI support, material properties can be predicted cost-
effectively using physically based approaches.

Scattered knowledge: The silent loss of materials expertise

Recurring questions often arise in new load scenarios and development projects. However, test reports, failure analyses, and data sheets are scattered and lack a uniform data structure. Measurement and simulation data also come in inconsistent formats from various sources. And even chatbots are of
little help, as their seemingly plausible
answers cannot be verified for decision-
making purposes.

AI-powered solutions tap into scattered knowledge, enabling experience to serve as the foundation for new decisions.

AI-powered R&D services for materials and process development

Virtual development and testing through AI and accelerated simulation

Do you need fast and reliable answers for your next development decision? That’s exactly what we deliver: Using AI-based surrogate models, data-efficient prediction methods, and autonomous development assistants, we drastically shorten simulation and testing cycles without compromising physical robustness. We handle the entire process, from selecting suitable data to training and validating the models, all the way through to integration into your development processes. In doing so, we solve the specific problems that are holding you back: parameter spaces that are too large, insufficient test data, excessively long computation times, and overly expensive testing programs. The result isn’t academic prototypes, but operational tools that enable you to arrive more quickly at robust materials, processes, and component designs.

AI-powered quality assurance for image analysis

Every hour your experts spend on manual image analysis or report generation is time lost elsewhere. We take on precisely this burden: We develop AI systems that automatically classify, segment, and measure microstructures, fracture surfaces, micrographs, and CT data. We train AI models using your own image data, validate them against technical references, and build reporting assistants that consolidate measurement data, images, and findings into drafts ready for inspection. Technical approval remains with your engineers—we provide the tool. The result: less effort in data analysis, faster batch approvals and material qualifications, and lower scrap and warranty risks—all without having to hire additional staff.

AI-powered condition monitoring and service life prediction

Unplanned failures and fixed maintenance intervals cost money and valuable service life, usually because the actual condition of the component is unknown. We bring clarity to this situation: We build updatable digital twins that link physics-based service life models with operational sensor data, inspection images, and maintenance documents, and we develop data-driven methods for identifying stress fields and residual stresses. In doing so, we focus on your specific components, load scenarios, and damage mechanisms. The result: condition-based rather than worst-case-based maintenance planning, an extended safe service life for expensive components, higher material utilization, and a complete, traceable chain of evidence for certification and warranty purposes. You make decisions based on the actual condition—not on safety margins.

AI-powered knowledge management and decision support

Your most valuable knowledge is often found in test reports, data sheets, and the minds of individual experts—and is lost as soon as they leave the company. We preserve this knowledge and make it usable: We build AI assistants that make in-house data, technical literature, and process information searchable and provide answers backed by source citations—instead of plausible-sounding but unverifiable chatbot responses. To do this, we model your data sources, build knowledge graphs, and automatically reconcile customer requirements with manufacturing data and standards. The result: faster, better-informed decisions in development, quality, and manufacturing; less duplication of effort; reduced risk of overlooking relevant internal data—and a company that preserves its experiential knowledge across generations instead of quietly losing it.

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Reasons for collaborating with Fraunhofer IWM on AI-supported materials and process development

Explainable AI instead of a black box

We always combine machine learning with a physically grounded understanding of materials. This ensures that we do not end up with isolated correlations, but rather with models that account for relevant mechanisms and disclose their scope of validity. You receive explainable, robust results rather than mere predictive values, and can verify them technically before basing decisions on materials, processes, or components on them.

The entire chain from a single source

We cover the entire chain, from microstructure through simulation to component lifespan. Our many years of expertise in fracture mechanics, fatigue, and microstructure analysis are directly incorporated into the AI models. As a result, you don’t receive an isolated AI tool, but rather a fully integrated solution in which model assumptions, materials knowledge, and the specific application are perfectly aligned.

Extracting value from limited and heterogeneous data

We work efficiently with data using physics-informed approaches, even when only a few measured values are available. This allows you to fill data gaps—which are the norm in industrial practice—in a cost-effective manner and obtain reliable early insights for material selection, process windows, or component design—without having to rely on time-consuming, additional test campaigns.

A trustworthy partner for sensitive data

As a neutral, research-driven institution, we safeguard your intellectual property and act as a trusted intermediary, particularly in cross-company, IP-secure learning. This allows you to contribute data and model knowledge to collaborative projects without unnecessarily disclosing your know-how, while still benefiting from joint optimization across company boundaries.

Solutions tailored precisely to your use case

Our solutions are tailored to specific use cases, not generic benchmarks. Models and databases are precisely matched to your materials, processes, and test conditions. This provides you with results that are directly applicable to your real-world challenges.

Applied research with industrial implementation expertise

As an institute of the Fraunhofer Society, we combine applied research with industrial implementation expertise, keeping us closely aligned with real-world practice. We deliver verifiable results rather than mere forecasts and support you from the initial feasibility concept through to the integration of the solution into your existing development, quality, or maintenance processes.