AI-Powered Knowledge System for Material Joining: First-Time-Right Decisions with LLMs, RAG & Expert Knowledge
Quickly find the best joining method for your material combination and application. Powered by LLMs, RAG, and a dynamic knowledge graph, the system ranks suitable processes (e.g., welding, bonding, brazing) and explains its choices using scientific sources and expert knowledge from MPA Stuttgart. You get direct access to documents, detailed process info, and the right MPA contact — enabling confident, first-time-right decisions.
MPA Universität Stuttgart
The Materials Testing Institute University of Stuttgart specializes in materials testing, simulation, and component analysis. With over 135 years of experience, they offer expert services to industries across research and manufacturing. And MPA also leads CyberJoin, a nationwide hub helping automotive suppliers drive transformation through sustainable joining and manufacturing technologies.
Why AI-Powered Knowledge Systems Improve Material Joining Selection
Accurate Process Recommendations Instantly: Based on your materials, requirements, or joining goals, the system ranks suitable processes like laser brazing, structural adhesive bonding, or resistance welding.
Embedded Knowledge from MPA Experts: Internal documents, trials, and domain-specific expertise are directly integrated, giving you more than public data alone.
Direct Document Access: From the knowledge graph, access full content sources hosted on Google Docs, publication platforms, or internal tools — no manual searching required.
Expert Contact at a Glance: Automatically see the right MPA expert for your selected joining method, including focus area and contact info.
First-Time-Right with AI + Graph-Based Reasoning: Save time and avoid trial-and-error by using a system that combines LLMs with graph-based decision logic and traceable sources.
See How the AI Knowledge Graph Delivers First-Time-Right Process Selection
Your Questions Answered
Our AI-powered knowledge system suggests the most suitable joining methods — such as welding, adhesive bonding, or brazing — based on your specific material pair, functional requirements, and context. Using Mistral LLMs and scientific knowledge sources, it helps you achieve first-time-right process selection without trial-and-error.
Can the AI system be customized for specific use cases and domains?
Yes. The system is fully customizable — to your data, terminology, and workflows. Whether it's specific joining methods like adhesive bonding in multi-material design or internal guidelines and process specifications — the platform flexibly adapts to your individual requirements.
Does the AI integrate with our existing documentation systems and tools?
Absolutely. We support integration with ERP, CRM, PLM, and collaboration platforms. The system accepts standard data formats (PDF, HTML, Word, XML) and allows direct access to your documentation — automatically synchronized and accessible via the knowledge graph.
How does the system ensure data quality and result accuracy?
The system uses Retrieval-Augmented Generation (RAG) and continuously learns from trusted sources — scientific publications, MPA expert input, validated test results, and internal documentation. All outputs are explainable and linked to verified references.
Is the solution scalable for SMEs and large enterprises alike?
Yes. The architecture supports both lightweight deployments for small teams and robust enterprise-scale integrations. Whether you’re a research unit, a manufacturing team, or an industrial OEM, the system fits your joining knowledge landscape.
Can the system identify the right expert to contact?
Yes. For each recommended joining method, the system shows the most relevant MPA expert contact — based on domain expertise, method relevance, and project focus — so you can directly connect with the right person.
Can data or contacts be removed later if needed?
Yes. In a RAG-based system, data and expert contacts can easily be removed or updated. Since the LLM only generates responses from the current embedding space, there is no need for complex model retraining. Updates are fast, secure, and fully under your control.
Can the system also run on internal servers?
Yes. The system was developed by EDI and deployed on MPA’s internal servers, where it runs fully operational. For data protection reasons, it is hosted entirely within the organization’s infrastructure and secured with appropriate access controls, including user authentication and role-based login management.
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