Smart manufacturing solutions for modern industrial processes

Comprehensive coverage of all development phases – from requirements and modeling to implementation, analysis, and testing – ensuring adherence to stringent industry standards, supported by structured approaches such as Advanced Product Quality Planning (APQP) to ensure quality and efficiency criteria are met.

Technical assistance systems integrate design, engineering, and production, leveraging semantic interoperability for efficient data exchange, streamlined workflows, and real-time decision-making across the product lifecycle.

By combining simulated tests with real production data, we create a realistic testing environment. This technology enables precise fault detection, optimization of production workflows, and maximization of system efficiency.

Digital twin technology enables simulations and the exchange of relevant production data. It supports the optimization of manufacturing processes, shortens iteration cycles, and enhances precision in planning, control, and development within production.

Building on the optimization capabilities of Digital Twin technology, which enables simulations and data exchange for manufacturing processes, research combining semantic knowledge and machine learning enhances automated production. This integration allows for flexible task deployment, autonomous operation, and KPI evaluation, ensuring reduced downtime and optimized workflows.

Creation of complex scenario-based test cases using advanced algorithms, machine learning and model-based techniques to dynamically generate comprehensive production test cases.

Edge computing and AI minimize latency, deliver real-time insights, and enable adaptive, data-driven decisions. Context-aware systems enhance transparency and responsiveness to changes in dynamic production environments.

Advanced ML and semantic knowledge is combined to analyze process data in real time to identify patterns and predict failures, enabling proactive maintenance and optimizing workflows for efficient, uninterrupted production.

Advanced data integration and real-time analytics unlock enterprise data’s potential, enabling predictive decisions, system connectivity, and adaptive production workflows to enhance robustness and responsiveness.

Integrating legacy systems with IoT, AI, and real-time monitoring enhances efficiency and decision-making. Using semantic interoperability, OPC UA, and Mendix, legacy infrastructures connect with modern systems, unlocking the potential of existing assets.

Development of standardized interfaces and flexible architectures for the dynamic integration of new sensors. Creating information models for seamless sensor data exchange ensures efficient, adaptable integration into existing systems, enhancing real-time monitoring and operational efficiency.

Leveraging AI for advanced object detection and pose estimation, recognition of human collaborators and their activities, and gesture recognition. These capabilities improve safety and interaction in manufacturing environments by enabling intelligent responses to dynamic conditions and human-machine cooperation.

Real-time monitoring enhances precision and operational insights. By focusing on semantic interoperability, these solutions ensure seamless data integration, automatic data segmentation and interpretation, and support cross-functional collaboration. As a result, data-driven decision-making is enabled, process accuracy is improved, and errors are minimized.

Utilizing formal knowledge models combined with data-driven anomaly detection and error classification models to enable production systems to respond flexibly to faults. This approach minimizes downtime and enhances production security by addressing external influences and compensating for unforeseen events.

Enabling autonomous production of multi-variant configurations, with the capability to automatically analyse and adjust production systems for customized single-unit orders. This approach enhances flexibility in production lines, allowing for efficient management of diverse product variants while ensuring high-quality, individualized production.