Industrial Data Concepts

The work group combines the competences of Fraunhofer IKTS in the data-based monitoring and optimization of complex manufacturing processes, especially in the field of ceramic powder and mass preparation. Based on decades of experience in the production of advanced ceramic components, the researchers develop the necessary data-scientific infrastructure to record all relevant parameters in battery production and process them for optimization.

Using the latest methods of Artificial Intelligence and integration into machine learning concepts, it is possible to design manufacturing processes in a modular and demand-oriented system, to significantly improve product quality and thus to increase the overall economic efficiency of battery production. The research focus is on modular concepts in the sense of a microservice architecture, which can be validated directly on the pilot lines at BITC and then scaled up at the customer's site. By using industrial standard interfaces and software frameworks, the transfer of the software solutions to other industrial processes is significantly streamlined.

The knowledge- and data-driven approaches of the work group interlock closely with the hardware developments of the Smart Machine and Production Design work group and offer project partners overall solutions in the field of networked and modular battery production (Industry 4.0).

 

Core competencies     

 

• Knowledge- and data-based software development for industry 4.0 system concepts
• Data preparation and structuring
• Development and integration of software and computing infrastructures in industrial manufacturing processes and pilot lines
• Data and knowledge management structures
• Process and technology understanding for complex ceramic manufacturing processes

Services offered

 

Data management

• Data acquisition on all levels of the shopfloor (machine, machine network, factory)
• Data preparation and structuring (ETL/ELT processes, data consolidation)
  
• Data processing and data access
• Data and software quality (automated software tests)
• Development and integration of data storage concepts (Data Warehouse, Data Lake,Distributed Filesystems)
• Definition and in-house development of industry-relevant data formats (in-house data formats)
• Metadata management

 

Computing concepts

• Development and integration of central and decentralized computing concepts (data center solutions, edge computing, cloud computing)
• Integration of commercial IoT platforms
• Development of new business models based on production-relevant data structures (SaaS models)
• Interface management (Industrial Ethernet and field buses)

 

Evaluation and analysis methods

• Development and application of AI-based technologies for production data management (semantic technologies, ontologies, reasoning/inference etc.)
• Programming of algorithms for networked and distributed systems
• Control of assistance systems and Human-Machine-Interaction (HMI)
• Process modeling
• Data analytics and data visualization for internal and external reporting (dashboards, interactive data analysis)

 

Workflow management

• Development of production workflows and machine control concepts as well as testing in industrial pilot lines (data pipelines, container technologies, smart scheduling, dependency management, error handling, machine orchestration, interface definition etc.)
• Development of Track&Trace technologies for traceability