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Industrial use cases

The engineering procedures, the integration platform, its associated tools and the tool chain integration technology will be demonstrated, veried and tested in a number of commercially motivated use cases.

There are in total technology 21 use cases dened within Arrowhead Tools in application areas like e.g. Automotive, Medical, Manufacturing, Building management, Teaching and Mining. The use cases are all commercially motivated. Thus tools and tool chains are to be verified in realistic settings. 

Described under every use case is the foreseen innovation based on each use case respectively.

Automated formal verification

  • Automated tool chain for verication and validation of systems under development.

Engineering processes and tool chains development of diagnostic imaging

  • A tools chain supporting design investigation and creation of design virtual twins.

Integration of electronic design automation tools with product lifecycle tools

  • Integration of electronic design automation tools with product lifecycle tools -> Improve the efficiency and performance in the design and development of new IoT products.

Interoperability between (modelling) tools for cost effective lithography process integration

  • A tool chain establishing seamless interoperability between modelling and analysis tools to facilitate multi-disciplinary engineering teams to eciently develop, verify, evolve, and deploy calibration, performance and diagnostics test scenarios, that are required to compensate for hardware imperfections on nanometer scale.

Support quick and reliable decision making in the semiconductor industry

  • Tool chain for enabling reliable run-time decision making in production.

Production preparation tool chain integration

  • Integrated tool chain from customer conguration to production conguration.

CNC machine automation

  • Highly congurable HMI enabling ecient customisation of CNC control systems.

SoS engineering of IoT edge devices

  • Engineering development of IoT systems, including edge IoT devices and cloud-based integration platform. Targets: Smart City and Energy domains.

Machine operation optimization

  • Engineering tool chain that will considerably increase eciency of earthwork execution.

Rapid HW development, prototyping, testing and evaluation

  • Engineering tool chain that will reduce the overall system development time by up to 40 percent.

Configuration tool for autonomous provisioning of local clouds

  • Tool chain for ecient conguration and deployment of local automation clouds.

Digital twins and structural monitoring

  • Integrated tool chain for digital twin support in monitoring applications.

Deployment engine for production related sensor data

  • Tool chain for considerable cost reduction on sensor data integration in production.

Smart diagnostic environment for contactless module testers

  • Tool for smart run time diagnosis in production.

Virtual commissioning of a cyber-physical system for increased flexibility

  • Extending virtual commissioning processes leading to considerable reduction in engineering lead time as well as the ramp-up time in production.

Production support, energy efficiency, task mangement, data analytics and smart maintenance

  • Tool chains to improve semiconductor manufacturing, maintenance and engineering applications within semiconductor front end and facility core processes.

Linking building simulation to a physical building in real-time

  • That the simulator can eciently and robustly be made to follow the building in real time with sucient accuracy.

Secure sharing of IoT generated data with partner ecosystem

  • Tools for secure sharing of IoT data in multi stakeholder production.

Deployment and configuration

  • Increase quality to reduced cost for System of Systems integration.

Elastic data acquisition system

  • Providing wide interoperability for data acquisition in a production environment.

Data-based digital twin for electrical machine condition monitoring

  • Lightweight run-time digital twins based on machine learning technology trained with simulation and measurement data.