The primary objective of the SAFIRE project is to develop cloud-based analytics and reconfiguration capabilities that provide:
- Both reactive and predictive reconfiguration for both production systems and smart products
- Flexible run-time reconfiguration decisions during production rather than pre-planned at production planning time
- Real-time reconfiguration decisions for optimisation of performance and real-time production and product functions
The SAFIRE project targets two related technology challenges for smart factories that present new opportunities for improving production, products and services: Interconnected Systems of Production Systems (SoPS) within smart manufacturing environments where individual production systems and the SoPS as a whole have hardware and software requirements to be addressed to achieve specific business objectives such as scheduling, power consumption, throughput, and maintenance.
Connected Product Networks (CPNs) where networked smart products collect data, can be adapted in the field, and can deliver extended services to customers through optimisation of smart product performance parameters and customisation of products to environments, usage patterns and other dynamic factors. The advanced analytics and reconfiguration capabilities to be developed in SAFIRE will be based on mastering the big data challenges associated with manufacturing (sensor and process data), enterprise data and smart product data to provide advanced analytics that allow manufacturers to address production system behaviour forecasting and to establish optimisation methods that are integrated in the design and product chain.
The project will deliver big data analytic capabilities that meet real-time requirements so that dynamic run-time reconfiguration decisions are made during production time rather than pre-planned at production planning time.
- SAFIRE Electrolux pilot: Cloud-driven product...
- SAFIRE OAS pilot: Optimisation of production ...
- SAFIRE ONA pilot: Adaptive Machining
- SAFIRE Demonstrators
- Quality Management Plan
- Optimisation Metrics and Benchmarking
- Methodology for Predictive Analytics Platform
- Methodology for Dynamic and Predictable Recon...
- Methodology for Situational Awareness
|Total budget - Public funding:||3 137 076,00 Euro - 3 137 076,00 Euro|
Original descriptionIn traditional models of manufacturing, the information flow from product design, over production processes, to the manufactured product has been strictly unidirectional. The production equipment “blindly” executes tasks that have no direct relationship to the concepts that are present in the original design models and the product is used with little or no feedback concerning product use patterns. In order to enhance the manufacturability of products and at the same time the flexibility of both factory production systems and modern products, both effective configurability and feedback to design and production is required to assure their highest efficiency.
The SAFIRE project will provide technology and infrastructure to enable Reconfiguration as a Service for dynamic smart factory systems and manufactured smart products that take advantage of cloud-based services and computing power to continually optimise the performance of manufacturing systems and products with respect to key performance characteristics including throughput, power consumption, utilisation, maintenance and other factors.
A key objective of the project is to develop cloud-based analytics and reconfiguration capabilities that extend the operating systems of smart factories with: 1) both reactive and predictive reconfiguration for production systems; 2) flexible run-time reconfiguration decisions during production rather than pre-planned at production planning time; 3) real-time reconfiguration decisions for optimisation of performance and real-time production functions. The advanced analytics and reconfiguration capabilities will be based on innovations in shared situational awareness and mastering the big data challenges associated with sensor, smart objects and process data from manufacturing, logistics and enterprise systems.
Decision support systems