H2020 CREMA - Cloud-based Rapid Elastic MAnufacturing
Cloud-based Rapid Elastic Manufacturing
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CREMA Second year review, Brussels

CREMA Second year review, Brussels

The 21st of February took place the second year review of CREMA at the European Commission. Members of all the partners were there to defend the project and clarify the doubts and questions of the reviewers.

A project overview was presented followed by the impact and business plan. Then the hard work of the partners was displayed with the demonstrations of most of the components, first with a general presentation and then with the use cases. 

Industrial partners of Fagor and Goizper together with the technical partner Ikerlan presented the Use Case I about Machinery Maintenance; based on the existing machinery maintenance problem that they specifically have with the clutch brake of their press machines, which cause losses because non expected machine stoppages.  Live demonstrations were displayed showing different implemented parts:

  • How the process was created on the process design environment by using services previously created on the SVA
  • Explanation of the business rules selected for the use case and how the maintenance process start when the Maintenance button is pressed on the Monitoring and alarming component.
  • Maintenance process starts and get the requirements from the alarm and from the user, functional and non-functional requirements. Oderu runs optimization and select the optimal Assistance team and the spare part, based on the conditions given by the industrial partner; in this case:
  • Retrieval of currently available services from MPM
  • Filtering of currently leasable services with OSL
  • High-precision semantic matching of services with process tasks  based on their functional (IOPE) descriptions, and QoS coverage
  • Assignment of top-ranked equivalent services to tasks in functionally optimal process service plans
  • Solve constraint optimisation problem UC1-COP of model  and select final plan based on optimal solution
  • Determine service/environment data flow bindings of final plan
  • Store plan with its objective values and bindings in CRI
  • Request approval of final plan by the user
  • Return (CRI pointer of) plan to PRU for execution 

Tenneco and TANet presented the Use Case II describing common problems in the automotive sector shop floor and how CREMA components can solve these issues. The demonstrations showed during this review show how the following problems are solved:

  • The number of different assets (tooling, operator, components, machines) makes easier to make mistakes, for instances selecting wrong tooling for certain process, taking into account that this items are continuously moving because of maintenance. To avoid this kind of failures Ubisense sensors and tags are installed on the current prototype to locate the assets and identify when something is not correct using Smart factory software together with Industreweb, which collect and gather data coming from the different machines. This was presented with a live demonstration showing using a webcam to see how the operator was moving the different devices on real time and how Smart Factory and Industreweb software displays this situations.
  • In some instances during production a machine stops due to a break down or failure, to solve this time is needed and that means time and cost losses. In order to react faster, CREMA runs the runtime optimization to find an available machine that can do the work, looking for the available machines existing on the market place and choosing the one that gives better performance, providing also the values of certain parameters that want to be used to reach best results possible. A video was displayed, in which was possible to see how the CREMA cloud Process and Messaging Runtime Environment call oderu at certain moment of the process flow when the machine fails; and how the optimization provide an optimized alternative to the process.

Data collection and data analysis is required to create relationships between parameters and then create a logic for the optimization.