AI REGIO Exp11 Automatic Capability Matchmaking For Re-Configurable Robotics Platform

Summary

The experiment, “Automatic Capability Matchmaking for Reconfigurable Robotics Platform”, has been performed by Tampere University and involved the development of an AI-based system to support the design and reconfiguration of production lines in the event of product changeovers.

During the Horizon2020-funded project ReCaM, TAU developed an automatic capability matchmaking system, which intends to ease up the manufacturing system design and reconfiguration planning procedure by automatically suggesting alternative resource combinations for specific product requirements. The approach relies on formal description of resources and products, providing a foundation for rapid creation of new system configurations through capability-based matchmaking of product requirements and manufacturing resource offerings. The matchmaking system allows the designer to search through large resource catalogues to find feasible resource combination alternatives without manual effort.

The new experiment aimed to show how the Capability Matchmaking System can:

Automatise the search and filtering of feasible resources and resource combinations for specific product requirements from large search spaces.
Automatise the identification of required reconfiguration actions on the current system layout.
Let the designer to concentrate on final resource selection and layout planning, instead of search and filtering.
Reduce the time used for system design and reconfiguration planning activity
Increase the amount of alternative resource solutions considered, leading potentially to more efficient and fitting for purpose production system configurations.

This AI REGIO experiment applies the Capability Matchmaking system in a context of Reconfigurable robotics laboratory, with the purpose to extend the old resource catalogue with a large set of new resource descriptions in order to be able to test and validate the system with larger search spaces. Furthermore, several new case products are being utilized to test the system with different requirements. Due to new kind of resources and processes, the Capability Model needs to be extended with new capabilities. Also, the rule base needs to be extended to enable the combined capability calculation and capability matchmaking with these new capabilities. The efforts during the experiment has been focused on creating a large resource catalogue, which can be used, as such, as a search space for the experimentation service offered through the DIH network. The case products that are used for the experiment implementation, are real products from real companies.

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Country:	FI
Address:	Kalevantie 4, Tampere 33100

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Comment:

Provide automatically feasible resource suggestions to the system designer

Reduce time used for system design and reconfiguration planning

Allow more alternative resources to be considered during design and planning, leading to potentially innovative solutions

Reduce humane errors in search and filtering

Automating the search and filtering of feasible resources and resource combinations for specific product requirements from large search spaces.

Automating the identification of required reconfiguration actions on current layout.

Comment:

Letting the designers and reconfiguration planners to use their time for the actual design and planning tasks, instead of cumbersome search and filtering of feasible resources and resource combinations from various catalogues with somewhat incomparable information.

Reducing human errors in resource search and filtering (e.g. potential alternative resources overlooked by human, selecting resources with incompatible interfaces)

Increasing the amount of alternative resource solutions considered, leading potentially to more efficient production system configurations. This may mean, e.g. faster throughput time, better product quality, reduced investment cost or some other improvement, depending on the target KPIs.

Reducing the time used for system design and reconfiguration planning activity, and thus lowering the design costs.