Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context

Summary

The Project

The main objective of this 3-year project is the development and evaluation of a new generation of sustainable and adaptive workplaces dealing with the evolving requirements of manufacturing processes and human variability.

A4BLUE will introduce adaptive automation mechanisms for an efficient and flexible execution of tasks, ensuring a constant and safe human-machine interaction as well as advanced and personalised worker assistance systems including virtual/augmented reality and knowledge management capabilities to support them in the assembly and training related activities. Furthermore, A4BLUE will provide methods and tools to determine the optimal degree of automation of the new assembly processes by combining and balancing social and economic criteria to maximize long term worker satisfaction and overall process performance.

Aims and goals

  • Adaptability by providing an open, secure, configurable, scalable and interoperable adaptation management and assistance system.
  • Interaction by providing a set of safe, easy to use, intuitive, personalized and context aware multimodal human-automation interaction mechanisms.
  • Sustainability by providing methods and tolos to determine the optimal degree of automation of the new assembly processes that combine and balance social and economic criteria to maximize long term worker satisfaction and overall performance.
More information
Web resources: http://a4blue.eu/
https://cordis.europa.eu/project/rcn/205508/factsheet/en
Start date: 01-10-2016
End date: 30-09-2019
Total budget - Public funding: 4 179 063,00 Euro - 4 179 063,00 Euro
Call topic: Continuous adaptation of work environments with changing levels of automation in evolving production systems (FoF.2016.04)
Location

This is a set of Specific Objectives and Research & Innovation Objectives that is subject to a consultation in preparation of the Made In Europe Partnership.  For more guidance about the consultation, please see www.effra.eu/made-in-europe-state-play.

      Comment:

       

      Contribution

      • Reference architecture and implementation based on Fiware and specific components supporting
        • Standards base Integration with automation equipment/devices, including plug & produce capabilities (based on OPC UA standard), as well as with Enterprise Systems (particularly, MES – Manufacturing Execution System)
        • Adaptation management to respond quickly to changes (human and process variability) to increase productivity and responsiveness
        • On the job training, knowledge sharing and support on decision making

       

      Future developments necessary

      • Improvement in the adaptation management, currently based on rules, introducing more intelligence based on AI.

       

      KPIs for impact: Flexibility, Adaptability, Productivity, Error rate, Quality, Training time

      Comment:

       

      Contribution

      • AR based solutions for on the job training and guidance in assembly and maintenance tasks adapted to worker’s profile and context, with demonstration in four different scenarios:
        • Assembly of a complex hydraulic system of an aircraft wing
        • Assembly of an aircraft retraction actuator for a commercial aircraft landing gear
        • Picking of components and assembly of the rear light in small electric vehicle prototype
        • Maintenance tasks for logistic robot

       

      Future developments necessary

      • Integration with new interaction mechanisms (e.g. auditive, tangible)

       

      KPIs for impact: Quality rate, Training time, Trust, Technology acceptance, Worker satisfaction

      Comment:

       

      Contribution

      • Adaptive Human-Robot collaboration based on workers profile for assembly of an aircraft part
      • Human-Machine interaction
        • non-verbal: based on gesture recognition (1) to command both industrial and logistic robots in an aircraft assembly scenario, and (2) to command a mobile tool trolley in an electric vehicle assembly scenario
        • verbal: (1) use of predefined voice commands to command the tool trolley and (2) use of natural speaking to command both assembly and logistic robots.
      • Safe co-existence in fenceless environments based on proximity and human body parts detection.

       

      Future developments necessary

      • New interaction mechanisms (e.g. auditive, tangible)
      • Improvement of active safety mechanisms to detect human intention and adapt the robot behaviour consequently

       

      KPIs for impact: Trust, Technology acceptance, Worker satisfaction, Safety

      Comment:

      Working on an adaptive automation that meets changing production environment and human variability, automation that is introduced assessing sustainability (economic and social) aspects.

      Comment:

      Adaptive automation are introduced after the corresponding safety risk assessment which guarantees a safe productive environment. Such adaptiveness, new ways of human interaction (commanding through natural speaking and gestures) and assistance and guidance in the productive tasks via augmented reality make factory easier to interact with and more satisfactory to workers.


      Active safety features are included to avoid risks in the collaboration of workers and automation.

    Comment:

    A4BLUE enables the transference of knowledge among workers and from the organization to the workers as well, including best practives, instructions, tips from workers, etc., which is integrated within the ICT infrastructure for production.

    Comment:

    Security and privacy risk assessment to identify countermeasures to be included in the A4BLUE framework.

    Comment:

    Safety risk assessment has been performed to identify countermeasures to be included in the A4BLUE framework. Active safety components have been developed for automation mechanisms.

    Comment:

    A complete review of related standards have been carried out.

    Technical developments are using several standards:

    - For automation mechanisms implementation A4BLUE complies with (EN ISO 12100:2010, EN ISO 13849-1: 2015, IEC/EN 60240-1:2016, EN ISO 10218, ISO/TS 15066:2016) and follows as guidelines several other standards.

    - The A4BLUE framework follows as guidelines several standards (ISO/IEC 27001, ISO/IEC 27002, ISO/IEC 27005, ISO/IEC 27032, ISO/IEC 27035).

    - For integration with automation mechanisms (Plug & Produce Adaptive Automation) OPC UA standard (EN 62541) is used.

    - The Virtual Asset Representation is based on B2MML (Business to Manufacturing Markup Language) (ISA-95 standard - IEC/ISO 62264).

    Contribution to standardization is being analysed. Initiating a CWA on worker satisfaction measurement methodology.