A4BLUE | 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 & hyperlinks
Web resources: http://a4blue.eu/
https://cordis.europa.eu/project/id/723828
Start date: 01-10-2016
End date: 30-09-2019
Total budget - Public funding: 4 179 063,00 Euro - 4 179 063,00 Euro
Cordis data

Original description

Sectors such as aerospace, automotive, wind power, capital goods are characterised, on the one hand, by complex products and small scale production that require high flexibility and on the other hand by an increasing pressure to raise productivity rates. Furthermore manufacturing systems need to deal with an ever-changing environment due to short term changes caused by human or production related variability or long term changes caused by market`s demands and company’s strategy, technology advancements and demographic trends. In this context assembly systems need to put together humans and automation taking advantage of each other’s strengths.

A4BLUE proposes (1) the development and evaluation of a new generation of sustainable, adaptive workplaces dealing with the evolving requirements of manufacturing processes, and (2) the introduction of automation mechanisms that are suitable for flexible and efficient task execution in interaction with human workers by optimising human variability through personalised and context aware assistance capabilities as well as advanced human-machine interfaces.

To support this objective the key features to be covered by A4BLUE are: (1) adaptability by providing an open, secure, configurable, scalable and interoperable adaptation management and assistance system (A4BLUE adaptive framework) that allows effortless integration of heterogeneous hardware and software components and is able to adjust the behaviour of workplace parts according to changes; (2) interaction by providing a set of safe, easy to use, intuitive and personalised and context aware multimodal human-automation interaction mechanisms and (3) sustainability by providing methods and tools 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.

Status

CLOSED

Call topic

FOF-04-2016

Update Date

27-10-2022
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Result items:

The assembly collaborative robot considers both the operation being performed and operator’s anthropometric characteristics for control program selection and part positioning. Besides, the workplace includes multimodal interactions with both the dual arm assembly and logistic robots as well as with the Manufacturing Execution System. Verbal interaction includes natural speaking (i.e. Spanish language) and voice-based feedback messages, while nonverbal interaction is based on gesture commands considering both left and right-handed workers and multichannel notifications (e.g. push notifications, emails, etc.). Furthermore, the maintenance technician is assisted by on event Intervention request alerts, maintenance decision support dashboard and AR/VR based step by step on the job guidance.

The proposed solution comprises an adaptive smart tool and an AR instruction application using HoloLens wearable devices and a framework for ensuring digital continuity starting from the data recorded in the system for manufacturing engineering up to the execution and analysis phase

An AR based solution is proposed for instructions visualization enabling also on-job training activities and guidance. Regarding the ergonomics, an autonomous tool-trolley has been integrated including voice command and AR based gesture steering.

A collaborative robotic cell has been implemented for the deburring operation where the robot executes the most exhausting phases, while the worker focuses on final quality inspection. Regarding the assembly process, an AR solution, using ultra-real animations has been implemented to guide operators through tasks. Additional AR functionalities include the visualization of textual information (tips, best practices…), access to technical documents and voice recording

Result items:

Trust is identified as a key indicator in the pilot. Trust experiments are critical when introducing automation mechanisms that co-operate with workers.

Workers’ opinion is key, especially for decision and acceptance, during the design and development of adaptive automation solutions.

Adaptation within automation mechanisms is reported to be an enhancement at the workplace, according to workers.

The introduction of the is perceived by workers as helpful, especially when productive tasks are exhausting and may provoke health issues. They are not received with reluctance but as supportive in workers’ tasks at the workplace. Regarding AR, it is generally considered as very useful, although the HMD (HoloLens) are too heavy for long time tasks.

Project clusters are groups of projects that cooperate by organising events, generating joint papers, etc...

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.

 

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:

The A4BLUE framework consists of an ICT infrastructure that enables the integration of automation mechanisms, sensors, legacy systems based on IoT components (FIWARE,...) that enables the assistance and support on decision through divers HMI, including Augmented Reality.

Comment:

Included technologies:

- Multimodal interaction mechanisms of workers with automation (voice and gestures).

- Augmented Reality interfaces for assistance and guiding

Comment:

The A4BLUE framework introduces new ways of interacting with automation mechanisms (specifically, with robots) thorugh natural speaking and gestures.

Comment:

FIWARE components are used within the A4BLUE architecture (context broker, CEP, identity management, collaborative asset management (FITMAN)).

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.

Result items:

Adaptation to human variation is boosted, reducing work requirements and worker’s physical demands without incrementing mental and cognitive workload. Additionally, safety and trust in collaborative workplaces are risen while improving usability and human satisfaction. Reconfigurability and flexibility are also improved, as well as efficiency due to the reduction of displacements.

The goals for this use case include assembly time reduction, minimization of errors, and increase of efficiency, productivity and quality. What is more, with AR training and learn-by-doing method adoption, productivity of newcomers is also increased. Finally, a full quality assurance approach and traceability for supervision is enabled.

Improved technician satisfaction has been noted, as well as reduced process times and improved process efficiency with reduced errors. Moreover, the assembly process has benefited from better ergonomics and shorter waste time.

In the deburring process, manual labor is reduced, resulting in improved ergonomic and safety conditions. Moreover, process variability is reduced while quality and productivity, increased. In the assembly process, training time has been reduced with the AR solution and access to centralized documentation is faster, with greater transparency and ease of use while its maintenance costs have been reduced.