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

Periodic Reporting for period 2 - A4BLUE (Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context)

Reporting period: 2018-04-01 to 2019-09-30

Sectors such as aerospace, automotive, wind power or capital goods are characterised by: (1) complex products and small-scale production that require high flexibility, (2) increasing pressure to raise productivity rates and (3) manual intensive activities. Manual work has the advantage of being highly flexible although it presents several drawbacks such as the difficulty to increase productivity rates or the dependence on highly skilled workers. Furthermore, manufacturing systems need to deal with an ever-changing environment due to short term changes caused by human (e.g. different worker’s physical or cognitive characteristics, skills, etc.) or production related variability as well as long term changes caused by market`s demands, technology advancements or demographic trends (e.g. reduced and ageing workforce).
In this context manufacturing companies need to put together humans and automation mechanisms (e.g. collaborative or assistance robots) taking advantage of each other’s strengths to balance flexibility and productivity requirements and set up adaptation and assistance means enhancing worker’s capabilities, skills and satisfaction to support long term socio-economic sustainability aiming to maintain competitiveness and employment as well as to foster the inclusion of workers with varying capabilities and the increase of organisational commitment and retention.
A4BLUE aimed to develop and evaluate a new generation of sustainable, adaptive workplaces dealing with short and long-term changes by introducing: (1) safe automation mechanisms that are able to adapt their behaviour to both human and process characteristics and are suitable for flexible and efficient task execution in interaction with humans; (2) assistance tools and advanced human-machine interfaces considering workers’ capabilities and skills as well as the activity being performed and the environmental conditions; (3) methods and tools to determine the optimal degree of automation of the new processes that combine and balance social and economic criteria to maximize long term worker satisfaction and overall performance as well as methods and tools to assess workers’ satisfaction.
A4BLUE involved four validation scenarios: two industrial ones representing the aeronautic sector (AIRBUS and CESA) and two laboratories (RWTH: electric car, and IK4-TEKNIKER: assembly).
A4BLUE followed an iterative an incremental implementation loop to provide initial prototypes and working proofs of concepts to validate the defined approach before completing the developments. As a result, A4BLUE generated tangible and intangible results (e.g. know-how or approaches to identify socio economic assessment indicators). Furthermore, four use case applications have been implemented to cover the specifications of the use case scenarios involved in the project.
The tangible results are:
- The A4BLUE Adaptive Framework which includes, in turn, other results such as:
• Semantic virtual asset representation
• Collaborative asset manager
• Mediation services (for the integration with legacy systems)
• Device manager (for interoperable plug & produce function with automation mechanisms)
• Context aware multichannel interaction manager
• Virtual Reality /Augmented Reality tool for assembly and auxiliary processes
• Collaborative Knowledge Management Platform
• Decision support dashboard and monitoring system
• Model and tool for assessment of worker satisfaction
• Methods and tools for the definition of the optimal level of automation
- Automation mechanisms
• Deburring robot
• Automated tool trolley for tool provision, which includes verbal and non-verbal human machine interaction mechanism
From these tangible results, A4BLUE has identified key exploitable ones and further analysed them by characterising all the A4BLUE key exploitable results, by defining and evaluating their risks and by understanding customer needs to identify a clear value proposition.
The project results have been disseminated by using different channels, mainly through the participation in events, conferences and workshops and through the publication of whitepapers, news, newsletters and press releases on the project social media channels.
Main innovations and expected results are:
• New or enhanced automation mechanisms (i.e. smart torque wrench, deburring robot, dual arm robot, logistic robot and automated tool trolley) including plug & produce capabilities and supporting continuous automation data exchange and remote control to enable adaptation to operator or context variability.
• Multichannel human-robot interaction mechanisms considering natural interaction and including the fusion of verbal and non-verbal interaction channels such as gestures and voice.
• Generic A4BLUE adaptive framework integrating assistance tools such as on-the-job training and guidance based on mixed reality that provides the required information adapted to the operator's' profile, collaborative knowledge management to collect and provide best practise information and support learning activities, decision support systems adapted to the worker role and performance monitoring capabilities.
• Method and tool for the definition of different levels of automation and assessing the optimal degree of automation from a socio-technical an economic perspective considering static boundary conditions such as investment costs, etc. as well as dynamic boundary conditions such as human workforce availability, skills and effects on worker satisfaction.
• Method and tool for assessment of worker satisfaction to support the design adaptive systems involving humans and automations and perform the quantitative assessment of the different levels of worker satisfaction.
• Usability methodology considering user needs in relation to advanced adaptive automation and including integration with engineering and technical science.
Socio economic impact: A4BLUE’s key objectives are to empower people by improving productivity and making workplaces more flexible, safe and inclusive and to strength competitiveness by increasing overall performance to support long term employment. Main expected impacts are:
• reduced training time by providing “just in time” guidance adapted to workers’ profile and process context;
• increased quality rate by reducing the sources of non-quality such us instructions misunderstandings;
• increased productivity rate by reducing the learning curve and enabling work re-allocations due to the introduction of assistance tools supporting adapted on the job guidance, collaborative knowledge management and decision making;
• increased flexibility/adaptability by introducing adaptation capabilities and assistance tools along with a plug&produce approach and the integration of enterprise control systems;
• promotion of a wide adoption of the new developments by providing replicable and generic methodologies and solutions;
• increased worker satisfaction by implementing a robust methodology considering the key factors affecting it and therefore raising acceptance and engagement;
• supporting the achievement of the objectives of the EU 2020 strategy (i.e. 75% overall employment, 50% for people aged 55-64) by implementing inclusive working environments able to adapt to work demands and workers capabilities and skills and reshaping how skills are acquired to minimise the skills mismatch when facing new technologies or activities.
AIRBUS UC: AR supported training
RWTH UC: Intelligent tool trolley
TEKNIKER UC: Active safety – Person detection
RWTH UC: AR supported guidance
CESA UC: Deburring robot
AIRBUS UC: AR supported training
TEKNIKER UC: Ergonomic adaptation of dual arm robot
RWTH UC: AR supported guidance
AIRBUS UC: AR based guidance
TEKNIKER UC: Voice and Gesture based interaction mechanisms
CESA UC: Deburring robot cell
CESA UC: Automated deburring process simulation
AIRBUS UC: Smart torque wrench at AIRBUS experimentation site
TEKNIKER UC: AR guided maintenance
RWTH UC: Gesture steered tool trolley