Periodic Reporting for period 1 - A4BLUE (Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context)

Summary
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...\n\nSectors 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 aims 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 involves four validation scenarios: two industrial ones representing the aeronautic sector (AIRBUS and CESA) and two laboratories (RWTH: electric car, and IK4-TEKNIKER: assembly)\n\nA4BLUE follows an iterative an incremental implementation loop. The aim of the alpha iteration was to provide initial methods and procedures as well as initial prototypes and working proofs of concepts to validate the defined approach. Main results are:
? Initial prototype introducing a smart torque wrench connected to a guidance tool based on augmented reality to support the complex hydraulic system assembly. The prototype displays the required information adapted to the operator's' profile through a head mounted device and collects relevant real-time information.
? Detailed design, simulation and initial tests of a new deburring process involving a robot able to co-operate with the worker.
? Initial prototype of an assistance tool to support knowledge sharing in the assembly of the main landing gear extraction actuator.
? Initial prototype supporting collaborative assembly of a latch valve that involves the integration of a manufacturing execution system to collect context variability as well as multimodal human-robot interaction capacities including gestures and natural speaking. Furthermore, once it is plugged the dual arm robot is ready to share updated data and allow remote control. The exchanged context information supports the adaption of the behaviour of the robot to the context (i.e. including operator profile), multichannel intervention requests and decision making.
? Initial augmented reality based on the job guidance prototype supporting the assembly of the rear light in an electric vehicle and displaying the required information adapted to the operator's' profile through a head mounted device.
? Initial automated tool trolley prototype including free navigation and gesture steering for short-range interaction.
? Initial method for the definition of the optimal degree of automation and supporting proof of concept tool.\n\nMain 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:
? 50% searching and training time reduction by providing ?just in time? guidance adapted to workers? profile and process context;
? 10% quality rate increase by reducing the sources of non-quality such us instructions misunderstandings;
? 30% productivity rate increase 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;
? 20% adaptability increase by introducing adaptation capabilities and assistance tools along with a plug&produce approach and the integration of enterprise control systems;
? promoting a wide adoption of the new developments by providing replicable and generic methodologies and solutions;
? increased worker satisfaction by designing and implementing a 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.
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Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context
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