A4BLUE | Adaptive Automation in Assembly For BLUE collar workers satisfaction in Evolvable context

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

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.

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.