A cyber-physical architecture to monitor human-centric reconfigurable manufacturing systems

Reconfigurable manufacturing systems represent the most adequate production paradigm due to their ability to meet mass customized needs while ensuring cost-effective flexibilities and performances. However, digital solutions are required to manage these dynamic environments over working shifts and processes’ reconfiguration. In this scenario, this work proposes a layout and task-insensitive cyber-physical architecture to monitor human-centric reconfigurable manufacturing systems. Workers’ motion patterns and industrial resources’ positions are acquired through a radio-frequency-based real-time locating system. These data streams are fed into a machine-learning cyber layer to segment operators’ activities during production cycles into two steps. The first computational stream assigns workers’ motion patterns to industrial resources regardless of the system configuration. The following step distinguishes workers’ operations into value-added and non-value-added. These outputs are stored i...

Reconfigurable manufacturing systems represent the most adequate production paradigm due to their ability to meet mass customized needs while ensuring cost-effective flexibilities and performances. However, digital solutions are required to manage these dynamic environments over working shifts and processes’ reconfiguration. In this scenario, this work proposes a layout and task-insensitive cyber-physical architecture to monitor human-centric reconfigurable manufacturing systems. Workers’ motion patterns and industrial resources’ positions are acquired through a radio-frequency-based real-time locating system. These data streams are fed into a machine-learning cyber layer to segment operators’ activities during production cycles into two steps. The first computational stream assigns workers’ motion patterns to industrial resources regardless of the system configuration. The following step distinguishes workers’ operations into value-added and non-value-added. These outputs are stored in a decision support system where customized callback functions develop key performing indicators to monitor the performance of such reconfigurable human-centric environments. The validity of the cyber-physical architecture is demonstrated in an industrial-related pilot environment, involving 40 workers and 8 production set-ups.