Supply chains and manufacturing systems robustness and resilience are, for many years, but especially nowadays, key features requested to ensure reliable and efficient production processes. Two domains are crucial to achieve such purpose: the former is fast and comprehensive monitoring, efficient and reliable condition detection and effective and explicable support for decision making. The latter refers to the intervention by operators, able to better identify problems and to put in place effective operations aimed at fixing it or, better, to prevent such circumstances. This paper presents an integrated approach encompassing a sophisticated IoT and AI-based approach to monitor and detect critical situations, fully integrated with an AR (Augmented Reality) system supporting operators in the field to take informed actions in bi-directional continuous connection. Activities in the context of EC funded project Qu4lity developed in Politecnico di Milano Industry 4.0 Lab, a test environment implementing the proposed approach and demonstrating in an automated production line the effectiveness of the approach, significantly improving performances. Analysis of performance indicators demonstrates the soundness of the proposed solution and implementation methodology to make the overall production process more resilient, efficient and with product defects reduction.

Resilient manufacturing systems enabled by AI support to AR equipped operator

Tavola G.
2021

Abstract

Supply chains and manufacturing systems robustness and resilience are, for many years, but especially nowadays, key features requested to ensure reliable and efficient production processes. Two domains are crucial to achieve such purpose: the former is fast and comprehensive monitoring, efficient and reliable condition detection and effective and explicable support for decision making. The latter refers to the intervention by operators, able to better identify problems and to put in place effective operations aimed at fixing it or, better, to prevent such circumstances. This paper presents an integrated approach encompassing a sophisticated IoT and AI-based approach to monitor and detect critical situations, fully integrated with an AR (Augmented Reality) system supporting operators in the field to take informed actions in bi-directional continuous connection. Activities in the context of EC funded project Qu4lity developed in Politecnico di Milano Industry 4.0 Lab, a test environment implementing the proposed approach and demonstrating in an automated production line the effectiveness of the approach, significantly improving performances. Analysis of performance indicators demonstrates the soundness of the proposed solution and implementation methodology to make the overall production process more resilient, efficient and with product defects reduction.
2021 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021 - Proceedings
978-1-6654-4963-2
AI
AR
Connected worker
Human-Machine Collaboration
IIoT
Predictive Maintenance
Remote Operator Support
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1195649
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