TY - GEN
T1 - Defect-Aware Automation
T2 - 35th Irish Signals and Systems Conference, ISSC 2025
AU - Penica, Mihai
AU - O'brien, William
AU - Dooley, Adam
AU - Bhattacharya, Mangolika
AU - Ghita, Vladimir
AU - O'connell, Eoin
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - In the context of Industry 4.0, this study presents a defect-aware, closed-loop manufacturing system that integrates low-cost Internet of Things (IoT) devices with robotic automation to enhance quality control in 3D printing. A custom ESP32-based IoT device continuously monitors environmental and vibrational data to enable early defect detection during the printing process. If the predicted print quality falls below a configurable threshold (60%), the system halts the operation and instructs a UR3 robotic arm to remove the defective part - demonstrating full interoperability within a digital twin framework. The integrated machine learning model provides real-time feedback and autonomous control. More than 50 test scenarios confirmed the system's reliability, with latency remaining well within real-time operational limits. By reducing material waste and energy consumption, the proposed solution offers a scalable and sustainable approach to intelligent additive manufacturing.
AB - In the context of Industry 4.0, this study presents a defect-aware, closed-loop manufacturing system that integrates low-cost Internet of Things (IoT) devices with robotic automation to enhance quality control in 3D printing. A custom ESP32-based IoT device continuously monitors environmental and vibrational data to enable early defect detection during the printing process. If the predicted print quality falls below a configurable threshold (60%), the system halts the operation and instructs a UR3 robotic arm to remove the defective part - demonstrating full interoperability within a digital twin framework. The integrated machine learning model provides real-time feedback and autonomous control. More than 50 test scenarios confirmed the system's reliability, with latency remaining well within real-time operational limits. By reducing material waste and energy consumption, the proposed solution offers a scalable and sustainable approach to intelligent additive manufacturing.
KW - 3D printing
KW - Digital twin
KW - Industrial Internet of Things (IoT)
KW - Industry 4.0
KW - Monitoring system
KW - Sensors
KW - Smart Manufacturing
UR - https://www.scopus.com/pages/publications/105032409549
U2 - 10.1109/ISSC67739.2025.11291427
DO - 10.1109/ISSC67739.2025.11291427
M3 - Conference contribution
AN - SCOPUS:105032409549
T3 - Irish Signals and Systems Conference: Signalling our Strength, ISSC 2025
BT - Irish Signals and Systems Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 June 2025 through 10 June 2025
ER -