Details
| Original language | English |
|---|---|
| Pages (from-to) | 480-485 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 116 |
| Early online date | 18 Apr 2023 |
| Publication status | Published - 2023 |
| Event | 30th CIRP Life Cycle Engineering Conference, LCE 2023 - New Brunswick, United States Duration: 15 May 2023 → 17 May 2023 |
Abstract
At regular intervals, aircraft and their components, such as engines, are inspected following specified maintenance, repair and overhaul (MRO) guidelines. The modular design of the engines supports complete or partial disassembly for inspection of all relevant parts. The often used bolted joints are significantly altered by the harsh environmental conditions, severely increasing the disassembly effort usually carried out manually. Using tools like a ratchet or rotary impact wrenches, workers apply torque on the screw head to loosen the screw. If the loosening torque exceeds the material limits, screw heads are torn off, leaving the shaft in the base thread. This article presents a strategy to prevent disassembly damages through precise monitoring of the loosening torques and angle of rotation. Based on this data, a machine learning algorithm detects and predicts potential breakage, to allow adapted disassembly strategies to prevent complex rework. The algorithm will classify potential damage into different categories. Preliminary testing proved the applicability of machine learning toward aircraft disassembly.
Keywords
- Damage Detection using Machine Learning, Disassembly, Threaded Fasteners
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 116, 2023, p. 480-485.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Towards Early Damage Detection during the Disassembly of Threaded Fasteners using Machine Learning
AU - Blümel, Richard
AU - Raatz, Annika
N1 - Funding Information: We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), funding code: SFB 871/3, T16#.
PY - 2023
Y1 - 2023
N2 - At regular intervals, aircraft and their components, such as engines, are inspected following specified maintenance, repair and overhaul (MRO) guidelines. The modular design of the engines supports complete or partial disassembly for inspection of all relevant parts. The often used bolted joints are significantly altered by the harsh environmental conditions, severely increasing the disassembly effort usually carried out manually. Using tools like a ratchet or rotary impact wrenches, workers apply torque on the screw head to loosen the screw. If the loosening torque exceeds the material limits, screw heads are torn off, leaving the shaft in the base thread. This article presents a strategy to prevent disassembly damages through precise monitoring of the loosening torques and angle of rotation. Based on this data, a machine learning algorithm detects and predicts potential breakage, to allow adapted disassembly strategies to prevent complex rework. The algorithm will classify potential damage into different categories. Preliminary testing proved the applicability of machine learning toward aircraft disassembly.
AB - At regular intervals, aircraft and their components, such as engines, are inspected following specified maintenance, repair and overhaul (MRO) guidelines. The modular design of the engines supports complete or partial disassembly for inspection of all relevant parts. The often used bolted joints are significantly altered by the harsh environmental conditions, severely increasing the disassembly effort usually carried out manually. Using tools like a ratchet or rotary impact wrenches, workers apply torque on the screw head to loosen the screw. If the loosening torque exceeds the material limits, screw heads are torn off, leaving the shaft in the base thread. This article presents a strategy to prevent disassembly damages through precise monitoring of the loosening torques and angle of rotation. Based on this data, a machine learning algorithm detects and predicts potential breakage, to allow adapted disassembly strategies to prevent complex rework. The algorithm will classify potential damage into different categories. Preliminary testing proved the applicability of machine learning toward aircraft disassembly.
KW - Damage Detection using Machine Learning
KW - Disassembly
KW - Threaded Fasteners
UR - http://www.scopus.com/inward/record.url?scp=85164302663&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2023.02.081
DO - 10.1016/j.procir.2023.02.081
M3 - Conference article
AN - SCOPUS:85164302663
VL - 116
SP - 480
EP - 485
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 30th CIRP Life Cycle Engineering Conference, LCE 2023
Y2 - 15 May 2023 through 17 May 2023
ER -