Integration of eddy current sensors into repair patches for fatigue reinforcement at rivet holes

Schmid, Sebastian; Martens, Ulrike; Schomburg, Werner Karl; Schröder, Kai‐Uwe

doi:10.1111/str.12387

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Previously, the attached flexible PCB coils proved to be sensitive to electrical discharge machining (EDM) notches in static conditions [1]. The study documented in this article is a step toward more realistic applications: fatigue cracks developed from starter notches radially introduced around circular holes in aluminum alloy coupons under the condition of applied tensile load, and it has applications in the aviation industry [8,17].…”

Section: Introductionmentioning

confidence: 92%

“…This shortcoming is evident for inspections of multi-layer structures, where structure disassembly may be required to allow access to the area of interest. However, nowadays, with the advancements in printed electronics and wireless communication, eddy-current techniques are resurging as an in situ means of NDT, i.e., in the form of structural health monitoring (SHM) for damage detection [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Crack Growth Monitoring with Structure-Bonded Thin and Flexible Coils

Mandache

Desnoyers

Bombardier

2022

Sensors

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Structural health monitoring with thin and flexible eddy-current coils is proposed for in situ detection and monitoring of fatigue cracks in metallic aircraft structures, providing a promising means of crack sizing. This approach is seen as an efficient replacement to periodic inspections, as it brings economic and safety benefits. As such, printed-circuit-board eddy-current coils are viable for in situ crack monitoring for multi-layer, electrically conductive structures. They are minimally invasive and could be attached to or embedded into the evaluated structure. This work focuses on the monitoring of fatigue crack growth from a fastener hole with structure-bonded, thin, and flexible spiral coils. Numerical simulations were used for optimization of the driving frequency and selection of crack-sensitive coil parameters. The article also demonstrates the fatigue crack detection capabilities using spiral coils attached to a 7075-T6 aluminum coupon.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Crack Growth Monitoring with Structure-Bonded Thin and Flexible Coils

Mandache

Desnoyers

Bombardier

2022

Sensors

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“…Stone and Krishnamurthy 144 proposed a thrust force controller using an ANN to reduce the development of delaminations caused by the entry and removal of a drill bit to an FRP. Machine learning was employed to classify the Visual inspection [85][86][87][88][89][90] Ultrasonic inspection [91][92][93][94] Eddy current [95][96][97][98][99] Radiography 100-105 (e.g., x-ray) Thermography 97,[106][107][108][109][110] Acoustic emission [111][112][113][114][115] Fiber optic sensors [116][117][118][119][120][121] (e.g., fiber Bragg grating) Piezoelectric transducers [122][123][124][125] Laser vibrometry [126][127][128][129][130][131][132] failure methods of composite plates bolted together. 145 A beneficial application of ML is prediction making.…”

Section: Composite Applications With Machine Learningmentioning

confidence: 99%

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Nelon

Myers

Hall

2022

Journal of Composite Materials

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Machine learning (ML) has emerged as a useful predictive tool based on mathematical and statistical relationships for various engineering problems. The pairing of structural health monitoring (SHM) and nondestructive evaluation (NDE) methods with ML algorithms has yielded beneficial results in addressing the damage state of a material or system. Damage state descriptions addressed with ML include detecting a damage mechanism, locating a mechanism, identifying the type of mechanism, assessing the extent of the damage mechanism, and estimating the useful remaining life of a material or system. Damage evaluation research of composite materials has progressed with the increased usage of composite structural elements in the aerospace industry. NDE methods are a viable candidate for pairing with ML algorithms to improve damage state monitoring of composite materials due to the complexity associated with the structure of composites. Fiber-reinforced polymers (FRP), for example, contain at least two constituent materials a fiber and matrix material whose mechanical behavior and interactions contribute to the performance of an FRP. Unlike conventional composite analytical models that require explicit information about the constituents and microstructure of a laminate, an ML algorithm can construct damage evaluation predictions when employing exclusively past operational performance or data from an SHM or NDE method. A researcher determines the type of data selected when applying an ML model for trend analysis, anomaly detection, or prediction making. However, no one specific input feature is required for utilizing an ML model, and examples of possible data features include material properties, physical dimensions, and collected evaluation data. In the present review, applications of ML combined with the damage state evaluation of composite materials, particularly examining FRPs, are discussed to demonstrate the predictive capabilities of ML and its viability for future applications, especially in industrial environments, to minimize costs and improve damage detection rates.

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“…In addition to predictive maintenance, this category also contains the new repairing methodologies and techniques, which include, besides many others integrating sensors in a repair patch to reduce crack growth but at the same time monitor the fatigue status [37].…”

Section: Category 3: Process Development In Maintenance Repair and Ov...mentioning

confidence: 99%

Technology Roadmap for Aircraft Maintenance, Repair and Overhaul

Ichou

Veress

2022

RepTudKoz

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Nowadays, the demand for aircraft Maintenance, Repair and Overhaul (MRO) is constantly growing. The market size of the European MRO segment is estimated to be USD 206.13 billion in 2022, growing at a Compound Annual Growth Rate (CAGR) of 2.8% between 2022 and 2030 [1]. This forecast is a good indication of the growth in the number of incoming assignments. As a result, airlines and aircraft operators will increasingly rely on companies with experience in the MRO field to perform maintenance and repair work. Furthermore, as many airlines now choose to outsource maintenance and repair, this will further increase the load on MRO companies. As the number of incoming jobs increases, the companies concerned are constantly looking for and implementing new and better methods and technologies, with another aim of gaining a larger market share. Moreover, as there is still scope for the development and introduction of new technologies and processes in this area, a significant number of research and development projects are underway or in the pipeline. Therefore, the main objective of this study is to use the available information to present a generalised technology roadmap for the companies involved in MRO activities and, on this basis, to collect, present and categorise the state-of-the-art developments in the MRO sector, highlighting what the future will hold for companies that incorporate these revolutionary innovations into their daily work processes.

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Integration of eddy current sensors into repair patches for fatigue reinforcement at rivet holes

Cited by 4 publications

References 24 publications

Crack Growth Monitoring with Structure-Bonded Thin and Flexible Coils

Crack Growth Monitoring with Structure-Bonded Thin and Flexible Coils

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Technology Roadmap for Aircraft Maintenance, Repair and Overhaul

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