Surface damage evaluation of honeycomb sandwich aircraft panels using 3D scanning technology

Reyno, Tyler; Marsden, Catharine; Wowk, Diane

doi:10.1016/j.ndteint.2018.03.007

Cited by 39 publications

(18 citation statements)

References 36 publications

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“…Based on geometry, honeycombs, corrugated cores, lattice cores and different kinds of truss structures are regarded as the most common types of core materials. Honeycomb sandwich composites are extensively used in aerospace and automotive applications because of their moisture‐resistant hollow structure that provides an impressive impact resistance . Honeycomb cores are also continuously being reinforced with other materials such as metallic tubes and aluminum grids to further improve properties like blast resistance, energy absorption, and stiffness, which make them ideal for aerospace applications.…”

Section: Sandwich Structuresmentioning

confidence: 99%

A review on recent advances in sandwich structures based on polyurethane foam cores

et al. 2020

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Sandwich composites with a polyurethane foam (PUF) core have become a versatile set of materials with applications ranging from basic construction elements to advanced engineering materials. This diverse range of applications has provided these sandwich materials with a distinctive position in today's engineering world. This review focuses on the broad research that has been carried out on PUF core‐based sandwich composites over the years—especially in the last decade. Thorough details have been presented focusing on basic PUF core sandwich structures, advanced PUF core sandwich composites with different kinds of reinforcements and complex structures such as hybrid sandwich panels. All the research presented here has been categorized carefully, such as the types of materials used in various studies, the types of reinforcements and testing techniques used, including mechanical, electrical, dynamic, thermal and acoustic methods etc. Comparisons have also been made based on similar materials, similar testing procedures and similar application areas. Research areas have been highlighted by giving a deep insight into the most promising research, manufacturing techniques and improvement procedures. Major consumption areas and application sectors for PUF core sandwich composites have also been discussed in this review. Finally, conclusions have been made based on the results found from a literature investigation.

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Section: Sandwich Structuresmentioning

confidence: 99%

A review on recent advances in sandwich structures based on polyurethane foam cores

et al. 2020

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“…An ECT pencil probe was retrofitted to a servo controlled CScan apparatus and probe output was calibrated to lift-off (mm). Measurements were compared to those taken on the same panel using an optical 3D scanning method [44], as well as with identifications made by inspection personnel as shown on the panel in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Surface Profiling and Core Evaluation of Aluminum Honeycomb Sandwich Aircraft Panels Using Multi-Frequency Eddy Current Testing

Reyno¹,

Underhill²,

Krause³

et al. 2017

Preprint

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Surface damage on honeycomb aircraft panels is often measured manually, and is therefore subject to variation based on inspection personnel. Eddy current testing (ECT) is sensitive to variations in probe-to-specimen spacing, or lift-off, and is thus promising for high resolution profiling of surface damage on aluminum panels. Lower frequency testing also allows inspection through the face sheet, an advantage over optical 3D scanning methods. This paper presents results from the ECT inspection of surface damage on an approximately flat aluminum honeycomb aircraft panel, and compares the measurements to those taken using optical 3D scanning technology. An ECT C-Scan of the dented panel surface was obtained by attaching the probe to a robotic scanning apparatus. Data was taken simultaneously at four frequencies of 25, 100, 400 and 1,600 kHz. A reference surface was then defined that approximated the original, undamaged panel surface, which also compensated for the effects of specimen tilt and thermal drift within the ECT instrument. Data was converted to lift-off using height calibration curves developed for each probe frequency. A damage region of 22,550 mm 2 area with dents ranging in depth from 0.13-1.01 mm was analyzed. The method was accurate at 1,600 kHz to within 0.05 mm (2σ) when compared with 231 measurements taken via optical 3D scanning. Testing at 25 kHz revealed a 3.2 mm cell size within the honeycomb core, which was confirmed via destructive evaluation. As a result, ECT demonstrates potential for implementation as a method for rapid in-field aircraft panel surface damage assessment.

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“…Data acquisition methods for free-form have been discussed in [10]. Specifically, point cloud data-based identification has been applied in aircraft panel dent detection [17] and in engine surface inspection [18]. Scanned data has also been applied in surface defect detection through visual inspection of color difference in [19,20].…”

Section: Related Workmentioning

confidence: 99%

Anomaly detection in Skin Model Shapes using machine learning classifiers

Yacob

Semere

Nordgren

2019

Int J Adv Manuf Technol

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The concept of Skin Model Shapes has been proposed as a method to generate digital twins of manufactured parts and is a new paradigm in the design and manufacturing industry. Skin Model Shapes use discrete surface representation schemes, such as meshes and point clouds, to represent surfaces, which makes them enablers to perform an accurate tolerance analysis and surface inspection. However, online inspection of manufactured parts through use of Skin Model Shapes has not been extensively studied. Moreover, the existing geometric variation inspection techniques do not detect unfamiliar changes within tolerance, which could be the precursors to the onset of the manufacturing of out of tolerance part. To detect the unfamiliar changes, as anomalies, and categorize them as systematic and random variations, some unique surface characteristics can be extracted and studied. Random surface deviations exhibit narrow normal distributions, and systematic deviations, on the other hand, exhibit wide, skewed, and multimodal distributions. Using those surface characteristics as key traits, machine learning classifiers can be used to classify deviations into systematic and random variations. To illustrate the method, multiple samples from a truck component manufacturing line were scanned and the collected 3D point cloud data was used to extract features. A prediction score of 97-100% can be achieved by decision tree, k-nearest neighbor, support vector machines, and ensemble classifiers. The purposed approach is expected to extend the existing online inspection approaches and applications of Skin Model Shapes in quality control.

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Surface damage evaluation of honeycomb sandwich aircraft panels using 3D scanning technology

Cited by 39 publications

References 36 publications

A review on recent advances in sandwich structures based on polyurethane foam cores

A review on recent advances in sandwich structures based on polyurethane foam cores

Surface Profiling and Core Evaluation of Aluminum Honeycomb Sandwich Aircraft Panels Using Multi-Frequency Eddy Current Testing

Anomaly detection in Skin Model Shapes using machine learning classifiers

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