Image processing based detection of the fibre orientation during depth-controlled laser ablation of CFRP monitored by optical coherence tomography

Holder, Daniel; Buser, Matthias; Boley, Steffen; Weber, Rudolf; Graf, Thomas

doi:10.1016/j.matdes.2021.109567

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…Schmitt et al [30] developed a real-time machine-vision system based on the Hough transform with the main purpose of the real-time, accurate and robust fiber orientation detection under industrial conditions. Holder et al [31] used the Hough transform to determine the fiber orientation in optical coherence tomography (OCT) images. Nelson et al [32] used the Radon transform to measure the fiber orientation of local 2D images extracted from the 3D ultrasonic data.…”

Section: Structural and Deep Learning Methodsmentioning

confidence: 99%

Automated Industrial Composite Fiber Orientation Inspection Using Attention-Based Normalized Deep Hough Network

Zhang

Liang

2023

Micromachines

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Fiber-reinforced composites (FRC) are widely used in various fields due to their excellent mechanical properties. The mechanical properties of FRC are significantly governed by the orientation of fibers in the composite. Automated visual inspection is the most promising method in measuring fiber orientation, which utilizes image processing algorithms to analyze the texture images of FRC. The deep Hough Transform (DHT) is a powerful image processing method for automated visual inspection, as the “line-like” structures of the fiber texture in FRC can be efficiently detected. However, the DHT still suffers from sensitivity to background anomalies and longline segments anomalies, which leads to degraded performance of fiber orientation measurement. To reduce the sensitivity to background anomalies and longline segments anomalies, we introduce the deep Hough normalization. It normalizes the accumulated votes in the deep Hough space by the length of the corresponding line segment, making it easier for DHT to detect short, true “line-like” structures. To reduce the sensitivity to background anomalies, we design an attention-based deep Hough network (DHN) that integrates attention network and Hough network. The network effectively eliminates background anomalies, identifies important fiber regions, and detects their orientations in FRC images. To better investigate the fiber orientation measurement methods of FRC in real-world scenarios with various types of anomalies, three datasets have been established and our proposed method has been evaluated extensively on them. The experimental results and analysis prove that the proposed methods achieve the competitive performance against the state-of-the-art in F-measure, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE).

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Section: Structural and Deep Learning Methodsmentioning

confidence: 99%

Automated Industrial Composite Fiber Orientation Inspection Using Attention-Based Normalized Deep Hough Network

Zhang

Liang

2023

Micromachines

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“…Carbon fiber reinforced plastic (CFRP) has gained widespread use in the primary load-bearing structure of airplanes, such as wings and tailplanes, due to its high specific strength, specific modulus, and good corrosion resistance and fatigue resistance. [1][2][3][4] It is gradually transitioning towards large and integrated structures, such as the entire composite fuselages utilized in aircraft models like the B787 and A350. However, throughout the service life of an aircraft, various damages are inevitable, including those caused by bird strikes, lightning strikes, erosion by wind and rain, and accidental damage, leading to an overall decrease in structural integrity.…”

Section: Introductionmentioning

confidence: 99%

“…Carbon fiber reinforced plastic (CFRP) has gained widespread use in the primary load‐bearing structure of airplanes, such as wings and tailplanes, due to its high specific strength, specific modulus, and good corrosion resistance and fatigue resistance 1–4 . It is gradually transitioning towards large and integrated structures, such as the entire composite fuselages utilized in aircraft models like the B787 and A350.…”

Section: Introductionmentioning

confidence: 99%

Layer ablation and surface textured of Carbon fiber reinforced plastics by infrared pulsed laser

Li,

Wang,

Yang

et al. 2023

Polymer Composites

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For high‐quality composite bonded repairs, the research utilized a 1064 nm infrared pulsed laser to obtain geometry and texturing surface. The impact of grinding and laser treatment (0.4, 0.7, and 1.5 J/cm2) on the surface features of composite material was investigated, along with the tensile strength and failure modes. Findings demonstrated that laser treatment could significantly enhance surface roughness parameters and wetting properties compared to grinding. Additionally, an optimal laser energy density of 0.7 J/cm2 achieves complete resin removal without fiber damage while creating a laser‐induced periodic surface structure (LIPSS) based on a fiber weaving texture. The research revealed the synergistic bonding mechanism of “submicron mechanical locking” and “micron anchoring.” Laser treatment enhances the tensile strength of repaired specimens to 445 MPa, which was 90% of the original specimens, and substrate fracture was the associated failure mode.Highlights This paper applies laser technology to simultaneously achieve 3D geometry and surface texture on composites bonded repairs. A laser‐induced periodic surface structure was achieved while the resin was completely removed without carbon fiber damage. The tensile recovery rate of the laser‐treated sample was satisfied with the requirements of aircraft maintenance.

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“…The in situ measurement of the current depth during processing has already been demonstrated using optical coherence tomography for percussion drilling of microholes [15,17] and for areal micromachining [18,19]. This approach is also suitable for the production of grooves, as shown in [6] for groove depths up to 500 µm.…”

Section: Introductionmentioning

confidence: 99%

Analytical Model for the Depth Progress during Laser Micromachining of V-Shaped Grooves

2022

Self Cite

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An analytical model is presented that allows predicting the progress and the final depth obtained by laser micromachining of grooves in metals with ultrashort laser pulses. The model assumes that micromachined grooves feature a V-shaped geometry and that the fluence absorbed along the walls is distributed with a linear increase from the edge to the tip of the groove. The depth progress of the processed groove is recursively calculated based on the depth increments induced by successive scans of the laser beam along the groove. The experimental validation confirms the model and its assumptions for micromachining of grooves in a Ti-alloy with femtosecond pulses and different pulse energies, repetition rates, scanning speeds and number of scans.

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Image processing based detection of the fibre orientation during depth-controlled laser ablation of CFRP monitored by optical coherence tomography

Cited by 10 publications

References 26 publications

Automated Industrial Composite Fiber Orientation Inspection Using Attention-Based Normalized Deep Hough Network

Automated Industrial Composite Fiber Orientation Inspection Using Attention-Based Normalized Deep Hough Network

Layer ablation and surface textured of Carbon fiber reinforced plastics by infrared pulsed laser

Analytical Model for the Depth Progress during Laser Micromachining of V-Shaped Grooves

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