Effects of defects in automated fiber placement laminates and its correlation to automated optical inspection results

Böckl, Benno; Wedel, André; Misik, Adam; Drechsler, Klaus

doi:10.1177/07316844221093273

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Automated Fibre Placement (AFP) is an advanced composite manufacturing method for forming strong and lightweight components from strips of reinforced fibres known as tows. It is commonly used in quality-critical industries such as aerospace, where quality inspection and assurance are paramount (Palardy-Sim et al, 2019b;Böckl et al, 2023). Most existing inspection techniques are implemented via manual human examination strip by strip, which is time-consuming, and thus a major production bottleneck (Meister et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in automated fibre placement: learning with data limitations

Ghamisi,

Charter,

et al. 2024

Front. Manuf. Technol.

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Introduction: Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective samples for effective training. However, the scarcity of such labelled data poses a challenge.Methods: To overcome this limitation, we present a comprehensive framework for defect detection and localization in Automated Fibre Placement. Our approach combines unsupervised deep learning and classical computer vision algorithms, eliminating the need for labelled data or manufacturing defect samples. It efficiently detects various surface issues while requiring fewer images of composite parts for training. Our framework employs an innovative sample extraction method leveraging AFP’s inherent symmetry to expand the dataset. By inputting a depth map of the fibre layup surface, we extract local samples aligned with each composite strip (tow).Results: These samples are processed through an autoencoder, trained on normal samples for precise reconstructions, highlighting anomalies through reconstruction errors. Aggregated values form an anomaly map for insightful visualization. The framework employs blob detection on this map to locate manufacturing defects.Discussion: The experimental findings reveal that despite training the autoencoder with a limited number of images, our proposed method exhibits satisfactory detection accuracy and accurately identifies defect locations. Our framework demonstrates comparable performance to existing methods, while also offering the advantage of detecting all types of anomalies without relying on an extensive labelled dataset of defects.

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Section: Introductionmentioning

confidence: 99%

Anomaly detection in automated fibre placement: learning with data limitations

Ghamisi,

Charter,

et al. 2024

Front. Manuf. Technol.

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Fatigue and residual strain monitoring for thermoplastic composite using embedded FBG inscribed in highly‑birefringent side-hole elliptical core optical fiber

Wachtarczyk,

Yadav,

Błachut

et al. 2024

Measurement

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In-situ consolidation of thermoplastic composites by automated fiber placement: Characterization of defects

Fereidouni,

Hoa

2024

Journal of Thermoplastic Composite Materials

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The emergence of automated manufacturing of composites has not only transformed the manufacturing of optimized and geometrically complex structures but has also expanded the promising prospect of in-situ manufacturing of thermoplastic composites (TPC), where both material placement and consolidation are carried out by automated fiber placement (AFP) equipment, streamlining the process into single step manufacturing. However, the inherent complexities in different aspects of robotic automation, imperfections in the supplied material, and the occurrence of multi-physical phenomena during in-situ consolidation introduce various manufacturing-induced defects. While the defects in thermoset composites (TSC) made by AFP have been widely studied in the past, this study explores the diverse defects at micro and macro scales for TPCs made by AFP, with a focus on carbon-fiber/poly-ether-ether-ketone (CF/PEEK) tapes consolidated using hot gas torch (HGT) heating system. An overview of defects and associated characteristics is presented across three phases: defects in supplied impregnated tapes, defects and limitations in performance of AFP system, and defects in the final in-situ consolidated composite. For the defects subject to studies in the past, the description is limited to a concise review, while those with limited understanding are supported by new empirical observations in this work.

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Effects of defects in automated fiber placement laminates and its correlation to automated optical inspection results

Cited by 4 publications

References 21 publications

Anomaly detection in automated fibre placement: learning with data limitations

Anomaly detection in automated fibre placement: learning with data limitations

Fatigue and residual strain monitoring for thermoplastic composite using embedded FBG inscribed in highly‑birefringent side-hole elliptical core optical fiber

In-situ consolidation of thermoplastic composites by automated fiber placement: Characterization of defects

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