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Application of Reference-Free Strain Measurement to Assess the Deformation Level of Pre-Deformed Components
Abstract:
In the framework of sustainable manufacturing and circular economy, the reuse of metallic components at the end of their first life (EoL) is a promising strategy to reduce energy consumption and material waste, but it requires an accurate assessment of residual plastic deformation, which strongly affects structural integrity and remaining formability. Conventional full-field techniques such as Digital Image Correlation (DIC) require a reference image of the undeformed state, which is generally unavailable for EoL components. To address this limitation, this work investigates a deep learning–based, reference-free approach for strain estimation directly from a single image of the deformed surface. The method relies on a convolutional neural network architecture derived from VGG-16, trained to regress the in-plane principal strains and their orientation from local image subsets of the surface texture using a synthetically generated dataset based on real material textures and realistic imaging conditions. The trained model is applied to high-resolution optical images of pre-deformed steel and aluminum components from regions subjected to different deformation histories, with partial validation provided by finite element simulations and conventional DIC measurements. Preliminary results show that the proposed approach can distinguish regions with different levels of plastic deformation and provide strain maps consistent with independent mechanical assessments, demonstrating its potential as a rapid, non-destructive tool for deformation mapping and classification of EoL components to support remanufacturing and reuse decisions.
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123-129
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Online since:
April 2026
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