Automated recognition and correction of warp deformation in extrusion additive manufacturing
Automated recognition and correction of warp deformation in extrusion additive manufacturing
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DOI：
10.1016/j.addma.2022.102838
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发表时间：
2022-05
影响因子：
11
通讯作者：
Douglas A. J. Brion;M. Shen;S. W. Pattinson
中科院分区：
文献类型：
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作者：
Douglas A. J. Brion;M. Shen;S. W. Pattinson
翘曲变形是添加剂制造中常见的误差。它通常是由材料冷却时制造零件中的残余内应力引起的。这些错误很难预防或纠正，因为它们会随着时间的推移而积累，因此只有在导致它们的行为很长时间之后才能看到。因此，挤出添加剂制造中的现有工作试图进行翘曲检测，但没有进行校正或预防。我们报告了一种结合深度学习、计算机视觉和专家启发式方法来纠正或防止翘曲的混合方法。我们使用不同的标记图像来训练深度卷积神经网络，以实时识别经纱。我们从检测候选中计算五个度量来预测翘曲变形的严重程度，并按比例更新打印设置。这使得能够在印刷期间和将来的印刷中首次演示自动翘曲检测和校正。
Warp deformation is a common error encountered in additive manufacturing. It is typically caused by residual internal stresses in the manufactured part that arise as material cools. These errors are challenging to prevent or correct as they build over time and thus are only visible long after the actions that caused them. As a result, existing work in extrusion additive manufacturing has attempted warp detection but not correction or prevention. We report a hybrid approach combining deep learning, computer vision, and expert heuristics to correct or prevent warp. We train a deep convolutional neural network using diverse labelled images to recognise warp in real-time. We compute five metrics from detection candidates to predict the severity of warp deformation and proportionately update print settings. This enables the first demonstration of automated warp detection and correction both during printing and for future prints.