卷积神经网络在增材制造中的应用综述

IF 3.9 Q2 ENGINEERING, INDUSTRIAL
Mahsa Valizadeh, Sarah Jeannette Wolff
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引用次数: 15

摘要

增材制造(AM)是一种很有前途的数字制造方法,近年来发展迅速。尽管增材制造技术具有快速发展的性质,但由于在打印部件中观察到的各种缺陷,其资格和认证已经放缓。另一方面,卷积神经网络(CNN)作为一种深度学习方法,在过去的十年中受到了广泛的关注,并在处理图像数据方面表现出了出色的性能。深度学习是机器学习的一个子集,指的是任何具有两个以上隐藏层的人工神经网络。本文全面概述了自该领域出现以来CNN在AM过程的几个方面的应用。这篇综述还强调了当前的挑战和可能的解决方案,为未来的研究提供了一个视野。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Network applications in additive manufacturing: A review

Additive manufacturing (AM) is a promising digital manufacturing approach that has seen recent rapid growth. Despite the fast-growing nature of the technology, AM has been slowed by the qualification and certification due to various defects observed in printed parts. On the other hand, Convolutional Neural Networks (CNN), as a deep learning method, have received a great deal of attention over the last decade and demonstrated excellent performance in dealing with image data. Deep learning is a subset of machine learning and refers to any Artificial Neural Network with more than two hidden layers. This article provides a comprehensive overview of CNN’s application to several aspects of the AM process since the emergence of this field. This review also highlights current challenges and possible solutions to provide a horizon for future studies.

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来源期刊
Advances in Industrial and Manufacturing Engineering
Advances in Industrial and Manufacturing Engineering Engineering-Engineering (miscellaneous)
CiteScore
6.60
自引率
0.00%
发文量
31
审稿时长
18 days
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