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Survey on artificial intelligence for additive manufacturing

机译:增材制造人工智能调查

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Additive manufacturing of three-dimensional objects are now more and more realised through 3D printing, known as an evolutional paradigm in the manufacturing industry. Artificial intelligence is currently finding wide applications to 3D printing for an intelligent, efficient, high quality, mass customised and service-oriented production process. This paper presents a comprehensive survey of artificial intelligence in 3D printing. Before a printing task begins, the printability of given 3D objects can be determined through a printability checker using machine learning. The prefabrication of slicing is accelerated through parallel slicing algorithms and the path planning is optimised intelligently. In the aspect of service and security, intelligent demand matching and resource allocation algorithms enable a Cloud service platform and evaluation model to provide clients with an on-demand service and access to a collection of shared resources. We also present three machine learning algorithms to detect product defects in the presence of cyber-attacks. Based on the reviews on various applications, printability with multi-indicators, reduction of complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of good opportunities for further research, especially in the era of Industry 4.0.
机译:现在,通过3D打印越来越多地实现了三维对象的增材制造,这在制造业中被称为进化范式。人工智能目前正在3D打印中找到广泛的应用,以实现智能,高效,高质量,大规模定制和面向服务的生产过程。本文对3D打印中的人工智能进行了全面的概述。在开始打印任务之前,可以使用机器学习通过可打印性检查器确定给定3D对象的可打印性。通过并行切片算法可以加速切片的预制,并智能地优化路径规划。在服务和安全方面,智能的需求匹配和资源分配算法使Cloud服务平台和评估模型能够为客户提供按需服务并访问共享资源的集合。我们还提出了三种机器学习算法,可以在存在网络攻击的情况下检测产品缺陷。根据对各种应用的评论,对于具有个性化设计的多指示器,可印刷性,降低复杂性阈值,加快预制速度,实时控制,增强安全性和缺陷检测被认为是进一步研究的好机会,尤其是在那个时代工业4.0版。

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