审计:通过物理和数字孪生在增材制造中的功能资格

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-10-04 DOI:10.1115/1.4063655
Michael Biehler, Reinaldo Mock, Shriyanshu Kode, Maham Mehmood, Palin Bhardwaj, Jianjun Shi
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引用次数: 0

摘要

增材制造(AM)已经彻底改变了我们设计、原型和生产具有前所未有几何形状的复杂零件的方式。然而,缺乏对3D打印部件功能特性的了解阻碍了它们在可靠性和耐用性至关重要的关键应用中的应用。本文提出了一种通过物理和数字孪生对3d打印部件进行功能鉴定的新方法。物理双胞胎是在与功能部件相同的工艺条件下打印的部件,并经过广泛的(破坏性)测试以确定其机械,热和化学性能。数字双胞胎是物理双胞胎的虚拟复制品,使用基于感兴趣部分的3D形状的有限元分析(FEA)模拟生成。我们提出了一种新的迁移学习方法,专门用于融合来自多个来源的各种非结构化3D形状数据和过程输入。该方法在预测3d打印晶格结构的功能特性方面取得了显著的效果。从工程角度出发,本文介绍了一种全面创新的3d打印部件功能鉴定方法。通过将物理和数字双胞胎的优势与迁移学习相结合,我们的方法为在安全关键应用中广泛采用3D打印开辟了可能性。在方法上,这项工作提出了迁移学习技术的重大进步,特别是解决了多源(例如,数字和物理双胞胎)和多输入(例如,3D形状和过程变量)迁移学习的挑战。
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AUDIT: Functional Qualification in Additive Manufacturing via Physical and Digital Twins
Abstract Additive manufacturing (AM) has revolutionized the way we design, prototype, and produce complex parts with unprecedented geometries. However, the lack of understanding of the functional properties of 3D printed parts has hindered their adoption in critical applications where reliability and durability are paramount. This paper proposes a novel approach to the functional qualification of 3D-printed parts via physical and digital twins. Physical twins are parts that are printed under the same process conditions as the functional parts and undergo a wide range of (destructive) tests to determine their mechanical, thermal, and chemical properties. Digital twins are virtual replicas of the physical twins that are generated using finite element analysis (FEA) simulations based on the 3D shape of the part of interest. We propose a novel approach to transfer learning, specifically designed for the fusion of diverse, unstructured 3D shape data and process inputs from multiple sources. The proposed approach has demonstrated remarkable results in predicting the functional properties of 3D-printed lattice structures. From an engineering standpoint, this paper introduces a comprehensive and innovative methodology for the functional qualification of 3D-printed parts. By combining the strengths of physical and digital twins with transfer learning, our approach opens up possibilities for the widespread adoption of 3D printing in safety-critical applications. Methodologically, this work presents a significant advancement in transfer learning techniques, specifically addressing the challenges of multi-source (e.g., digital and physical twins) and multi-input (e.g., 3D shapes and process variables) transfer learning.
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
期刊最新文献
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