In-Process Data Integration for Laser Powder Bed Fusion Additive Manufacturing

Milica Perisic, Yan Lu, Albert T. Jones
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Abstract

Additive manufacturing (AM) is a powerful technology that can create complex metallic parts and has the potential to improve the economic bottom line for various industries. However, due to process instabilities, and the resulting material defects that impact the part quality, AM still isn’t as widely used as it could be. To overcome this situation, it is crucial to develop an environment for easy, in-process monitoring and real-time control to detect process anomalies and predict part defects as quickly as possible. AM in-process monitoring measures various process variables and the sensors generate large volumes of structured or unstructured, 1D, 2D, and 3D data, some of which are acquired at very high frequencies. Integration of such data and their analysis are necessary for effective in-process monitoring and real-time control, but they are facing many challenges due to the characteristics of AM in-process data. This paper provides an overview of different in-process monitoring data sources and their connection methods and addresses the integration issues associated with acquiring and fusing the data for both on-fly control and offline analysis. The paper also presents a guideline to help high-speed data integration. This guideline can help users to decide the best data-integration configuration for a specific use case.
激光粉末床熔融增材制造过程中的数据集成
增材制造(AM)是一项强大的技术,可以制造复杂的金属零件,并有可能提高各行业的经济底线。然而,由于工艺的不稳定性,以及由此产生的影响零件质量的材料缺陷,增材制造仍然没有得到广泛的应用。为了克服这种情况,开发一个简单的过程监控和实时控制环境至关重要,以尽快检测过程异常并预测零件缺陷。增材制造过程监控测量各种过程变量,传感器生成大量结构化或非结构化、1D、2D和3D数据,其中一些数据以非常高的频率获得。这些数据的集成和分析是有效的过程监控和实时控制所必需的,但由于AM过程数据的特点,它们面临着许多挑战。本文概述了不同的进程内监控数据源及其连接方法,并解决了与获取和融合在线控制和离线分析数据相关的集成问题。本文还提出了有助于高速数据集成的指导方针。该指南可以帮助用户为特定用例决定最佳的数据集成配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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