IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process

Processes Pub Date : 2024-06-07 DOI:10.3390/pr12061180
Sebastian Hartmann, Bohdan Vykhtar, Nele Möbs, I. Kelbassa, Peter Mayr
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Abstract

Additive manufacturing processes are prone to production errors. Specifically, the unique physical conditions of Laser-Directed Energy Deposition (DED-L) lead to unexpected process anomalies resulting in subpar part quality. The resulting costs and lack of reproducibility are two major barriers hindering a broader adoption of this innovative technology. Combining sensor data with data from relevant steps before and after the production process can lead to an increased understanding of when and why these process anomalies occur. In the present study, an IoT-based data mining framework is presented to assess the stability of processing Ti6Al4V on an industrial-grade DED-L machine. The framework employs an edge-cloud computing methodology to collect data efficiently and securely from various steps in the part lifecycle. During manufacturing, multiple sensors are employed to monitor the essential process characteristics in situ. Mechanical properties of the 160 printed specimens were obtained using appropriate destructive testing. All data are stored on a central database and can be accessed via the web for data analytics. The results prove the successful implementation of the proposed IoT framework but also indicate a lack of process stability during manufacturing. The occurring part errors can only be partially correlated with anomalies in the in situ sensor data.
基于物联网的数据挖掘框架,用于激光定向能量沉积过程的稳定性评估
快速成型制造工艺容易出现生产错误。具体来说,激光定向能量沉积(DED-L)的独特物理条件会导致意想不到的工艺异常,从而导致零件质量不合格。由此产生的成本和缺乏可重复性是阻碍这一创新技术更广泛应用的两大障碍。将传感器数据与生产流程前后相关步骤的数据相结合,可以加深对这些流程异常发生的时间和原因的理解。本研究提出了一个基于物联网的数据挖掘框架,用于评估在工业级 DED-L 机器上加工 Ti6Al4V 的稳定性。该框架采用边缘云计算方法,从零件生命周期的各个步骤高效、安全地收集数据。在制造过程中,采用多个传感器对基本工艺特征进行现场监控。通过适当的破坏性测试获得 160 个打印试样的机械性能。所有数据都存储在中央数据库中,并可通过网络进行数据分析。结果证明了所提出的物联网框架的成功实施,但也表明在制造过程中缺乏工艺稳定性。出现的部件误差只能与现场传感器数据的异常情况部分相关联。
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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