基于选择性激光熔化声学信号的熔化状态识别和工艺性能研究

IF 1.7 4区 工程技术 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Dongju Chen, Anqing Wang, Peng Wang, Na Li
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引用次数: 0

摘要

为了监测和精确管理选择性激光熔化(SLM)成形过程,提高整体质量,建立了声信号采集实验平台,采集了长度为120mm的铜锡合金35条单轨成形过程中的声信号。SLM成形过程的监测包括时域和频域分析,利用线性预测技术提取SLM过程特征,以及支持向量机(SVM)模型、反向传播(BP)神经网络模型和卷积神经网络模型。结果表明,在给定范围内,通过提取时间域和频域特征可以识别出过熔化状态,但正常状态和未熔化状态难以区分。优化后的卷积神经网络模型识别率为99%,BP神经网络有效识别率为90%,SVM模型对三种状态的综合分类率为83.14%。相比之下,卷积神经网络模型在监测方面表现最好,为声信号分析和在线SLM质量监测提供了框架和参考点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of melting state identification and process performance based on selective laser melting acoustic signals
An acoustic signal acquisition experiment platform was constructed to gather the acoustic signals throughout the formation of 35 single-tracks of a 120 mm length copper-tin alloy in order to monitor and precisely manage the selective laser melting (SLM) forming process and enhance overall quality. The monitoring of the SLM forming process includes the analysis of the time and frequency domains, the extraction of the SLM process features using linear prediction techniques, and the development of support vector machine (SVM) model, back-propagation (BP) neural network models, and convolutional neural network models. The results show that the over-melted state can be identified by extracting time and frequency-domain features over a given range, but the normal and unmelted states are difficult to distinguish. The convolutional neural network model had a recognition rate of 99%, the BP neural network had an effective recognition rate of 90%, and the SVM model had a combined classification rate of 83.14% for the three states after optimization. In contrast, the convolutional neural network model performs best in monitoring and offers a framework and point of reference for acoustic signal analysis and online SLM quality monitoring.
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来源期刊
CiteScore
3.60
自引率
9.50%
发文量
125
审稿时长
>12 weeks
期刊介绍: The Journal of Laser Applications (JLA) is the scientific platform of the Laser Institute of America (LIA) and is published in cooperation with AIP Publishing. The high-quality articles cover a broad range from fundamental and applied research and development to industrial applications. Therefore, JLA is a reflection of the state-of-R&D in photonic production, sensing and measurement as well as Laser safety. The following international and well known first-class scientists serve as allocated Editors in 9 new categories: High Precision Materials Processing with Ultrafast Lasers Laser Additive Manufacturing High Power Materials Processing with High Brightness Lasers Emerging Applications of Laser Technologies in High-performance/Multi-function Materials and Structures Surface Modification Lasers in Nanomanufacturing / Nanophotonics & Thin Film Technology Spectroscopy / Imaging / Diagnostics / Measurements Laser Systems and Markets Medical Applications & Safety Thermal Transportation Nanomaterials and Nanoprocessing Laser applications in Microelectronics.
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