Abstract

Wire arc additive manufacturing (WAAM) is getting much research attention because of its cost-effectiveness in the metallic production of large and complex parts. In pursuit of best-quality products and minimizing material loss, multimodal process monitoring methods are key. This paper presents the use of acoustic and current signals in identifying one of the critical defects in WAAM, i.e., porosity. Aluminum and unalloyed steel were deposited in a controlled environment which developed different amounts of porosity alongside measurements from current and gas sensors. Feature reduction of the signals was carried out using a combination of wavelet scattering networks and sparse principal component analysis (sPCA). While the models predict porosity reasonably, the dominant features learned by the model are also investigated and reported.

中文翻译：

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使用小波散射网络和稀疏主成分分析预测电弧增材制造 (WAAM) 中的孔隙率

摘要

电弧增材制造 (WAAM) 因其在大型复杂零件金属生产中的成本效益而受到广泛的研究关注。为了追求最优质的产品并最大限度地减少材料损失，多模式过程监控方法是关键。本文介绍了使用声学和电流信号来识别 WAAM 的关键缺陷之一，即孔隙率。铝和非合金钢沉积在受控环境中，随着电流和气体传感器的测量，该环境产生不同数量的孔隙率。结合使用小波散射网络和稀疏主成分分析 (sPCA) 来减少信号的特征。在模型合理预测孔隙度的同时，还研究并报告了模型学到的主要特征。