论文标题
为了使用深层卷积神经网络启用可靠的质量监测系统，以进行增材制造过程
Toward Enabling a Reliable Quality Monitoring System for Additive Manufacturing Process using Deep Convolutional Neural Networks
论文作者
论文摘要
增材制造（AM）是智能行业的关键组成部分。在本文中，我们使用深卷积神经网络（CNN）模型为AM过程提出了自动质量分级系统。使用内部和表面缺陷的图像在材料的一层沉积中进行了离线训练，并通过研究以不同的挤出机速度和温度对AM过程中的失败进行研究，并在线测试。该模型证明了94％的精度和96％的特异性，以及在FSCORE的三个分类器测量中的准确性，高于75％，灵敏度和精确度，用于实时五年级的打印过程质量。提出的在线模型为AM过程添加了一个自动，一致和非接触质量控制信号，该信号在完全构建后消除了对零件的手动检查。机器也可以使用质量监视信号来建议通过实时调整参数来提出补救措施。提出的质量预测模型是任何类型的AM机器的概念验证，可在限制时间和材料的浪费的同时生产质量较少的可靠零件。
Additive Manufacturing (AM) is a crucial component of the smart industry. In this paper, we propose an automated quality grading system for the AM process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The model demonstrates the accuracy of 94% and specificity of 96%, as well as above 75% in three classifier measures of the Fscore, the sensitivity, and precision for classifying the quality of the printing process in five grades in real-time. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built. The quality monitoring signal can also be used by the machine to suggest remedial actions by adjusting the parameters in real-time. The proposed quality predictive model serves as a proof-of-concept for any type of AM machines to produce reliable parts with fewer quality hiccups while limiting the waste of both time and materials.
