ABSTRACT

Additive Manufacturing (AM) technologies are experiencing rapid growth in the past decades. Critical objectives for the AM processes are how to ensure product quality and process consistency. The detection and correction of part and process anomalies show great promises and challenges. This paper presents an online laser-based process monitoring and control system to improve the geometric accuracy and in-plane surface quality for the AM process. The point cloud dataset obtained from the 3D laser scanner provides the current part height in the Z direction and in-plane surface depth information for each layer. A Convolutional Neural Network (CNN) model is designed with the pre-trained VGG16 model and validated using the monitoring data to effectively classify the in-plane anomalies. Two developed PID-based online closed-loop control systems are implemented which can significantly reduce the height deviation errors between the fabricated part measurements and design values, and correct the in-plane surface anomalies.

摘要

在过去的几十年中，增材制造（AM）技术正在经历快速发展。AM过程的关键目标是如何确保产品质量和过程一致性。零件和过程异常的检测和纠正显示出巨大的希望和挑战。本文提出了一种基于激光的在线过程监控系统，以提高AM过程的几何精度和平面内表面质量。从3D激光扫描仪获得的点云数据集提供Z方向上的当前零件高度以及每层的平面内表面深度信息。使用预训练的VGG16模型设计卷积神经网络（CNN）模型，并使用监视数据进行验证以有效地对平面异常进行分类。