Abstract

The extraction of meaningful features from the monitoring of laser processes is the foundation of new non-destructive quality inspection methods for the manufactured pieces, which has been and remains a growing interest in industry. We present ConvLBM, a novel approach to monitor Laser Based Manufacturing processes in real-time. ConvLBM uses a Convolutional Neural Network model to extract features and quality indicators from raw Medium Wavelength Infrared coaxial images. We demonstrate the ability of ConvLBM to represent process dynamics, and predict quality indicators in two scenarios: dilution estimation in Laser Metal Deposition, and location of defects in laser welding processes. Obtained results represent a breakthrough in the 3D printing of large metal parts, and in the quality control of welding processes. We are also releasing the first large dataset of annotated images of laser manufacturing.

中文翻译：

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卷积激光制造质量监控方法

摘要

从激光加工监控中提取有意义的特征是对制成件采用新的非破坏性质量检查方法的基础，这种方法已经并且一直在工业中引起越来越多的兴趣。我们介绍ConvLBM，这是一种实时监视基于激光的制造过程的新颖方法。ConvLBM使用卷积神经网络模型从原始的中波长红外同轴图像中提取特征和质量指标。我们演示了ConvLBM代表过程动态的能力，并在两种情况下预测质量指标：激光金属沉积中的稀释估算和激光焊接过程中的缺陷位置。获得的结果代表了大型金属零件的3D打印以及焊接工艺质量控制方面的突破。