Department of Engineering Science, University West, 461-32 Trollhättan, Sweden.
Physics Department, University of Bari, Via Orabona 4, 70126 Bari, Italy.
Sensors (Basel). 2022 May 20;22(10):3881. doi: 10.3390/s22103881.
Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line.
激光束焊接具有生产效率高、热输入相对较低的特点，是实现高效制造夹层结构的关键技术之一。然而，该工艺对激光束相对于接头的位置非常敏感，即使激光束相对于正确的接头位置有微小的偏差（光束偏移），也会导致所生产部件严重缺陷。对于 T 型接头，由于从顶部无法看到接头，因此传统的焊缝跟踪方法不适用，因为它们依赖于接头的视觉信息。因此，需要有一种监测系统，能够及早发现光束偏移，并停止过程，以避免缺陷和减少废料。本文提出了一种使用光谱仪的监测系统，旨在寻找过程中光谱发射与光束偏移之间的相关性。光谱仪产生高维数据，不清楚这与光束偏移有何关系。因此，建议采用机器学习方法来找到这些相关性。多层感知机神经网络（MLPNN）、支持向量机（SVM）、学习向量量化（LVQ）、逻辑回归（LR）、决策树（DT）和随机森林（RF）被评估为分类器。在将数据输入分类器之前，使用随机森林和非支配排序遗传算法 II（NSGAII）进行特征选择，然后比较分类器的结果。在测试了不同的偏移量后，对于实时检测大于 0.9 毫米的激光束偏离接头中心线的情况，达到了 94%的准确率。
