A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. (December 2022)

• Record Type: Journal Article
• Authors: Li, Jingchang; Zhang, Xiaoge; Zhou, Qi; Chan, Felix T.S.; Hu, Zhen
• Is Part Of: Journal of manufacturing processes. Volume 84(2022)
• Page Start: 913
• Page End: 926
• Publication Date: 2022-12
• Subjects: AE acoustic emission -- AM additive manufacturing -- BPNN back propagation neural network -- CNN convolutional neural networks -- CT computed tomography -- CV computer vision -- DAQ data acquisition -- DL deep learning -- FFT fast Fourier transform -- GRU Gated recurrent unit -- Infrared IR -- LPBF laser powder bed fusion -- LSTM long-short term memory -- ML machine learning -- NN neural networks -- OM optical microscopy -- PCA principal component analysis -- ReLU rectified linear units -- ROI region of interest -- RNN recurrent neural networks -- SLM selective laser melting -- STFT short-time Fourier transform -- SVM support vector machines; Additive manufacturing -- Selective laser melting -- AI-driven production control -- Multi-sensor fusion -- In-situ quality monitoring -- Product quality; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2022.10.050
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: Selective laser melting (SLM) is a commonly used technique in additive manufacturing to produce metal components with complex geometries and high precision. However, the poor process reproducibility and unstable product reliability has hindered its wide adoption in practice. Hence, there is a pressing demand for in-situ quality monitoring and real-time process control. In this paper, a feature-level multi-sensor fusion approach is proposed to combine acoustic emission signals with photodiode signals to realize in-situ quality monitoring for intelligence-driven production of SLM. An off-axial in-situ monitoring system featuring a microphone and a photodiode is developed to capture the process signatures during the building process. According to the 2D porosity and 3D density measurements, the collected acoustic and optical signals are grouped into three categories to indicate the quality of the produced parts. In consideration of the laser scanning information, an approach to transform the 1D signal to 2D image is developed. The converted images are then used to train a convolutional neural network so as to extract and fuse the features derived from the two individual sensors. In comparison with several baseline models, the proposed multi-sensor fusion approach achieves the best performance in quality monitoring. Highlights: The acoustic emission signals and photodiode signals are combined to achieve in-situ quality monitoring. A signal-to-image method is developed to convert the 1D signals to 2D images. A CNN-based feature extraction and multi-sensor fusion approach is proposed to identify the part quality. The proposed approach has the best classification performance in quality monitoring.