Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks

In this article, a method of hybrid convolutional neural networks (CNNs) is proposed for powder-bed fusion (PBF) process monitoring. The proposed method can learn both the spatial and temporal representative features from the raw images automatically based on the advantages of the CNN architecture. The results demonstrate the superior performance of the proposed method compared with the traditional methods with handcrafted features. The overall detection accuracy of four process conditions, e.g., overheating, normal, irregularity, and balling, can be up to 0.997. In addition, it is found that the temporal information for PBF process monitoring by the vision detection of the process zone (including melt pool, plume, and spatters) is significant. As the proposed method can save image processing steps, it simplifies the procedure on feature extraction. This makes it more suitable for online monitoring applications.

[1]  Ekachai Phaisangittisagul,et al.  An Analysis of the Regularization Between L2 and Dropout in Single Hidden Layer Neural Network , 2016, 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS).

[2]  Prahalada Rao,et al.  Sensor-Based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process Using a Spectral Graph Theoretic Approach , 2018, Journal of Manufacturing Science and Engineering.

[3]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[4]  Quirico Semeraro,et al.  In-process Monitoring of Selective Laser Melting: Spatial Detection of Defects via Image Data Analysis , 2017 .

[5]  Tuomas Virtanen,et al.  Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[7]  B. Colosimo,et al.  Process defects and in situ monitoring methods in metal powder bed fusion: a review , 2017 .

[8]  Jack Beuth,et al.  Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm , 2018 .

[9]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[10]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Atheel Sabih Shaker.  Recognition of Off-line Printed Arabic Text Using Hidden Markov Models , 2018 .

[12]  S.A.戈德.  Multivariate statistical process control of laser powder bed additive manufacturing , 2017 .

[13]  Hui Yang,et al.  Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing , 2018 .

[14]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[15]  Peng Wang,et al.  An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox , 2017, Sensors.

[16]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[17]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[18]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[19]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  G. Tapia,et al.  A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing , 2014 .

[21]  W. Marsden.  I and J , 2012 .

[22]  S. Shevchik,et al.  Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks , 2017 .

[23]  Min Xia,et al.  Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks , 2018, IEEE/ASME Transactions on Mechatronics.

[24]  Deyong You,et al.  Multisensor Fusion System for Monitoring High-Power Disk Laser Welding Using Support Vector Machine , 2014, IEEE Transactions on Industrial Informatics.

[25]  Bianca Maria Colosimo,et al.  On the use of spatter signature for in-situ monitoring of Laser Powder Bed Fusion , 2017 .

[26]  Xiang Li,et al.  Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Richard Leach,et al.  Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing , 2016 .

[28]  Rik Van de Walle,et al.  Deep Learning for Infrared Thermal Image Based Machine Health Monitoring , 2017, IEEE/ASME Transactions on Mechatronics.

[29]  Bianca Maria Colosimo,et al.  In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume , 2018 .

[30]  Geok Soon Hong,et al.  Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring , 2018, Materials & Design.

[31]  Sung Wook Baik,et al.  Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features , 2018, IEEE Access.