Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory

Purpose This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development. Design/methodology/approach Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied. Findings The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%. Originality/value An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

[1]  K. Wegener,et al.  Mutual comparison of acoustic, pyrometric and thermographic laser powder bed fusion monitoring , 2021 .

[2]  Yan Lu,et al.  Deep representation learning for process variation management in laser powder bed fusion , 2021, Additive Manufacturing.

[3]  Prahalada K. Rao,et al.  Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion – A single-track study , 2020 .

[4]  Gisela Lanza,et al.  Classification of specimen density in Laser Powder Bed Fusion (L-PBF) using in-process structure-borne acoustic process emissions , 2020, Additive Manufacturing.

[5]  Aihua Zhu,et al.  Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory , 2019, Sensors.

[6]  Alaa Elwany,et al.  Layerwise Anomaly Detection in Laser Powder-Bed Fusion Metal Additive Manufacturing , 2019, Journal of Manufacturing Science and Engineering.

[7]  Geok Soon Hong,et al.  In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches , 2019, Additive Manufacturing.

[8]  S. Coeck,et al.  Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data , 2019, Additive Manufacturing.

[9]  B. Colosimo,et al.  Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion , 2018, Additive Manufacturing.

[10]  Jack Beuth,et al.  A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process , 2018, Additive Manufacturing.

[11]  Yong Deng,et al.  Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory , 2018, Advances in Mechanical Engineering.

[12]  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.

[13]  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.

[14]  Shashi Phoha,et al.  Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. , 2018 .

[15]  Edward William Reutzel,et al.  Multi-sensor investigations of optical emissions and their relations to directed energy deposition processes and quality , 2018 .

[16]  Fuyuan Xiao,et al.  A Weighted Combination Method for Conflicting Evidence in Multi-Sensor Data Fusion , 2018, Sensors.

[17]  Eric J. Faierson,et al.  In-situ acoustic signature monitoring in additive manufacturing processes , 2018 .

[18]  Sheng Liu,et al.  In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods , 2018, Sensors.

[19]  Geok Soon Hong,et al.  Defect detection in selective laser melting technology by acoustic signals with deep belief networks , 2018, The International Journal of Advanced Manufacturing Technology.

[20]  Xiaoyan Su,et al.  Research on fault diagnosis methods for the reactor coolant system of nuclear power plant based on D-S evidence theory , 2018 .

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

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

[23]  Reza Malekian,et al.  Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review , 2018 .

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

[25]  Stanislaw Osowski,et al.  Transfer learning in recognition of drill wear using convolutional neural network , 2017, 2017 18th International Conference on Computational Problems of Electrical Engineering (CPEE).

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

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

[28]  Lixiang Duan,et al.  Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing , 2017 .

[29]  A. Nassar,et al.  Flaw detection in powder bed fusion using optical imaging , 2017 .

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

[31]  Brandon M. Lane,et al.  Multiple sensor detection of process phenomena in laser powder bed fusion , 2016, SPIE Commercial + Scientific Sensing and Imaging.

[32]  Daryl J. Dagel,et al.  Four-color imaging pyrometer for mapping temperatures of laser-based metal processes , 2016, SPIE Commercial + Scientific Sensing and Imaging.

[33]  James M. Fraser,et al.  In situ morphology-based defect detection of selective laser melting through inline coherent imaging , 2016 .

[34]  Edward William Reutzel,et al.  Sensing for directed energy deposition and powder bed fusion additive manufacturing at Penn State University , 2016, SPIE LASE.

[35]  Satish T. S. Bukkapatnam,et al.  Heterogeneous Sensor Data Fusion Approach for Real-time Monitoring in Ultraprecision Machining (UPM) Process Using Non-Parametric Bayesian Clustering and Evidence Theory , 2016, IEEE Transactions on Automation Science and Engineering.

[36]  Wen Jiang,et al.  An evidential sensor fusion method in fault diagnosis , 2016 .

[37]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[38]  Chen Jing,et al.  SVM and PCA based fault classification approaches for complicated industrial process , 2015, Neurocomputing.

[39]  Shawn Moylan,et al.  Thermographic Measurements of the Commercial Laser Powder Bed Fusion Process at NIST. , 2015, Rapid prototyping journal.

[40]  Pan Michaleris,et al.  3D spatial reconstruction of thermal characteristics in directed energy deposition through optical thermal imaging , 2015 .

[41]  Maurice Kettner,et al.  Spark plug fault recognition based on sensor fusion and classifier combination using Dempster–Shafer evidence theory , 2015 .

[42]  Jean-Pierre Kruth,et al.  In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system , 2014 .

[43]  Liqun Hou,et al.  Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis , 2012, IEEE Transactions on Instrumentation and Measurement.

[44]  Shun-Peng Zhu,et al.  Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty , 2011 .

[45]  Thierry Denoeux,et al.  Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion , 2010, Eng. Appl. Artif. Intell..

[46]  Xiaohong Yuan,et al.  Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory , 2007, Inf. Fusion.

[47]  Bo-Suk Yang,et al.  Application of Dempster–Shafer theory in fault diagnosis of induction motors using vibration and current signals , 2006 .

[48]  Catherine K. Murphy.  Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[49]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[50]  Jack Beuth,et al.  Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process , 2019, Additive Manufacturing.

[51]  Bianca Maria Colosimo,et al.  Data fusion methods for statistical process monitoring and quality characterization in metal additive manufacturing , 2018 .

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

[53]  Mohd Salman Leong,et al.  Dempster-Shafer evidence theory for multi-bearing faults diagnosis , 2017, Eng. Appl. Artif. Intell..

[54]  Bernd Scholz-Reiter,et al.  Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection , 2016 .

[55]  Akira Hosokawa,et al.  Investigation of laser consolidation process for metal powder by two-color pyrometer and high-speed video camera , 2013 .

[56]  Jan Bültmann,et al.  Design of an Optical system for the In Situ Process Monitoring of Selective Laser Melting (SLM) , 2011 .

[57]  John C. Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .