An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes from heterogeneous sensor data

Bastani, Kaveh; Rao, Prahalada; Kong, Zhenyu

doi:10.1080/0740817x.2015.1122254

Cited by 48 publications

(13 citation statements)

References 23 publications

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“…This section thus demonstrates that the spectral graph invariants are preferable over conventional statistical features. These results are in line with the authors' recent findings that sparse representation is relatively computationally efficient with accuracy comparable to popular classifiers, such as SVM and NN [37].…”

Section: Application Andsupporting

confidence: 91%

“…In this section, the sparse representation-based classification approach is briefly summarized as it is found to be the classifier of choice later in Sec. 5.6 [37]. A much more detailed explanation is available in the authors' recent work [37].…”

Section: 1mentioning

confidence: 97%

“…5.6 [37]. A much more detailed explanation is available in the authors' recent work [37]. Considering the system of independent linear equations…”

Section: 1mentioning

confidence: 99%

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Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches

Tootooni

Dsouza

Donovan

et al. 2017

Journal of Manufacturing Science and Engineering

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110

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The objective of this work is to develop and apply a spectral graph theoretic approach for differentiating between (classifying) additive manufactured (AM) parts contingent on the severity of their dimensional variation from laser-scanned coordinate measurements (3D point cloud). The novelty of the approach is in invoking spectral graph Laplacian eigenvalues as an extracted feature from the laser-scanned 3D point cloud data in conjunction with various machine learning techniques. The outcome is a new method that classifies the dimensional variation of an AM part by sampling less than 5% of the 2 million 3D point cloud data acquired (per part). This is a practically important result, because it reduces the measurement burden for postprocess quality assurance in AM—parts can be laser-scanned and their dimensional variation quickly assessed on the shop floor. To realize the research objective, the procedure is as follows. Test parts are made using the fused filament fabrication (FFF) polymer AM process. The FFF process conditions are varied per a phased design of experiments plan to produce parts with distinctive dimensional variations. Subsequently, each test part is laser scanned and 3D point cloud data are acquired. To classify the dimensional variation among parts, Laplacian eigenvalues are extracted from the 3D point cloud data and used as features within different machine learning approaches. Six machine learning approaches are juxtaposed: sparse representation, k-nearest neighbors, neural network, naïve Bayes, support vector machine, and decision tree. Of these, the sparse representation technique provides the highest classification accuracy (F-score > 97%).

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Section: Application Andsupporting

confidence: 91%

Section: 1mentioning

confidence: 97%

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Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches

Tootooni

Dsouza

Donovan

et al. 2017

Journal of Manufacturing Science and Engineering

Self Cite

110

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“…Authors. This work builds upon the authors' previous research in spectral graph theory for manufacturing applications [67][68][69][70][71][72]. These previous works are enumerated below:…”

Section: Previous Work In Spectral Graph Theory By Thementioning

confidence: 95%

Sensor-Based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process Using a Spectral Graph Theoretic Approach

Montazeri

Rao

2018

Journal of Manufacturing Science and Engineering

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The goal of this work is to monitor the laser powder bed fusion (LPBF) process using an array of sensors so that a record may be made of those temporal and spatial build locations where there is a high probability of defect formation. In pursuit of this goal, a commercial LPBF machine at the National Institute of Standards and Technology (NIST) was integrated with three types of sensors, namely, a photodetector, high-speed visible camera, and short wave infrared (SWIR) thermal camera with the following objectives: (1) to develop and apply a spectral graph theoretic approach to monitor the LPBF build condition from the data acquired by the three sensors; (2) to compare results from the three different sensors in terms of their statistical fidelity in distinguishing between different build conditions. The first objective will lead to early identification of incipient defects from in-process sensor data. The second objective will ascertain the monitoring fidelity tradeoff involved in replacing an expensive sensor, such as a thermal camera, with a relatively inexpensive, low resolution sensor, e.g., a photodetector. As a first-step toward detection of defects and process irregularities that occur in practical LPBF scenarios, this work focuses on capturing and differentiating the distinctive thermal signatures that manifest in parts with overhang features. Overhang features can significantly decrease the ability of laser heat to diffuse from the heat source. This constrained heat flux may lead to issues such as poor surface finish, distortion, and microstructure inhomogeneity. In this work, experimental sensor data are acquired during LPBF of a simple test part having an overhang angle of 40.5 deg. Extracting and detecting the difference in sensor signatures for such a simple case is the first-step toward in situ defect detection in additive manufacturing (AM). The proposed approach uses the Eigen spectrum of the spectral graph Laplacian matrix as a derived signature from the three different sensors to discriminate the thermal history of overhang features from that of the bulk areas of the part. The statistical accuracy for isolating the thermal patterns belonging to bulk and overhang features in terms of the F-score is as follows: (a) F-score of 95% from the SWIR thermal camera signatures; (b) 83% with the high-speed visible camera; (c) 79% with the photodetector. In comparison, conventional signal analysis techniques—e.g., neural networks, support vector machines, linear discriminant analysis were evaluated with F-score in the range of 40–60%.

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“…SIB events are relatively rare compared to non-SIB events, which leads to a skewed distribution as found in prior work with ASD-related behaviors 7,39,79 . SIBs here lasted for about two seconds at minimum, though more subtle movements, such as picking, lasted longer, which lasted ~ 10 to 90 s. SIB and non-SIB data were balanced as in other work to address skewness 28,39,[79][80][81] . Balanced data were used for training, and tenfold cross-validation was used 18,26,30 .…”

Section: Regression Modeling a Multilevel Logistic Regression (Mlr) mentioning

confidence: 99%

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder

Cantin-Garside

Srinivasan

Ranganathan

et al. 2020

Sci Rep

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Self-injurious behavior (SIB) is among the most dangerous concerns in autism spectrum disorder (ASD), often requiring detailed and tedious management methods. Sensor-based behavioral monitoring could address the limitations of these methods, though the complex problem of classifying variable behavior should be addressed first. We aimed to address this need by developing a group-level model accounting for individual variability and potential nonlinear trends in SIB, as a secondary analysis of existing data. Ten participants with ASD and SIB engaged in free play while wearing accelerometers. Movement data were collected from > 200 episodes and 18 different types of SIB. Frequency domain and linear movement variability measures of acceleration signals were extracted to capture differences in behaviors, and metrics of nonlinear movement variability were used to quantify the complexity of SIB. The multi-level logistic regression model, comprising of 12 principal components, explained > 65% of the variance, and classified SIB with > 75% accuracy. Our findings imply that frequency-domain and movement variability metrics can effectively predict SIB. Our modeling approach yielded superior accuracy than commonly used classifiers (~ 75 vs. ~ 64% accuracy) and had superior performance compared to prior reports (~ 75 vs. ~ 69% accuracy) This work provides an approach to generating an accurate and interpretable group-level model for SIB identification, and further supports the feasibility of developing a real-time SIB monitoring system.

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An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes from heterogeneous sensor data

Cited by 48 publications

References 23 publications

Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches

Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches

Sensor-Based Build Condition Monitoring in Laser Powder Bed Fusion Additive Manufacturing Process Using a Spectral Graph Theoretic Approach

Multi-level modeling with nonlinear movement metrics to classify self-injurious behaviors in autism spectrum disorder

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