Petrich et al., 2021 - Google Patents
Multi-modal sensor fusion with machine learning for data-driven process monitoring for additive manufacturingPetrich et al., 2021
View PDF- Document ID
- 4063875722984415449
- Author
- Petrich J
- Snow Z
- Corbin D
- Reutzel E
- Publication year
- Publication venue
- Additive Manufacturing
External Links
Snippet
This paper presents a complete concept and validation scheme for potential inter-layer flaw detection from in-situ process monitoring for powder bed fusion additive manufacturing (PBFAM) using supervised machine learning. Specifically, the presented work establishes a …
- 238000000034 method 0 title abstract description 53
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
- G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Petrich et al. | Multi-modal sensor fusion with machine learning for data-driven process monitoring for additive manufacturing | |
Snow et al. | Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning | |
Scime et al. | Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation | |
Montazeri et al. | Sensor-based build condition monitoring in laser powder bed fusion additive manufacturing process using a spectral graph theoretic approach | |
Snow et al. | Correlating in-situ sensor data to defect locations and part quality for additively manufactured parts using machine learning | |
Gobert et al. | Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning | |
US11458542B2 (en) | Systems and methods for powder bed additive manufacturing anomaly detection | |
Xiao et al. | Detection of powder bed defects in selective laser sintering using convolutional neural network | |
Petrich et al. | Machine learning for defect detection for PBFAM using high resolution layerwise imaging coupled with post-build CT scans | |
Kim et al. | Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing | |
WO2023111542A1 (en) | Defect identification in additive manufacturing based on time series in-process parameter data | |
Zhao et al. | Automated anomaly detection of laser-based additive manufacturing using melt pool sparse representation and unsupervised learning | |
Chithra et al. | Severity detection and infection level identification of tuberculosis using deep learning | |
Liu et al. | Multimodal probabilistic modeling of melt pool geometry variations in additive manufacturing | |
Snow et al. | Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning | |
Schwerz et al. | A neural network for identification and classification of systematic internal flaws in laser powder bed fusion | |
Joshi et al. | Applications of supervised machine learning algorithms in additive manufacturing: A review | |
EP3869400A1 (en) | Object identification system and computer-implemented method | |
Rababaah et al. | Asphalt pavement crack classification: a comparison of GA, MLP, and SOM | |
Voigt et al. | Benchmarking a multi-layer approach and neural network architectures for defect detection in PBF-LB/M | |
Yadav et al. | Drift detection in selective laser melting (SLM) using a machine learning approach | |
Khosa et al. | Defect detection in food ingredients using multilayer perceptron neural network | |
Kumar et al. | GLCM and ANN based approach for classification of radiographics weld images | |
Toorandaz et al. | A novel machine learning-based approach for in-situ surface roughness prediction in laser powder-bed fusion | |
Taspinar et al. | Distinguishing between AI images and real images with hybrid image classification methods |