Li et al., 2023 - Google Patents
In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser meltingLi et al., 2023
- Document ID
- 254262388968586588
- Author
- Li J
- Zhou Q
- Huang X
- Li M
- Cao L
- Publication year
- Publication venue
- Journal of Intelligent Manufacturing
External Links
Snippet
Selective laser melting is the most commonly used additive manufacturing technique for fabricating metal components. However, the SLMed part quality still largely suffered from the porosity defects that can significantly affect the mechanical properties. Recently, in situ …
- 238000013526 transfer learning 0 title abstract description 57
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting | |
Li et al. | A convolutional neural network-based multi-sensor fusion approach for in-situ quality monitoring of selective laser melting | |
Herzog et al. | Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing | |
Li et al. | Quality analysis in metal additive manufacturing with deep learning | |
Zhang et al. | In-Process monitoring of porosity during laser additive manufacturing process | |
Mi et al. | In-situ monitoring laser based directed energy deposition process with deep convolutional neural network | |
Mahmoud et al. | Applications of machine learning in process monitoring and controls of L-PBF additive manufacturing: A review | |
Xiao et al. | Detection of powder bed defects in selective laser sintering using convolutional neural network | |
Jayasinghe et al. | Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements | |
García-Moreno et al. | Image-based porosity classification in Al-alloys by laser metal deposition using random forests | |
Angelone et al. | Bio-intelligent selective laser melting system based on convolutional neural networks for in-process fault identification | |
Sundar et al. | Flaw identification in additively manufactured parts using X-ray computed tomography and destructive serial sectioning | |
Bevans et al. | Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing | |
Zhang et al. | Intelligent defect detection method for additive manufactured lattice structures based on a modified YOLOv3 model | |
Xing et al. | Recognition and classification of single melt tracks using deep neural network: A fast and effective method to determine process windows in selective laser melting | |
Lee et al. | A defect detection framework using three-dimensional convolutional neural network (3D-CNN) with in-situ monitoring data in laser powder bed fusion process | |
Wang et al. | Gaussian process classification of melt pool motion for laser powder bed fusion process monitoring | |
Tanaka et al. | Automated Vickers hardness measurement using convolutional neural networks | |
Zhang et al. | In situ monitoring plasma arc additive manufacturing process with a fully convolutional network | |
Tang et al. | A new method to assess fiber laser welding quality of stainless steel 304 based on machine vision and hidden Markov models | |
Ertay et al. | Toward sub-surface pore prediction capabilities for laser powder bed fusion using data science | |
Pandiyan et al. | Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework | |
Era et al. | Machine learning in Directed Energy Deposition (DED) additive manufacturing: A state-of-the-art review | |
Zamiela et al. | Deep multi-modal U-Net fusion methodology of thermal and ultrasonic images for porosity detection in additive manufacturing | |
Wang et al. | Traditional machine learning and deep learning for predicting melt-pool cross-sectional morphology of laser powder bed fusion additive manufacturing with thermographic monitoring |