Cai et al., 2015 - Google Patents
Measurement and characterization of porosity in aluminium selective laser melting parts using X-ray CTCai et al., 2015
View PDF- Document ID
- 18157928190449796784
- Author
- Cai X
- Malcolm A
- Wong B
- Fan Z
- Publication year
- Publication venue
- Virtual and Physical Prototyping
External Links
Snippet
Selective laser melting (SLM) is an additive manufacturing technique which has the capability to produce complex metal parts with almost 100% density and good mechanical properties. Despite the potential benefits of SLM technology, there are technical challenges …
- 238000002844 melting 0 title abstract description 33
Classifications
-
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by transmitting the radiation through the material
- G01N23/04—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by transmitting the radiation through the material and forming a picture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/40—Imaging
- G01N2223/419—Imaging computed tomograph
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons
- G01N23/22—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by measuring secondary emission
- G01N23/225—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by measuring secondary emission using electron or ion microprobe or incident electron or ion beam
- G01N23/2251—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by measuring secondary emission using electron or ion microprobe or incident electron or ion beam with incident electron beam
- G01N23/2252—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by measuring secondary emission using electron or ion microprobe or incident electron or ion beam with incident electron beam and measuring excited X-rays
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cai et al. | Measurement and characterization of porosity in aluminium selective laser melting parts using X-ray CT | |
| Kim et al. | Investigation of pore structure in cobalt chrome additively manufactured parts using X-ray computed tomography and three-dimensional image analysis | |
| Hu et al. | Nanoporous gold: 3D structural analyses of representative volumes and their implications on scaling relations of mechanical behaviour | |
| Villanova et al. | 3D phase mapping of solid oxide fuel cell YSZ/Ni cermet at the nanoscale by holographic X-ray nanotomography | |
| Xiao et al. | Detection of powder bed defects in selective laser sintering using convolutional neural network | |
| Laquai et al. | X-ray refraction distinguishes unprocessed powder from empty pores in selective laser melting Ti-6Al-4V | |
| Senck et al. | Additive manufacturing and non-destructive testing of topology-optimised aluminium components | |
| Furat et al. | Description of ore particles from X-ray microtomography (XMT) images, supported by scanning electron microscope (SEM)-based image analysis | |
| Kim et al. | The influence of X-Ray computed tomography acquisition parameters on image quality and probability of detection of additive manufacturing defects | |
| Salzer et al. | Quantitative comparison of segmentation algorithms for FIB‐SEM images of porous media | |
| Ziabari et al. | Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction | |
| du Plessis et al. | X-ray computed tomography of a titanium aerospace investment casting | |
| Zikmund et al. | Computed tomography based procedure for reproducible porosity measurement of additive manufactured samples | |
| García-Moreno et al. | Image-based porosity classification in Al-alloys by laser metal deposition using random forests | |
| Li et al. | AM-SegNet for additive manufacturing in situ X-ray image segmentation and feature quantification | |
| Sundar et al. | Flaw identification in additively manufactured parts using X-ray computed tomography and destructive serial sectioning | |
| Dang et al. | Multi‐step radiographic image enhancement conforming to weld defect segmentation | |
| Ohgaki et al. | In situ observations of compressive behaviour of aluminium foams by local tomography using high-resolution X-rays | |
| Lifton et al. | Internal surface roughness measurement of metal additively manufactured samples via x-ray CT: the influence of surrounding material thickness | |
| Hirabayashi et al. | Deep learning for three-dimensional segmentation of electron microscopy images of complex ceramic materials | |
| Liu et al. | HAADF‐STEM characterization and simulation of nanoparticle distributions in an inhomogeneous matrix | |
| Leszczyński et al. | Global and local thresholding methods applied to X-ray microtomographic analysis of metallic foams | |
| Mutiargo et al. | Defect detection using trainable segmentation | |
| Ertay et al. | Toward sub-surface pore prediction capabilities for laser powder bed fusion using data science | |
| Gontard et al. | Three-dimensional chemical mapping using non-destructive SEM and photogrammetry |