Classifications

• • G—PHYSICS
  • G06—COMPUTING OR 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
  • G06—COMPUTING OR 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 OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/12—Edge-based segmentation
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/136—Segmentation; Edge detection involving thresholding
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/40—Analysis of texture
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2200/00—Indexing scheme for image data processing or generation, in general
  • G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2200/00—Indexing scheme for image data processing or generation, in general
  • G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
• • G—PHYSICS
  • G06—COMPUTING OR 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
  • G06T2207/10004—Still image; Photographic image
• • G—PHYSICS
  • G06—COMPUTING OR 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
  • G06T2207/10056—Microscopic image
• • G—PHYSICS
  • G06—COMPUTING OR 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
  • G06T2207/10072—Tomographic images
  • G06T2207/10081—Computed x-ray tomography [CT]
• • G—PHYSICS
  • G06—COMPUTING OR 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
  • G06T2207/10072—Tomographic images
  • G06T2207/10088—Magnetic resonance imaging [MRI]
• • G—PHYSICS
  • G06—COMPUTING OR 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
  • G06T2207/10132—Ultrasound image
• • G—PHYSICS
  • G06—COMPUTING OR 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
  • G06T2207/20152—Watershed segmentation
• • G—PHYSICS
  • G06—COMPUTING OR 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

Definitions

• WO 2015 / 080912 A1 discloses a method of digitally modelling a reservoir in oil fields from computer tomography images on core samples. By running simulations on these models, oil field characteristics may be obtained. However, a lot of specific assumptions and physics have to go into the models, so they are hardly applicable for anything else than oil field analysis.
• WO 2018 / 206225 A1 discloses a method of modelling an object from images and compare it to its desired geometry in order to detect defects. However, no material properties are obtained.
• US 2014 / 044 315 A1 discloses a method for increasing the accuracy of a target property value derived from a rock sample. However, this method can hardly be transferred to foam samples.
• Samuel Pardo Alonso discloses in his PhD thesis with the title “X-Ray Imaging Applied to the Characterization of Polymer Foams' Cellular Structure and Its Evolution” from March 1, 2014 a method to generate a 3D model from images. However, no material properties are obtained.
• the object of the present invention to provide a method which can determine material properties with little effort in terms of apparatuses, personnel and time.
• the method should be variable to a wide range of different foam materials and size scales.
• the method was aimed to be fast and reliable.
• the present invention further relates to a non-transitory computer readable data medium storing a computer program including instructions for executing steps of the method according to any of the preceding claims.
• the present invention further relates to a production monitoring and/or control system for moni toring and/or controlling material properties of a sample comprising
• a processing unit configured to providing the at least one structural feature to a material model material model suitable for obtaining at least one material property from the structural feature
• Figure 1 depicts a possible implementation of the invention.
• Figure 2a to 2g show an example of the image processing using the method of the invention.
• the number can range from 5 to 1000, for example 10 to 50 or 100 to 400.
• the resolution of the images should be high enough such that the features can be clearly recog nized, but not too high so the computing time is does not become too long.
• Typical resolutions of images are between 10 x 10 to 1024 x 1024 pixels, wherein the images do not have to be quadratic, so 768 x 1024 or 512 x 288 pixels can be used equally well.
• the images are in gray-scale or are converted into gray scale.
• the images are preprocessed in order to facilitate detection of phase boundaries.
• Preprocessing can include adjusting bright ness, contrast, noise removal, applying thresholds or combinations thereof. Even more prefera bly, the preprocessing parameters yielding best results in the method according to the present invention are saved and automatically proposed to a user or directly applied to further images to be preprocessed.
• Generation of a representation from images can be achieved in various ways. Most of them involve edge or surface detection or segmentation determining the respective phase bounda ries.
• the edge detection converts 3D voxel data into 3D surface data, for example by assigning a threshold gray value to edge voxels, interpolation between voxel gray values, search for max imum gray value derivatives, mid gray value between light air voxel and dark material voxel lev els, or local adaptive gray threshold. Reducing noise and artifacts as well as interpolation is subject to many publications known to the skilled person.
• the representation is generated from images by segmenting the gray-scale image by applying a threshold algorithm, thereby converting the gray-scale into an image in which each color represents one phase, i.e. a certain material or a void.
• a threshold algorithm thereby converting the gray-scale into an image in which each color represents one phase, i.e. a certain material or a void.
• the materi al could be white and the void black.
• the first material could be white, the second material gray and the third material black.
• the segmented image may already be sufficient for the representation. However, to reliably ex tract structural features from the representation, it is often useful to apply further methods.
• the segmented image is subject to a distance function which assigns each pixel or voxel the distance to the nearest pixel or voxel with a different color.
• a watershed algorithm is applied to identify objects such as pores, embedded particles, walls, struts, or nodes. When overflooding the watershed algorithm, it is also possible to determine the center of walls, struts and nodes.
• the at least one structural feature comprises walls, struts, or nodes. If only one structural feature is extracted, it has to either of walls, struts, or nodes. Typi cally, more than one structural feature is extracted.
• at least one of the structural features is walls, struts, or nodes and the others can be one or more of the remaining of walls, struts, or nodes or other structural features as described above.
• the structural fea tures comprise at least two of walls, struts, or nodes, in particular the structural features com prise all, i.e. walls, struts, and nodes.
• the method according to the present invention comprises (d) outputting the at least one materi al property received from the material model.
• Outputting can mean writing the material property on a non-transitory data storage medium, display it on a user interface or transmit it to another program either locally or on a remote system, preferably the at least one material property is output onto a user interface.
• FIG. 1 An example how the invention can be implemented is depicted in figure 1.
• Samples may be produced in a factory 10. These are subjected to a microscopy device 11 which generates im ages of the sample. These images are converted to a representation by a processing unit 12.
• the representation is provided to processing unit 13 which extracts at least one structural fea ture from the representation.
• the at least one structural feature is provided to processing unit 14 which provides it to a model.
• This model has been trained by historical data obtained from a data storage device 15.
• the model obtains material properties which are provided to output de vice 16.
• This output device 16 may output the material property to the factory 10, for example to adjust the production parameters.
• the present invention further relates to a non-transitory computer readable data medium storing a computer program including instructions for executing steps of the method according to the present invention.
• Computer readable data medium include hard drives, for example on a serv er, USB storage device, CD, DVD or Blue-ray discs.
• the computer program may contain all functionalities and data required for execution of the method according to the present invention or it may provide interfaces to have parts of the method processed on remote systems, for ex ample on a cloud system.
• the present invention further relates to a production monitoring and/or control system for moni toring and/or controlling material properties of a foam sample.
• the system can be a computing device, for example a computer, tablet, or smartphone. Often the computing device has a network connection in order to communicate with other computing devices, such as servers or a cloud network.
• Production may refer to mass production in a factory or to production of several samples in the context of a research program.
• Monitoring is typically done in the context of quality management in order to ensure that a product is constantly within a set range of given material properties or to classify the products based on different specification, for example a high-quality product and an average- quality product.
• Controlling may refer to a process of picking the best samples in order facilitate and speed up a research and development process.
• the system comprises (a) an input unit configured to receive images showing the inner structure of the sample.
• the input unit comprises a user interface which allows the user to select images to be processed, for example from a local or remote storage medium or directly from a measurement apparatus analyzing the sample.
• the input unit is configured to receive the type of material for each phase in the sample.
• the input unit may be implemented as a webservice or a standalone software package.
• the input unit may form the presentation or application layer.
• the input unit comprises a user interface.
• the system comprises (b) a processing unit configured to extract at least one structural feature from the representation.
• the processing unit may be a local processing unit comprising a central processing unit (CPU) and/or a graphics processing units (GPU) and/or an application specific integrated circuit (ASIC) and/or a tensor processing unit (TPU) and/or a field-programmable gate array (FPGA).
• the processing unit may also be an interface to a remote computer system such as a cloud service.
• the system comprises (c) a processing unit configured to providing the at least one structural feature to a material model material model suitable for ob taining at least one material property from the structural feature.
• the processing unit can be the same as in (b) or a different one, for example the processing unit in (b) can be on the local ma chine while the processing unit in (c) is an interface to a cloud service.
• the system comprises (d) an output unit configured to output a material property received from the material model.
• the output unit may be implemented as a webservice or a standalone software package.
• the output unit may form the presentation or application layer.
• the output unit is a user interface which is configured to display the material property of the sample. The user may then take the necessary action, for example ad just production parameters if the sample is out of specification or pick samples with the highest quality in a research project.
• the output unit may include or have an interface to an apparatus which automatically adjusts production parameters or sorts the samples depending on their material properties.
• Figures 2a to 2g illustrate an example of how the steps (a) and (b) can be realized.
• Figure 2a shows the raw data as it is obtained for example from an X-ray tomography apparatus. After applying filters for preparing binarization the image in figure 2b is obtained.
• Figure 2c shows the outcome of applying threshold to binarize the image. For figure 2d a distance filter was applied on both phases, i.e. opposite negative sign in pore phase and positive sign in the material phase. Subsequently, local minima were identified and watershed algorithm with lines between cells and without masking were applied. The outcome is shown in figure 2e.
• Figure 2f shows mask labeled cells obtained therefrom with binarized data from the image in figure 2c to get labeled pores.
• voxels in skeleton can be labeled by their number of adjacent cells, i.e. a voxel with to neighboring cells represents a wall, a voxel with three neighboring cells represents a strut, and a voxel with four or more cells represents a node. Connected voxels are labeled the same type as single wall, strut, or node.

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• Engineering & Computer Science (AREA)
• Theoretical Computer Science (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Quality & Reliability (AREA)
• Computing Systems (AREA)
• Life Sciences & Earth Sciences (AREA)
• Bioinformatics & Cheminformatics (AREA)
• Bioinformatics & Computational Biology (AREA)
• Analysing Materials By The Use Of Radiation (AREA)
• Image Analysis (AREA)
• Investigating Or Analysing Materials By Optical Means (AREA)
• Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
• Management, Administration, Business Operations System, And Electronic Commerce (AREA)

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• 2020
  • 2020-12-01 WO PCT/EP2020/084145 patent/WO2021110690A1/en not_active Ceased
  • 2020-12-01 JP JP2022533597A patent/JP2023505508A/ja active Pending
  • 2020-12-01 MX MX2022006845A patent/MX2022006845A/es unknown
  • 2020-12-01 US US17/781,524 patent/US12482084B2/en active Active
  • 2020-12-01 CN CN202080083546.1A patent/CN114746897B/zh active Active
  • 2020-12-01 BR BR112022010789A patent/BR112022010789A2/pt unknown
  • 2020-12-01 KR KR1020227018852A patent/KR20220106987A/ko active Pending
  • 2020-12-01 EP EP20812122.8A patent/EP4070270A1/en active Pending
• 2025
  • 2025-08-07 JP JP2025132284A patent/JP2025176022A/ja active Pending

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