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EP-4740001-A1 - SYSTEM FOR DETERMINING A QUALITY OF A COATING SURFACE

EP4740001A1EP 4740001 A1EP4740001 A1EP 4740001A1EP-4740001-A1

Abstract

A system (100) for determining a coating surface quality is presented, comprising a height data providing unit (101) configured to provide height data indicative of heights of the coating surface relative to a substrate, and a defect detecting unit (102) configured to detect defects in the surface by a) determining, for any candidate defect position, height difference data, the height difference data for a candidate defect position indicating differences between the height at the candidate defect position and heights at a plurality of reference positions, and b) comparing the height difference data to predetermined reference height difference data indicating a defect. The system further comprises a quality determining unit (103) configured to determine the quality of the surface based on the detected defects. This allows for improved coating quality assessments, based on efficiently and yet accurately detected defects.

Inventors

  • SHAFEI, BEHRANG
  • GIGLBERGER, Kai
  • HAMERS, CHRISTOPH

Assignees

  • BASF SE

Dates

Publication Date
20260513
Application Date
20240708

Claims (15)

  1. 1 . A system (100) for determining a quality of a surface of a coating applied on a substrate, wherein the system comprises: a height data providing unit (101) configured to provide height data, wherein the height data are indicative of heights of the surface of the coating relative to the substrate, a defect detecting unit (102) configured to detect defects in the surface of the coating by a) determining, for any candidate defect position of the surface of the coating, height difference data based on the height data, wherein the height difference data for a candidate defect position are indicative of differences between the height at the candidate defect position and heights at a plurality of reference positions, the reference positions being determined based on the candidate defect position according to a predefined relation, and b) comparing the height difference data to predetermined reference height difference data indicating a defect, and a quality determining unit (103) configured to determine the quality of the surface based on the detected defects.
  2. 2. The system as defined in claim 1 , wherein the defect detecting unit (102) is configured to detect each defect individually.
  3. 3. The system as defined in any of the preceding claims, wherein the height difference data correspond to a local binary patterning of the height data.
  4. 4. The system as defined in any of the preceding claims, further comprising a classifying unit configured to classify the detected defects into one of a plurality of defect classes, wherein the quality determining unit (103) is configured to determine the quality of the surface based on the classified detected defects.
  5. 5. The system as defined in claim 4, wherein the defect classes are indicative of any of the following: cracks, craters, particles, flakes, bubbles.
  6. 6. The system as defined in any of claims 4 and 5, wherein the classifying unit is configured to classify the detected defects based on heights indicated by the height data for the detected defect.
  7. 7. The system as defined in any of claims 4 to 6, further comprising a rendered image providing unit configured to provide a rendered image corresponding to a view onto the surface of the coating, wherein the classifying unit is configured to classify the detected defects based on rendered image data corresponding to the detected defect.
  8. 8. The system as defined in any of claims 4 to 7, wherein the classifying unit is configured to classify the detected defects using a trained machine learning model.
  9. 9. The system as defined in claim 8, wherein the machine learning model comprises a convolutional neural network.
  10. 10. The system as defined in claim 9, wherein the convolutional neural network comprises a plurality of convolutional layers for providing respective feature maps.
  11. 1 1. The system as defined in claim 10, wherein the convolutional neural network comprises at least one pooling layer for pooling a respective one of the feature maps into a corresponding pooled feature map.
  12. 12. The system as defined in claim 11 , wherein the convolutional neural network applies spatial pyramid pooling.
  13. 13. The system as defined in any of claims 8 to 12, wherein the machine learning model comprises a transformer architecture.
  14. 14. A method (200) for determining a quality of a surface of a coating applied on a substrate, wherein the method includes: providing (201) height data, wherein the height data are indicative of heights of the surface of the coating relative to the substrate, detecting (202) defects in the surface of the coating by a) determining, for any candidate defect position of the surface of the coating, height difference data based on the height data, wherein the height difference data for a candidate defect position are indicative of differences between the height at the candidate defect position and heights at a plurality of reference positions, the reference positions being determined based on the candidate defect position according to a predefined relation, and b) comparing the height difference data to predetermined reference height difference data indicating a defect, and determining (203) the quality of the surface based on the detected defects.
  15. 15. A computer program for determining a quality of a surface of a coating applied on a substrate, wherein the computer program comprises instructions causing the system as defined in claim 1 to execute the method as defined in claim 14.

Description

System for determining a quality of a coating surface FIELD OF THE INVENTION The invention relates to a system, a method and a computer program for determining a quality of a surface of a coating applied on a substrate. BACKGROUND OF THE INVENTION The article “Rapid surface defects detection in wire and arc additive manufacturing based on laser profilometer” by C. Huang et al., Measurement, volume 189 (2022), relates to a laser inspection system for monitoring surface defects arising while manufacturing metal components by use of wire arc additive manufacturing, wherein, upon detection of surface defects, appropriate repair actions are to be taken during the manufacturing in order to avoid that surface defects are covered and thereby internal defects are generated in the manufactured metal component. The article “Online Convolutional Neural Network-based anomaly detection and quality control for Fused Filament Fabrication process” by J. Lyu et al., Virtual and Physical Prototyping, volume 16 (2021), refers to detecting anomalies in laser scan data of a respective layer generated in a fused filament fabrication process for manufacturing a metal component, wherein a thickness of a respective subsequent layer is adjusted based on the detected anomalies. The article “A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface” by L. Zhao et al., Sensors, volume 20 (2020), relates to inspection strategies for evaluating surfaces in continuous casting production lines for manufacturing metal products. Quality assessments for processes in which substrates like automobile parts, for instance, are being coated by paints or other coatings require suitable quality measures. Among the features that can be used for such quality measures are a number and type of defects in the coating surface. However, detecting and classifying defects in coating surfaces is prone to errors, particularly when carried out manually. On the other hand, already implementing just parts of the detection and classification on a computer can quickly require relatively large computational resources in order to arrive at sufficiently accurate results. There is therefore a need for improved quality assessments of coatings applied on substrates. SUMMARY OF THE INVENTION It is an object of the invention to allow for an improved quality assessment of coatings applied on substrates. In a first aspect, the invention relates to a system for determining a quality of a surface of a coating applied on a substrate. The system comprises i) a height data providing unit configured to provide height data, wherein the height data are indicative of heights of the surface of the coating relative to the substrate, and ii) a defect detecting unit. The defect detecting unit is configured to detect defects in the surface of the coating by a) determining, for any candidate defect position of the surface of the coating, height difference data based on the height data, wherein the height difference data for a candidate defect position are indicative of differences between the height at the candidate defect position and heights at a plurality of reference positions, the reference positions being determined based on the candidate defect position according to a predefined relation, and b) comparing the height difference data to predetermined reference height difference data indicating a defect. The system further comprises iii) a quality determining unit configured to determine the quality of the surface based on the detected defects. Since the quality of the surface of the coating applied on the substrate is determined based on defects detected in the surface of the coating by determining, for any candidate defect position of the surface of the coating, height difference data based on height data indicative of heights of the surface of the coating relative to the substrate, wherein the height difference data for a candidate defect position are indicative of differences between the height at the candidate defect position and heights at a plurality of reference positions, the reference positions being determined based on the candidate defect position according to a predefined relation, the quality of the surface can be determined based on efficiently and yet accurately detected defects. In this way, an improved quality assessment of coatings applied on substrates can be achieved. The height data could be regarded as a three-dimensional set of data in which two dimensions are indicative of a position on, or of, the surface of the coating, and the third dimension is indicative of the height of the surface of the coating relative to the substrate at the respective position. The height data providing unit may be configured to provide the height data based on a three-dimensional scan of the surface. The scan may be carried out using a chromatic confocal measurement system such as, for instance, the FocalSpec by LMI Technologies Inc. The height data ca