Multi-sensor-based battery electrode scanning for in-process multi-level defect detection and classification

Patent metadata

Field	Value
Publication number	US-2025251378-A1
Application number	US-202418429632-A
Country	US
Kind code	A1
Filing date	Feb 1, 2024
Priority date	Feb 1, 2024
Publication date	Aug 7, 2025
Grant date	—

Abstract

A defect detection system includes: sensors of different types configured to scan battery electrode material and generate output signals including respective image data; a feature extractor module receives the output signals and performs feature extraction to fuse the image data of the output signals to generate feature maps for respective portions of the battery electrode material; a coarse detector, based on the feature maps, determines whether there are defects in the portions of the battery electrode material, and generates binary information for each of the portions indicating whether the portions include one or more defects; and a fine detector, based on the binary information and at least one of the feature maps and image data, classifies the defects. A control module performs one or more operations based on the detected and classified defects.

First claim

What is claimed is:

1 . A defect detection system comprising:
a plurality of sensors of different types configured to scan battery electrode material and generate a plurality of output signals including respective image data; a feature extractor module configured to receive the plurality of output signals and perform feature extraction to fuse the image data of the plurality of output signals to generate feature maps for respective portions of the battery electrode material; a coarse detector configured, based on the feature maps, to determine whether there are defects in the portions of the battery electrode material, and generate binary information for each of the portions indicating whether the portions include one or more defects; a fine detector configured, based on the binary information and at least one of the feature maps and image data, to classify the defects; and a control module configured to at least one of i) report the classified defects, ii) determine whether one or more of the portions of the battery electrode material which have one or more defects ought to be discarded, iii) perform operations to discard the one or more portions of the battery electrode material which have one or more defects, and iv) perform feedback process control operations to prevent future occurrences defect similar to the classified defects.

2 . The defect detection system of claim 1 , wherein the plurality of sensors comprise at least one surface scanning sensor, at least one interior scanning sensor, and at least one interface scanning sensor.

3 . The defect detection system of claim 2 , wherein:
the at least one surface scanning sensor comprises at least one of a camera, a laser 3D profiler, and a flash thermography scanner; the at least one interior scanning sensor comprises at least one of an X-ray scanner, a neutron imaging sensor, an eddy current sensor, and a beta gauging sensor; and the at least one interface scanning sensor comprises at least one of a terahertz scanner and an ultrasound sensor.

4 . The defect detection system of claim 1 , wherein:
the coarse detector is configured to implement a first convolutional neural network and perform deep learning to determine whether there are defects in the portions of the battery electrode material; and the fine detector is configured to implement a second convolutional neural network and perform deep learning to classify the defects.

5 . The defect detection system of claim 1 , wherein the fine detector is configured, when classifying the defects, to determine whether each of the defects is a dark band defect, a contamination defect, a crack, an embrittlement defect, a wrinkle, an edge imperfection, a polka dot, a void, or a delamination.

6 . The defect detection system of claim 1 , wherein the fine detector analyzes portions of the at least one of the feature maps and image data associated with the portions of the battery electrode material that include defects and does not analyze portions of the at least one of the feature maps and image data associated with the portions of the battery electrode material that do not include defects.

7 . The defect detection system of claim 1 , wherein:
the control module is configured, prior to the coarse detector determining whether there are defects in portions of the battery electrode material, to perform a processing procedure on the image data received from the plurality of sensors; the processing procedure comprises at least one of aligning the image data, normalizing the image data, synchronizing the image data, and cropping the image data to generate processed data; and the control module is configured to generate fused data of the feature maps based on the processed data.

8 . The defect detection system of claim 7 , wherein the processing procedure includes aligning the image data, normalizing the image data, synchronizing the image data, and cropping the image data.

9 . The defect detection system of claim 1 , wherein the fine detector is configured to perform i) multi-classification to identify defect types, and ii) localization to locate and scale bounding boxes to defects present in an image.

10 . The defect detection system of claim 1 , wherein the fine detector is configured to analyze portions of the at least one of the feature maps and image data containing at least one defect and to not analyze portions of the at least one of the feature maps and image data not containing a defect.

11 . A defect detection method comprising:
scanning battery electrode material, via a plurality of sensors of different types, and generating a plurality of output signals including respective image data; receiving the plurality of output signals and performing feature extraction to fuse the image data of the plurality of output signals to generate feature maps for respective portions of the battery electrode material; based on the feature maps, determining whether there are defects in the portions of the battery electrode material, and generate binary information for each of the portions indicating whether the portions include one or more defects; based on the binary information and at least one of the feature maps and image data, classifying the defects; and at least one of i) reporting the classified defects, ii) determining whether one or more of the portions of the battery electrode material which have one or more defects ought to be discarded, iii) performing operations to discard the one or more portions of the battery electrode material which have one or more defects, and iv) performing feedback process control operations to prevent future occurrences defect similar to the classified defects.

12 . The defect detection method of claim 11 , wherein the plurality of sensors comprise at least one surface scanning sensor, at least one interior scanning sensor, and at least one interface scanning sensor.

13 . The defect detection method of claim 12 , wherein:
the at least one surface scanning sensor comprises at least one of a camera, a laser 3D profiler, and a flash thermography scanner; the at least one interior scanning sensor comprises at least one of an X-ray scanner, a neutron imaging sensor, an eddy current sensor, and a beta gauging sensor; and the at least one interface scanning sensor comprises at least one of a terahertz scanner and an ultrasound sensor.

14 . The defect detection method of claim 11 , further comprising:
implementing a first convolutional neural network and performing deep learning to determine whether there are defects in the portions of the battery electrode material; and implementing a second convolutional neural network and performing deep learning to classify the defects.

15 . The defect detection method of claim 11 , further comprising, when classifying the defects, determining whether each of the defects is a dark band defect, a contamination defect, a crack, an embrittlement defect, a wrinkle, an edge imperfection, a polka dot, a void, or a delamination.

16 . The defect detection method of claim 11 , further comprising analyzing portions of the at least one of the feature maps and image data associated with the portions of the battery electrode material that include defects and refraining from analyzing portions of the at least one of the feature maps and image data associated with the portions of the battery electrode material that do not include defects.

17 . The defect detection method of claim 11 , further comprising:
prior to the coarse detector determining whether there are defects in portions of th

Assignees

• Gm Global Tech Operations Llc

Inventors

• Tan Changbai
• James Robin
• Cai Wayne
• Manda Raghavendra Saikrishna Keerthi
• Arinez Jorge F

Classifications

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G01D21/02

  Measuring two or more variables by means not covered by a single other subclass · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06V10/774

  Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title