Lithium battery surface defects

(PDF) A novel approach for surface defect detection of lithium battery

In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering...

Surface Defects Detection and Identification of Lithium Battery

The experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method.

NUMBERS OF MAIN TYPES OF SURFACE DEFECTS OF LITHIUM BATTERY

A method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in literature [11], which can effectively detect

Machine vision-based detection of surface defects in cylindrical

Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface

Surface Defects Detection and Identification of Lithium Battery

Abstract: In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the

An Automatic Defects Detection Scheme for Lithium-ion Battery

This paper presents an automatic flaw inspection scheme for online real-time detection of the defects on the surface of lithium-ion battery electrode (LIBE) in actual industrial production. Firstly, based on the conventional methods of region extraction, ROI (region of LIBE) could be extracted from the captured LIBE original image. Secondly, in order to reduce the

Defects in Lithium-Ion Batteries: From Origins to Safety Risks

This paper addresses the safety risks posed by manufacturing defects in lithium-ion batteries, analyzes their classification and associated hazards, and reviews the research on metal foreign matter defects, with a focus on copper particle contamination. Furthermore, we summarize the detection methods to identify defective batteries and propose

Coating Defects of Lithium-Ion Battery Electrodes and Their

In order to reduce the cost of lithium-ion batteries, production scrap has to be minimized. The reliable detection of electrode defects allows for a quality control and fast operator reaction in ideal closed control loops and a well-founded decision regarding whether a piece of electrode is scrap. A widely used inline system for defect detection is an optical detection

3D Point Cloud-Based Lithium Battery Surface Defects

A 3D visual measurement system is a promising solution for detecting surface defects based on their roughness and height. This paper proposes an integrated approach to address the problem of lithium battery surface defect

(PDF) A novel approach for surface defect detection of

In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering...

3D Point Cloud-Based Lithium Battery Surface Defects

A 3D visual measurement system is a promising solution for detecting surface defects based on their roughness and height. This paper proposes an integrated approach to

Surface defect detection of cylindrical lithium-ion battery by

In the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery

An Automatic Defects Detection Scheme for Lithium-ion Battery

Surface Defects Detection and Identification of Lithium Battery

The experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average

A YOLOv8-Based Approach for Real-Time Lithium-Ion

Targeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved and optimized battery

Deep-Learning-Based Lithium Battery Defect Detection via Cross

This research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. With a scarcity of specific defect data, we introduce an innovative Cross-Domain Generalization (CDG) approach, incorporating Cross-domain Augmentation, Multi-task

(PDF) Deep-Learning-Based Lithium Battery Defect

for lithium battery surface defect detection. This method uses. a voxel density strategy for accelerating point cloud ﬁltering. and distinguishes defect features through clustering segmen-tation

Few-shot learning approach for 3D defect detection in lithium battery

Detecting the surface defects in a lithium battery with an aluminium/steel shell is a difficult task. The effect of reflectivity, the limitation of acquiring the 3D information, and the shortage

Sim-YOLOv5s: A method for detecting defects on the end face of lithium

In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells. In this model, we propose a fast spatial pooling pyramid structure, SimSPPF, to speed up the model and embed the attention mechanism convolutional block attention module in the backbone.

Minimal Defect Detection on End Face of Lithium Battery Shells

To solve the lack of surface defect images of lithium battery shells and provide experimental evidence for the effectiveness of YOLO-MDD proposed in this paper, a dataset

Sim-YOLOv5s: A method for detecting defects on the end face of

In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells. In this model, we propose a fast spatial pooling

Impact of Electrode Defects on Battery Cell

It is found that, although the impact of some defects is quite well understood, others almost completely lack an evaluation of their criticality. We finally make suggestions for further studies paving the way to deduce

Defects in Lithium-Ion Batteries: From Origins to Safety Risks

Machine vision-based detection of surface defects in cylindrical

Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image processing and deep learning to address this problem. First, the mechanism of surface

Surface defect detection of cylindrical lithium-ion battery by

In the proposed Lithium-ion battery Surface Defect Detection (LSDD) system, an augmented dataset of multi-scale patch samples generated from a small number of lithium-ion battery images is used in the learning process of a two-stage classification scheme that aims to differentiate defect image patches of lithium-ion batteries in the first stage

A novel approach for surface defect detection of lithium battery

In order to accurately identify the surface defects of lithium battery, a novel defect detection approach is proposed based on improved K-nearest neighbor (KNN) and Euclidean clustering segmentation. Firstly, an improved voxel density strategy for KNN is proposed to speed up the effect for point filtering. Then, the improved clustering

Deep-Learning-Based Lithium Battery Defect Detection via Cross

Lithium battery surface defects [PDF]

6 FAQs about [Lithium battery surface defects]

How to identify surface defects of lithium battery?

Can surface defect detection system improve the production quality of lithium battery?

The application results show that the surface defect detection system of lithium battery can accurately construct the three-dimensional model of lithium battery surface and identify the defects on the model, improving the production quality and efficiency of lithium battery.

Can computer terminals detect surface defects during lithium battery industrial production?

Shown in Fig. 14 is the use of computer terminals to control equipment and adjust parameters for defect detection during lithium battery industrial production. Based on the method presented in this paper, the system is used to detect the surface defects of lithium battery and display them in real time.

Do lithium battery shells have defects?

The presence of pits, R-angle injuries, hard printing, and other defects on the end face of lithium battery shells severely affects the production safety and usage safety of lithium battery products. In this study, we propose an effective defect-detection model, called Sim-YOLOv5s, for lithium battery steel shells.

How many defects are detected in a lithium battery?

According to his research result, the average missed detection rate of six defects was 6.21% and the average false detection rate was 3.91%. Their research focused on the detection of surface defects on cylindrical lithium batteries but not on the end faces.

Why is detecting lithium battery shell defects important?

The detection of lithium battery shell defects is an important aspect of lithium battery production. The presence of pits, R-angle injuries, hard printing, and other defects on the end face of lithium battery shells severely affects the production safety and usage safety of lithium battery products.

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