Electrode Plate Transport Anomaly Detection for Collision Prevention

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Solution Overview

Problem

In the secondary battery manufacturing process, defective electrode plates are often abnormally transported, leading to collisions and damage, which reduces productivity and affects product quality.

Innovation Solution

A method and system using machine learning-based unsupervised learning to detect abnormal transport of defective electrode plates by analyzing images from cameras installed along the transport path, identifying abnormal stacking, drop, or positioning of the plates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If defective electrode plates are transported by vacuum transport to NG box, then defective plates are removed from production line, but abnormal transport causes collision and damage to electrode plates

Engineering Contradiction:

Improvedefective plate removal reliabilityVSAvoidcollision damage to electrode plates

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary detection of abnormal transport states using cameras and machine learning models before collisions occur. By identifying abnormal positions, stacking, or dropping of defective electrode plates during transport, the system can trigger alerts or stop the transport mechanism in advance, preventing collision damage to subsequent normal electrode plates.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors the transport process using cameras and provides real-time feedback through the machine learning model. When abnormal transport is detected, the system generates feedback signals to alert operators or automatically adjust the transport process, enabling timely intervention to prevent collision damage while maintaining reliable defective plate removal.

Inventive Principle:

Principle #23Feedback

2Productivity

If machine learning-based detection system is implemented, then abnormal transport is detected and productivity is improved, but system complexity increases

Engineering Contradiction:

Improvebattery assembly productivityVSAvoiddetection system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning model performs unsupervised learning to automatically identify abnormal transport patterns without requiring manual programming or constant human intervention. The system trains itself using captured images and autonomously detects anomalies, reducing the need for complex manual configuration and simplifying system operation while maintaining high productivity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces complex mechanical monitoring mechanisms with an intelligent vision-based detection system. Instead of using multiple sensors, mechanical switches, or complex physical monitoring devices, the patent uses cameras combined with machine learning algorithms to detect abnormal transport, simplifying the overall system architecture while improving detection accuracy and productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250157018A1Method and system for detecting abnormal transport of defective electrodes

Publication Date: 2025.05.15 SAMSUNG SDI CO LTD

AI summary

A method of detecting abnormal transport of a defective electrode plate, which is performed by at least one processor, the method including receiving a plurality of images associated with transport of a defective electrode plate from one or more cameras installed on a path of a secondary battery assembly process and detecting abnormal transport of the defective electrode plate on the path based on the images using a machine learning model based on unsupervised learning.