Autonomous Visual Detection of Defects from Battery Electrode Manufacturing

Abstract

The increasing global demand for high‐quality and low‐cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long‐term reduction of reject rates, shortening of the production ramp‐up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You‐Only‐Look‐Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig.

PCB Soldering Defect Inspection Using Multitask Learning under Low Data Regimes

“…There are however not many open-source datasets related to PCB images, and all of them are not about PCB soldering defects. [15][16][17][18][19][20][21][22][23] For this reason, a new PCB soldering defect dataset, namely, PCBSPDefect is constructed. [24] The dataset contains images of PCB soldering points of three component parts, namely, dual in-line packages (DIP) located on the PCB back side (BDIP), DIP on the PCB front side (FDIP), and the flat flexible cables (FFC), as depicted in Figure 1.…”

Nonlinear Potentiodynamic Battery Charging Protocols for Fun, Education, and Application

“…Secondary batteries are a fascinating research topic as findings on the lab scale have a realistic potential to impact the electrification of everything . Therefore, great efforts are being undertaken across different domains, − i.e., everything from ab initio calculations , on the atomic level to manufacturing improvements through defect detection . A European effort to bring together this multimodal and multidisciplinary research is the Battery2030+ initiative, namely, the BIG-MAP project.…”

“…The idea is to discover and optimize batteries by jointly bringing together theory and experiment in workflows that allow us to seamlessly translate between the scales through the utilization of machine learning, artificial intelligence, and lab automation into a true materials acceleration platform . The established workflow to optimize − systems such as batteries is, however, to look at isolated parts of the complex system, i.e., to optimize electrolyte conductivity, reduce the defect concentration on coated electrodes, or to test whether or not an additive prolongs the cycle life of a battery . In the battery research field, one of the prime examples is the optimization , of fast charging schedules (e.g., for cargo bikes) that try to mitigate cell degradation despite high-charging currents.…”

“…1 Therefore, great efforts are being undertaken across different domains, 13−17 i.e., everything from ab initio calculations 2,3 on the atomic level 4 to manufacturing improvements through defect detection. 5 A European effort to bring together this multimodal and multidisciplinary research is the Battery2030+ 1 initiative, namely, the BIG-MAP 3 project. The idea is to discover and optimize batteries by jointly bringing together theory and experiment in workflows 6 that allow us to seamlessly translate between the scales 7 through the utilization of machine learning, 2 artificial intelligence, and lab automation 8 into a true materials acceleration platform.…”

Enhancing Quality Control in Battery Component Manufacturing: Deep Learning-Based Approaches for Defect Detection on Microfasteners

“…During the forging process, defects such as cracks or scratches on the surface cannot be Systems 2024, 12, 24 2 of 16 avoided. So, in order to prevent these defects from affecting the final product, researchers have studied different approaches to identify defects in the parts of the battery [9][10][11] or to improve battery manufacturing processing [12].…”