The growing demand for Lithium-ion (Li-ion) batteries, driven by applications such as electric vehicles and long-duration energy storage, has increased the pressure on battery manufacturers to enhance both product quality and process efficiency.
Electrode production plays a critical role in the battery supply chain, with an expanding range of options available for monitoring the electrode coating process.
Thermo Fisher Scientific’s Thermo Scientific™ LInspector™ Edge In-line Mass Profilometer is an innovative tool that determines the critical parameter of electrode coating weight. With its high resolution, the LInspector Edge In-line Mass Profilometer can efficiently and accurately detect manufacturing defects, ensuring real-time coating quality assurance.
This article explores the measurable metrics, their applications, and the opportunities they present for advanced process control.
A Foundation for Modern Manufacturing
For battery manufacturers, achieving smart manufacturing, digital transformation, and Industry 4.0 relies on adopting innovative solutions for process monitoring. Tools that deliver advanced analytics, real-time data, and interconnectivity are essential to enable decentralized and fully automated decision-making.
The LInspector Edge Inline Mass Profilometer utilizes novel metrology—in-line mass profilometry—to measure the entire surface of coated electrodes in real time, providing coating weight profiles within milliseconds. This generates a rich stream of information that aids in efforts to:
- Assertively classify defects during coating and develop appropriate remedial and management strategies to minimize their impact.
- Improve process control.
- Establish robust end-to-end traceability for every battery.
- Develop multi-physics and data-driven models to facilitate more predictive design and electrode manufacturing processes.
- Build digital twins capable of forecasting the outcomes of various operational strategies, enabling advanced process optimization.
In-line mass profilometry can help battery manufacturers overcome issues such as scrap rates—currently estimated at around 5 % and 30 %—and relatively high levels of unexpected downtime while enhancing battery quality and safety.
Figure 1. LInspector Edge In-line Mass Profilometer. Image Credit: Thermo Fisher Scientific – Production Process & Analytics
Data Analysis 1: Coating Weight Uniformity
Figure 2 illustrates a two-dimensional heat map of coating weight uniformity in the cross direction (CD) and machine direction (MD).
This live graphical representation of coating weight data employs color coding to depict the magnitude of various values across the coated surface. The traces displayed below and to the right of the heat map highlight the numerical variability for each emerging electrode.
In-line mass profilometry delivers 100 % coating weight measurement, a statistically significant real-time dataflow for responsive decision-making.
Providing complete surface inspection at full production speed, it eliminates the limitations of scanning gauges and other traversing frame technology, which can miss significant areas of the coating, especially at high production speeds.
The straightforward design of the heat map enables process engineers, production managers, and analysts to quickly identify and examine defective regions. For instance, it allows engineers to detect low or high spots, which can then be analyzed in detail to observe the magnitude of individual values and evaluate the degree of variability across the electrode.
Figure 2. 2D heat map, high-resolution CD and MD profiles. Image Credit: Thermo Fisher Scientific – Production Process & Analytics
Data Analysis 2: Defect Detection and Tracking
At higher magnifications, heat maps are particularly effective in revealing defects, as illustrated in Figure 3. Small red areas indicate localized patches of excess coating, which may result from issues such as agglomerates or bubbles. In contrast, broader blue striations across the surface suggest a general lack of loading uniformity across the sample.
Battery manufacturers must address a variety of defect types, including:
- Coating weight defects relating to either average weight or high/low points
- Uniformity defects such as CD spread, MD spread, total spread, Cp and CpK
- Dimensional defects – width or length
- Edge faults, including shallow edge slopes or high edge slopes (bunny ear) on any edge
- Scratches/streaks
- Voids/bubbles
- Agglomerates
- Contaminants
- Chatter/ribbing
Defects can either extend across the full width of the electrode roll, affecting a specific length or be localized to a small, confined area.
Figure 4 presents a heat map stripe profile display, emphasizing edge defects (marked in red).
To enhance visibility and facilitate assessment, stripe edge zoom profiles for these samples are shown in Figure 5. These profiles highlight the side edges of coated stripes across CD web profiles.
Together, these figures demonstrate key features of the system software for defect monitoring, including the ability to:
- Display a minimum of 4 stripe profiles per page and up to 16 scrollable stripes or full-width profiles.
- See coated (white) and uncoated (light blue) zones in real-time.
- Set-up sub-zoom windows for the left and right edges of each stripe for up to 16 stripes.
Statistical values and setup options for the chart display can be customized to suit specific processing requirements. For instance, in the sub-zoom window, users can adjust the X-axis edge zoom display size (in mm) and zoom width (as a percentage) with a 75/25 % bias toward the coating near the target edge position. Similar modifications can also be made to the Y-axis.
Figure 3. 2D birds eye view, high dimensional CD and MD data and defect identification. Image Credit: Thermo Fisher Scientific – Production Process & Analytics
Figure 4. Heat map view with edge defect identification. Image Credit: Thermo Fisher Scientific – Production Process & Analytics
Figure 5. CD stripe edge zoom profiles showing sub-zoom windows for each edge (lower half of the image). Image Credit: Thermo Fisher Scientific – Production Process & Analytics
The red areas in the image above indicate that the coating has deviated from the defined specifications, triggering remedial actions to minimize the amount of coated electrode lost to scrap.
In contrast, conventional scanning technology is slower and more prone to missing substandard areas of the electrode, resulting in a greater volume of out-of-specification product before corrective measures can be taken.
Importantly, all the data presented not only facilitates easier manual decision-making but also provides a robust foundation for advanced, automated process control.
Looking Ahead
Battery manufacturers must strengthen their understanding of production and employ advanced process control to create smart, highly effective processes and meet increasingly strict levels of product quality.
The LInspector Edge In-line Mass Profilometer captures high-resolution data at unprecedented speeds, offering a versatile toolset for analysis and establishing a strong foundation for achieving this goal.
Coating quality is assessed by measuring variations in loading, while rapid, high-sensitivity measurements enhance the ability to detect and identify defects. This valuable data enables the prediction and mitigation of potential issues, helping to minimize their impact.
Defect-free electrodes are essential for producing high-quality Li-ion batteries, requiring thorough and comprehensive quality inspection at all production speeds to achieve higher productivity and quality standards.
With its capability to assess product quality across all types of electrode coatings, this advanced technology plays a crucial role in promoting resource and cost efficiency during production while identifying opportunities for continuous improvement.
This information has been sourced, reviewed and adapted from materials provided by Thermo Fisher Scientific – Production Process & Analytics.
For more information on this source, please visit Thermo Fisher Scientific – Production Process & Analytics.