Thermal Imaging for Quality Control in Thin Silicon‐Based Coatings for Lithium‐Ion Batteries: Defect Detection, Drying Dynamics, and Machine Learning‐Based Mass Loading Estimation DOI Creative Commons
Adil Amin,

Philipp Valentin Geiping,

Ahammed Suhail Odungat

и другие.

Small Methods, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Abstract This study demonstrates thermal imaging as a non‐destructive, real‐time quality‐control‐method for detecting coating defects, analyzing mass loading, and understanding drying dynamics in silicon‐based thin coatings. Thermal identifies critical defects such streaks, pinholes, chatter marks through distinct signatures, with streaks reducing surface temperature by up to 15 °C. It establishes strong correlations between temperature, thickness: instance, 100 µm wet film thickness shows of ≈50 °C, corresponding loading 2.4 mg cm⁻ 2 . Drying reveal that thicker coatings retain more solvent, prolong drying, shrink significantly, wet‐gap shrinking 60%. A Random Forest machine learning model predicts high accuracy (±0.3 ) using data, highlighting the feasibility imaging‐based quality estimation. While validated batch process, this approach is well‐suited integration into roll‐to‐roll production across diverse applications, batteries, solar cells, functional films. provides robust pathway defect detection, optimization, control, improving performance reliability.

Язык: Английский

Thermal Imaging for Quality Control in Thin Silicon‐Based Coatings for Lithium‐Ion Batteries: Defect Detection, Drying Dynamics, and Machine Learning‐Based Mass Loading Estimation DOI Creative Commons
Adil Amin,

Philipp Valentin Geiping,

Ahammed Suhail Odungat

и другие.

Small Methods, Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Abstract This study demonstrates thermal imaging as a non‐destructive, real‐time quality‐control‐method for detecting coating defects, analyzing mass loading, and understanding drying dynamics in silicon‐based thin coatings. Thermal identifies critical defects such streaks, pinholes, chatter marks through distinct signatures, with streaks reducing surface temperature by up to 15 °C. It establishes strong correlations between temperature, thickness: instance, 100 µm wet film thickness shows of ≈50 °C, corresponding loading 2.4 mg cm⁻ 2 . Drying reveal that thicker coatings retain more solvent, prolong drying, shrink significantly, wet‐gap shrinking 60%. A Random Forest machine learning model predicts high accuracy (±0.3 ) using data, highlighting the feasibility imaging‐based quality estimation. While validated batch process, this approach is well‐suited integration into roll‐to‐roll production across diverse applications, batteries, solar cells, functional films. provides robust pathway defect detection, optimization, control, improving performance reliability.

Язык: Английский

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