Data-Driven Optimization of Aqueous Tape Casting for High-Performance Functional Ceramic Films
Keywords:
aqueous tape casting, functional ceramic films, data-driven optimization, machine learning, process control, multilayer ceramic capacitors, sustainability, governanceAbstract
Aqueous tape casting has emerged as a strategically important route for manufacturing functional ceramic films because it reduces organic solvent use, improves workplace safety, and aligns with sustainability mandates in electronics and energy systems. Yet the process remains difficult to optimize because slurry formulation, colloidal stability, casting rheology, drying kinetics, sintering trajectories, and microstructural outcomes are tightly coupled across multiple length and time scales. This paper examines data-driven optimization of aqueous tape casting as a socio-technical manufacturing system rather than a narrow laboratory technique. It argues that high-performance functional ceramic films require an integrated architecture that combines inline sensing, curated process data, physics-informed models, active learning, digital twins, and governance mechanisms for reproducibility and fairness. The discussion considers structural trade-offs among film density, flexibility, dielectric breakdown strength, surface roughness, shrinkage control, and manufacturing throughput. It also evaluates deployment challenges in pilot lines and foundries, including data ownership, calibration drift, model transferability, infrastructure gaps, and the unequal distribution of advanced characterization resources. Sustainability and policy implications are analyzed through the lens of solvent reduction, energy intensity, supply-chain resilience, and responsible automation. Case illustrations from multilayer ceramic capacitors, piezoelectric films, and solid-state energy devices show that data-driven aqueous tape casting can improve robustness and yield when optimization is treated as a lifecycle and ecosystem problem. The paper concludes that the most durable gains will come from hybrid human-machine learning, transparent data governance, and deployment frameworks that reward reproducibility, safety, and inclusive innovation alongside performance.
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