Achieving a uniform and reliable cure remains a key challenge in porcelain enamel processing, particularly as manufacturers contend with increasingly complex part geometries and the need for greater energy efficiency. This research lays the groundwork for a simulation-based framework aimed at supporting predictive diagnostics and process transparency in industrial firing systems. The long-term objective is to develop a digital twin architecture capable of estimating cure conditions at the part level, using a combination of physics-based modeling and soft real-time data fusion.

This presentation focuses on the initial stage of the project: constructing a transient thermal modeling framework for enamel-coated parts undergoing firing cycles. A CAD-based simulation approach is employed to resolve heat transfer behavior in complex three-dimensional geometries, enabling spatially accurate predictions of thermal gradients and time-dependent surface temperatures. The modeling approach accounts for conduction through multilayer materials and radiative interactions within furnace environments, offering flexibility for a wide range of part configurations.

These simulations are intended to support soft real-time cure prediction by estimating internal thermal histories using limited external sensor data, avoiding the need for invasive instrumentation. The presentation outlines the simulation methodology, boundary condition assumptions, and system architecture that will support future integration into industrial control and monitoring platforms.

A representative case study—interior pipe coating in a radiant tube batch furnace—is used to illustrate the framework, though the approach is designed to be extensible to other parts and furnace types, including both batch and continuous operations. This work establishes a foundation for predictive modeling and digital diagnostics in porcelain enamel curing, with the long-term goal of supporting improved quality assurance, energy efficiency, and adaptive process control.