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This study introduces a transient heat-transfer model for the automated tape laying (ATL) process that addresses the shortcomings of traditional thermal models in composite manufacturing. By solving the heat-conduction equation with a comprehensive approach that includes advection, conduction, convection, and radiation, the model significantly improves the accuracy of radiative heat flux estimations through an innovative analytical view factor method. Validation on an industrial ATL line showed an impressive deviation of only 1.08% (NRMSE), underscoring the model's effectiveness in enhancing thermal state estimation and part quality during rapid process changes.
Achieving just 1.08% deviation in thermal predictions under dynamic conditions could revolutionize quality control in composite manufacturing.
This work presents a transient heat-transfer model of an industrial automated tape laying (ATL) process designed to overcome the limitations of conventional thermal models in composite manufacturing. The model solves the heat-conduction equation with coupled advection, conduction, convection, and radiation. A key innovation is the implementation of an analytical view factor approach that accounts for finite emitter and tape widths, thereby correcting systematic overestimations of radiative heat flux inherent in 1.5D simplifications. Furthermore, a local convection assessment incorporates mixed convection effects characterized by the Richardson number, ensuring accuracy across a wide range of process speeds. The ATL system is represented by two interacting subsystems: the moving tape substrate and the infrared heat sources. The tape is discretized using a two-node model that resolves the physical phase shift between the heated and monitored surfaces. Numerical stability under high dynamics is ensured by a monolithic solution strategy using a high-order implicit integration scheme. Model predictions were validated on an industrial ATL line, demonstrating an overall deviation of only 1.08% (NRMSE) under rapid velocity and current modulations. This framework provides a high-fidelity, physics-based foundation for thermal state estimation, supporting consistent in-situ consolidation and improved part quality.