Equation-based and data-driven modeling strategies for industrial coating processes

Computational Fluid Dynamics (CFD) and Machine Learning (ML) approaches are implemented and compared in an industrial Chemical Vapor Deposition process for the production of cutting tools. In this work, the aim is to analyze the pros and cons of each method and propose a blend of the two approaches that is suitable in industrial applications, where the process is too complicated to address with first-principles models and the data do not allow the implementation of data-hungry methods. Both approaches accurately predict the coating thickness (Mean Absolute Percentage Error (MAPE) of 6.0% and 4.4% for CFD and ML respectively for the test case reactor). CFD, despite its increased computational cost, both in terms of developing and also calibrating for the application at hand, provides meaningful insight and illuminates the process. On the other hand, ML can provide predictions in a time-efficient manner, and is thus appropriate for inline and concurrent predictions. However, it is limited by the available data and has low extrapolation ability. Equation-based and data-driven methods are combined by exploiting a handful of CFD results for efficient interpolation in a reduced space defined by the principal components of the dataset, by implementing Gappy POD. This allows for the accurate reconstruction of the full state-space with limited data. •Industrial coating process investigated with Machine-learning and CFD models.•Both approaches predict coating thickness with comparable accuracy.•Simplified CFD model provides physical insight on species/velocity distributions.•Machine-learning prediction is ∼2500× faster, factoring in training time.•Two methods are merged for prediction from partial data with Gappy POD.