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Abstract

A polyurethane-based fabric coating process requires a series of parameters to be set in order to meet the desired quality of the final product. Usually, the optimal setting of such parameters is performed by means of experimental tests, based on the experience of trained operators. The lack of understanding of the interaction between the coating process parameters and the final quality properties of the coated fabric encourages the development of predictive models. The main aim of the present work is to provide a predictive model of a particular coating process for forecasting the final characteristics of a coated fabric, based on the process parameters. The devised model, based on artificial neural networks, is trained and validated using a wide experimental database created with reference to an innovative coating process. Once simulated with new process parameters, the model proves to be capable of determining the best possible process parameter values to obtain the preferred coated fabric properties. By employing the developed model, a series of charts are also built that can be used to provide technicians with a practical tool for effectively selecting the process parameters.

Keywords

coatings systems engineering testing

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Modelling and simulation of an innovative fabric coating process using artificial neural networks

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