Fiber dyeability is a core indicator of textile quality and added value. Pre-experiment accurate prediction of fiber dyeability reduces the waste and inefficiency of trial-and-error methods. However, due to the limited data volume and complex mechanisms of fiber dyeability, there are no relevant studies to date. Thus, this paper proposes a novel prediction model integrating domain knowledge and process data called multi-head attention–physics-informed neural network (MHA-PINN). Within the MHA-PINN framework, limited experimental data is first augmented by using variational autoencoders, and subjected to ensemble feature selection on the augmented samples. Subsequently, a multi-head attention layer is introduced to capture the interdependencies among sample variables, thereby outputting a new feature matrix that represents the weighted fusion of these variables. Finally, a physics-informed neural network module embeds the dyeing kinetic equations into the loss function, guiding the model to converge towards accurate solutions for sample predictions. The effectiveness and superiority of the proposed MHA-PINN have been validated on a fiber dyeability experimental dataset.
Zhou et al. (Tue,) studied this question.