This paper presents an optimization procedure of parameters in a multi-scale neural model, which is crucial for accurately characterizing the behavior of amorphous materials; more specifically glass transition kinetics, which is considered one of the most important yet not fully understood phenomena of solid-state physics and chemistry, with wide-ranging applications. Through systematic exploration of a hyperparameter grid space and rigorous evaluation of resultant models, a highly effective configuration was identified – Conv1D kernel size=8-24-48, Conv1D #filters=16 & Dense #neurons=16 – which offers optimal performance with remarkably short mean epoch times. Our findings suggest a promising strategy of increasing kernel size while decreasing the number of filters and neurons in the dense layer, supported by the superior performance of key competitors sharing this trend. However, further examination reveals comparable performance levels among subsequent competitors, indicating the need for additional samples to draw definitive conclusions. Our experiment highlights the intricate relationship between model accuracy and computational resources, emphasizing the necessity for further results to gain a comprehensive understanding of hyperparameter impact. Three promising combinations for future experimentation emerge, with Conv1D kernel size=8-24-48, Conv1D #filters=16 & Dense #neurons=16 standing out as the clear winner for practical application.

Coil coating is a method of applying an organic coating material to a rolled metal strip substrate in a continuous automated process. It is used to provide a high quality, durable finish to a variety of surfaces. The degradation resistance of coil-coated materials is assessed according to European Standard EN 13523–8 by exposing a coil-coated test specimen to a salt fog at a defined temperature for a defined period of time. After this process, a sample is tested according to the International Organisation for Standardisation ISO 4628 standard to determine the degree of degradation. In this study, a GAN-based technique for automated training set enhancement is proposed to assess the degree of degradation around a scribe. The presented technique is capable of enhancing a manually generated dataset of images with synthetic samples to help refine the performance of the area degradation detector.