Estimation and Prediction of the Polymers' Physical Characteristics Using the Machine Learning Models.

This article investigates the utility of machine learning (ML) methods for predicting and analyzing the diverse physical characteristics of polymers. Leveraging a rich dataset of polymers' characteristics, the study encompasses an extensive range of polymer properties, spanning compressive and tensile strength to thermal and electrical behaviors. Using various regression methods like Ensemble, Tree-based, Regularization, and Distance-based, the research undergoes thorough evaluation using the most common quality metrics.

The Technology of Using Liquid Glass Mixture Waste for Reducing the Harmful Environmental Impact.

The spent liquid glass mixture, which is widely used in foundries as a binder after knocking out of moldings, contains pieces of different sizes and strengths, and there is a strong silicate film on the sand grains themselves. The proposed regeneration plants, which provide for the removal of the silicate film by scrubbing, have low productivity and lead to abrasion of the grains themselves. For this reason, the knocked-out mixture is taken to the dump.

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process.

In industries that implement the technology of induction soldering, various sensors, including non-contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution that is effective in different operating conditions in terms of the accuracy of the data obtained and the reliability of the measurement equipment and duplication in case of a failure. The present article discusses the development of intelligent technology based on a collection of artificial neural networks, which allows a number of problems associated with technological process control when using pyrometric sensors to be solved: assessing the quality of measurements, correcting measurements when non-standard errors are detected, and controlling the process of induction heating in the absence of reliable readings of the measurement instruments.