The purpose of this work is to study the applicability of the U-Net architecture for automatically determining the contours of natural skins using the TensorFlow and Keras libraries in Python. A software application has been developed based on methods including OpenCV libraries, as well as a model for implementing a deep convolutional neural network. The dataset for training and testing the network was created using augmentation. Training was carried out using the stochastic gradient descent method after splitting the data sample into training and test images. In the future, the results obtained will be used to create an automated system that will make it possible to determine the contours of the skin and its defects, which in turn will open up the possibility of calculating the useful area of ​​the skin and creating an automated layout of patterns taking into account the identified defects.

Keywords: computer vision, edge detection, natural skin, machine learning, convolutional neural networks, U-Net architecture, deep learning

The article presents studies of a computer simulation statistical model between the characteristics of flows in terms of linear density and the proportion of components in a mixed flow. The results of evaluating the effect of the filling fraction of the component on the average and standard deviation of the fraction of the 1st component in the mixture are presented, the type of autocorrelation functions of the linear density of the mixed stream and the fraction of the 1st component in the mixed stream are determined, estimates of the spectral density of dispersion for the linear density of the mixed stream and the fraction of the 1st component in the mixed stream are shown.

Keywords: fiber mixing, linear density, component fraction, autocorrelation function, spectral density of dispersion, standard deviation

The article discusses the conducted studies of changes in the output signal from a measuring device to assess the quality of mixing natural and chemical fibers in semi-finished products of spinning production obtained on a belt machine at various transitions. The construction of polynomial models in data analysis makes it possible to interpret information about the uniformity of fiber distribution in the tape, without taking into account the effect on changes in its linear density.

Keywords: fiber mixing quality, linear density, infrared estimation method, data estimation, linear polynomial, polynomial function

The problem of developing an intelligent automated system for detecting defects in textile materials is considered. An analysis of machine learning and deep learning algorithms was carried out in relation to solving the problem of product quality control. The implementation of an artificial neural network implemented in a Raspberry Pi microcomputer and receiving a set of input data in the form of a large stream of images from a high-speed digital camera is considered. The stages of creating a model in Python using the TensorFlow and Keras libraries are described. The development process includes the preparation of initial data intended for training and testing the system, as well as testing the operation of the resulting neural network, which consists in recognizing images of defects on fabric according to classification criteria.

Keywords: machine learning, neural network, defect images, textile material, training, testing, accuracy

The advantages, disadvantages and limitations of the use of thread seams in the technological processes of product assembly are considered, the advantages of replacing thread seams with thread-free joints, ways to obtain them in order to control the competitiveness of the technological process of hot-glue assembly, the production processes of obtaining a flat thread-free connecting seam using modern technological equipment are shown, possible ways of its formation are given.

Keywords: method, control, technological process, thread-free connection, adhesive joint, seam, parts, products, thermal tape, adhesion, technological equipment