title: ÐÐ¸ÐºÑÐ¾ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÐ½Ð¾Ðµ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð¾ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð´ÐµÑÐµÐºÑÐ¾Ð² ÑÐ»ÐµÐºÑÑÐ¾Ð´Ð¾Ð², Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÑ Ð² Ð³Ð°Ð»ÑÐ²Ð°Ð½Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸ÑÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Microcontroller device for classifying and evaluating the parameters of defects in electrodes used in galvanic devices
Creators: name: Ð®ÑÑÐ¿Ð¾Ð²Ð°, ÐÐ¸Ð°Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: STM32
Ð¼ÐµÐ´Ð½Ð¾-ÑÐ¸ÑÐ°Ð½Ð¾Ð²ÑÐµ ÑÐ»ÐµÐºÑÑÐ¾Ð´Ñ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð·Ð°Ð´Ð°ÑÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
copper-titanium electrodes
machine learning
classification task
neural networks

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐ¸ÐºÑÐ¾ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÐ½Ð¾Ðµ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð¾ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð´ÐµÑÐµÐºÑÐ¾Ð² ÑÐ»ÐµÐºÑÑÐ¾Ð´Ð¾Ð², Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÑ Ð² Ð³Ð°Ð»ÑÐ²Ð°Ð½Ð¸ÑÐµÑÐºÐ¸Ñ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð°ÑÂ». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ñ ÑÐµÐ»ÑÑ, ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÑÑÐµÐ¹ Ð½Ð°Ð·Ð²Ð°Ð½Ð¸Ñ. Ð ÑÐ¾Ð´Ðµ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ Ð´Ð°Ð½Ð½ÑÐµ, Ð¿ÑÐµÐ´Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐµ Ð·Ð°ÐºÐ°Ð·ÑÐ¸ÐºÐ¾Ð¼: Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½ÑÐµ Ð´Ð¸Ð°Ð³ÑÐ°Ð¼Ð¼Ñ, ÑÐ½ÑÑÑÐµ Ñ Ð¼ÐµÐ´Ð½Ð¾-ÑÐ¸ÑÐ°Ð½Ð¾Ð²ÑÑ ÑÐ»ÐµÐºÑÑÐ¾Ð´Ð¾Ð². ÐÐ¾ Ð²Ð¸Ð´Ñ ÑÑÐ¸Ñ Ð´Ð¸Ð°Ð³ÑÐ°Ð¼Ð¼, Ð¿Ð¾ÑÐ»Ðµ ÑÐ¾Ð³Ð»Ð°ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ñ ÑÐºÑÐ¿ÐµÑÑÐ¾Ð¼, Ð±ÑÐ»Ð¾ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¾ Ð¿ÑÑÑ Ð³ÑÑÐ¿Ð¿, Ð¿Ð¾ ÐºÐ¾ÑÐ¾ÑÑÐ¼ ÑÐ°Ð¼ Ð·Ð°ÐºÐ°Ð·ÑÐ¸Ðº Ð¾Ð¿ÑÐµÐ´ÐµÐ»Ð¸Ñ Ð²Ð¸Ð´ Ð´ÐµÑÐµÐºÑÐ°. ÐÑÐ»Ð¸ Ð¸Ð·ÑÑÐµÐ½Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¿Ð¾ Ð³ÑÑÐ¿Ð¿Ð°Ð¼. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¾ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð° Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹ Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. ÐÐ»Ñ ÑÐ¼ÐµÐ½ÑÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð²ÑÐ¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½ÑÐµ Ð´Ð¸Ð°Ð³ÑÐ°Ð¼Ð¼Ñ Ð±ÑÐ»Ð¾ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¾ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð¸ÑÑ Ð² Ð²Ð¸Ð´Ðµ Ð¿Ð¾Ð»Ð¸Ð½Ð¾Ð¼Ð¸Ð°Ð»ÑÐ½ÑÑ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¹, Ð¾Ð´Ð½Ð°ÐºÐ¾ Ð¾ÐºÐ°Ð·Ð°Ð»Ð¾ÑÑ, ÑÑÐ¾ ÑÑÐ¾ Ð½Ðµ Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸Ñ Ðº Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. Ð£Ð´Ð°Ð»Ð¾ÑÑ Ð²ÑÑÑÐ½Ð¸ÑÑ, ÑÑÐ¾ Ð±Ð¾Ð»ÐµÐµ Ð²ÑÑÐ¾ÐºÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð» Ð¼Ð½Ð¾Ð³Ð¾ÑÐ»Ð¾Ð¹Ð½ÑÐ¹ Ð¿ÐµÑÑÐµÐ¿ÑÑÐ¾Ð½.ÐÐ¾Ð´ÐµÐ»Ñ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð² ÑÑÐµÐ´Ðµ PyCharm, Ð±ÑÐ»Ð° Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð° Ð² Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð»Ñ ÑÐ°Ð±Ð¾ÑÑ Ð½Ð° Ð¼Ð¸ÐºÑÐ¾ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÐµ, Ð¿ÑÐµÐ´Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¼ Ð·Ð°ÐºÐ°Ð·ÑÐ¸ÐºÐ¾Ð¼, Ð² ÑÑÐµÐ´Ðµ STM32CubeMX Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿Ð°ÐºÐµÑÐ° X-CUBE-AI.ÐÐ¸ÐºÑÐ¾ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑ Ð½Ð° Ð¾ÑÐ»Ð°Ð´Ð¾ÑÐ½Ð¾Ð¹ Ð¿Ð»Ð°ÑÐµ Ð±ÑÐ» Ð¿ÑÐ¾ÑÐ¸Ñ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°ÑÐ¾ÑÐ°, Ð·Ð°ÑÐµÐ¼ Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° ÐµÐ³Ð¾ ÑÐ°Ð±Ð¾ÑÐ° Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¾Ð²Ð°Ð» Ð¾Ð¶Ð¸Ð´Ð°ÐµÐ¼Ð¾Ð¼Ñ. Ð¢Ð°ÐºÐ¸Ð¼ Ð¾Ð±ÑÐ°Ð·Ð¾Ð¼, ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ ÑÐ°Ð±Ð¾ÑÑ ÑÐ»ÐµÐ´ÑÑÑÐ¸Ð¹: ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð¾ Ð¼Ð¸ÐºÑÐ¾ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð»ÐµÑÐ½Ð¾Ðµ ÑÑÑÑÐ¾Ð¹ÑÑÐ²Ð¾ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð´ÐµÑÐµÐºÑÐ¾Ð² ÑÐ»ÐµÐºÑÑÐ¾Ð´Ð¾Ð², Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÑ Ð² Ð³Ð°Ð»ÑÐ²Ð°Ð½Ð¸ÑÐµÑÐºÐ¸Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸ÑÑ.
description: Topic of the final qualification work: "Microcontroller device for classifying and evaluating the parameters of defects in electrodes used in galvanic devices". This work was carried out with the purpose corresponding to the title.In the course of this work, the data provided by the customer were analyzed: potential diagrams taken from copper-titanium electrodes. According to the type of these diagrams, after agreement with the expert, five groups were identified, according to which the customer himself will determine the type of defect. Possible methods have been explored that allow classification into groups. It is proposed to consider the possibility of using the method of nearest neighbors and neural networks. To reduce the amount of input data, it was proposed to present potential diagrams in the form of polynomial regressions, but it turned out that this does not lead to an increase in the classification accuracy. It was possible to find out that a multilayer perceptron showed a higher classification accuracy. The trained neural network model implemented in the PyCharm environment was converted to a model for running on a customer-provided microcontroller in the STM32CubeMX environment using the X-CUBE-AI package.The microcontroller on the debug board was flashed using a programmer, then its operation was tested and the result was as expected. Thus, the result of the work is as follows: a microcontroller device has been developed for classifying and evaluating the parameters of electrode defects used in galvanic technologies.