title: Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ ÑÐ²Ð¾Ð¹ÑÑÐ² Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð¾Ð² Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Machine learning algorithms of material pattern recognition
Creators: name: Ð¢ÑÑÑÐ°Ð½, Ð¥ÑÐ±ÐµÑÑ
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: 2018
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ

description: ÐÐ´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð¾Ð² Ð² Ð¾Ð´Ð½Ð¾Ð¹ Ð³Ð¸Ð±ÑÐ¸Ð´Ð½Ð¾Ð¹ Ð·Ð°Ð³Ð¾ÑÐ¾Ð²ÐºÐµ ÑÐ²Ð»ÑÐµÑÑÑ Ð½ÐµÐ¾ÑÑÐµÐ¼Ð»ÐµÐ¼Ð¾Ð¹ ÑÐ°ÑÑÑÑ Ð² ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÑÑÐµÐ¹ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾ÑÑÐ¸. ÐÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð»Ð¸Ð½Ð¸Ð¸ ÑÑÐ°Ð½Ð¾Ð²ÑÑÑÑ Ð±Ð¾Ð»ÐµÐµ Ð³Ð¸Ð±ÐºÐ¸Ð¼Ð¸, Ð° Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐµ ÐºÐ¾Ð¼Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ñ ÑÑÐ°Ð½Ð¾Ð²ÑÑÑÑ Ð±Ð¾Ð»ÐµÐµ ÑÐ¾Ð²ÐµÑÑÐµÐ½Ð½ÑÐ¼Ð¸, ÑÑÐ¾ ÑÐ¾Ð·Ð´Ð°ÐµÑ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð° Ð¸ Ð°Ð´Ð°Ð¿ÑÐ°ÑÐ¸Ð¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¿ÑÐ¾ÑÐµÑÑÐ° Ð²Ð¾ Ð²ÑÐµÐ¼Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸. [DEN18]. Ð¡ÑÑÐµÑÑÐ²ÑÐµÑ Ð¼Ð½Ð¾Ð³Ð¾ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð´Ð»Ñ ÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÐµÐ¼Ð¾Ð³Ð¾ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð°, Ð½Ð¾ Ð´Ð¾ ÑÐ¸Ñ Ð¿Ð¾Ñ Ð½ÐµÑ ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¾ÑÐ²ÐµÑÐ° Ð½Ð° Ð²Ð¾Ð¿ÑÐ¾Ñ Ð¾ ÑÐ¾Ð¼, ÐºÐ°ÐºÐ¾Ð¹ Ð»ÑÑÑÐµ Ð²ÑÐµÐ³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÑÑ.
Ð­ÑÐ° ÑÐ°Ð±Ð¾ÑÐ° ÑÐµÑÑÐ¸ÑÑÐµÑ 3 Ð¾Ð±ÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑ Ð¿ÑÐ¸ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÐºÐ¾Ð¼Ð±Ð¸Ð½Ð°ÑÐ¸Ð¸ Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»Ð¾Ð². ÐÐ·Ð¼ÐµÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÑÑ Ð½Ð° 6 Ð½Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð°Ð½Ð½ÑÑ: 2 Ð·Ð°Ð³Ð¾ÑÐ¾Ð²ÐºÐ¸ (1. ÐÐ»ÑÐ¼Ð¸Ð½Ð¸Ð¹ en-aw6082 - ÑÑÐ°Ð»Ñ 20mncr5 Ð¸ 2. C22 - 41cr4) Ð´Ð»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ Ð¸Ð· ÑÑÐµÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ (ÑÐ¾ÑÐµÐ½Ð¸Ðµ, ÑÑÐµÐ·ÐµÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ ÑÐ²ÐµÑÐ»ÐµÐ½Ð¸Ðµ). ÐÐ»Ð¸ÑÐ½Ð¸Ðµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¾Ð± Ð¸ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð¾Ð±ÑÑÐ¶Ð´Ð°ÐµÑÑÑ Ð² ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¸Ð¸ Ñ ÐºÐ¾Ð½ÐºÑÐµÑÐ½ÑÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸.
Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ, ÑÑÐ¾ Ð»ÑÑÑÐ¸Ð¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð¼ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ²Ð»ÑÐµÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ coarse tree algorithm. ÐÐµÑÐ¼Ð¾ÑÑÑ Ð½Ð° Ð±Ð¾Ð»ÑÑÐ¾Ðµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð½ÑÑ ÑÐµÑÑÐ¾Ð², Ð½Ðµ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ ÑÑÐ¾ÑÐ¼ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½ ÐµÐ´Ð¸Ð½ÑÐ¹ Ð²ÑÐ²Ð¾Ð´ Ð¾ Ð²Ð»Ð¸ÑÐ½Ð¸Ð¸ ÑÐ¸ÑÐ»Ð° Ð¿ÑÐ¾Ð± Ð½Ð° ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, Ð¿Ð¾ÑÐºÐ¾Ð»ÑÐºÑ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ðµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾Ð¿ÑÐ¾Ð± ÑÐ°Ð·Ð»Ð¸ÑÐ½Ð¾ Ð´Ð»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ Ð¿ÑÐ¾ÑÐµÑÑÐ°. ÐÐ»Ð¸ÑÐ½Ð¸Ðµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð½Ð° ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð² Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð¼ Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÐµÑ Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð²Ð½Ð¾Ðµ Ð²Ð¾Ð·Ð´ÐµÐ¹ÑÑÐ²Ð¸Ðµ Ð¸ Ñ Ð¸Ñ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸ÐµÐ¼ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿Ð¾Ð²ÑÑÐ°ÐµÑÑÑ Ð¸Ð»Ð¸ Ð¾ÑÑÐ°ÐµÑÑÑ Ð½Ð° ÑÐ¾Ð¼ Ð¶Ðµ Ð²ÑÑÐ¾ÐºÐ¾Ð¼ ÑÑÐ¾Ð²Ð½Ðµ.
description: The identification of materials combination in one hybrid workpiece is an important part in modern manufacturing processing. Manufacturing lines become more flexible and processed composite materials become more advanced, which creates the challenge of identifying the material and adapting the process parameters during machining. [DEN18]. There are many possible methods for precise determination of the machined material but there is still no clear answer as to which is best. 
This work tests 3 common machine learning algorithms and Neural Network in identification of the materials combination. The measures are tested on 6 data sets: 2 workpieces (1. aluminum EN-AW6082 - steel 20MnCr5 and 2. C22 - 41Cr4) for each of 3 processing methods (turning, milling, and drilling). The influence of the number of probes and number of features is discussed with regard to particular processing method and identification algorithm accuracy. 
The results indicate that the best identification algorithm is Coarse Tree algorithm. Despite the large number of evaluated cases, no unified conclusion can be formulated about the influence of number of probes on identification accuracy, because the optimal number of probes is different for each of the process. The influence of the number of features on the prediction accuracy mostly has a positive impact and with the increase of the features the accuracy rises, or stays on the same high level.