title: ÐÐµÑÐ¾Ð´ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ Ð´Ð°Ð½Ð½ÑÐ¼ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ¼Ð¸ÑÑÐ¸Ð¸ Ð¿ÑÐ¸ Ð¸Ð·Ð³Ð¾ÑÐ¾Ð²Ð»ÐµÐ½Ð¸Ð¸ Ð¸ Ð¸ÑÐ¿ÑÑÐ°Ð½Ð¸Ð¸ ÑÐ²Ð°ÑÐ½ÑÑ ÐºÐ¾Ð½ÑÑÑÑÐºÑÐ¸Ð¹
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: Method for detecting defects based on machine learning using acoustic emission data during manufacture and testing of welded structures
Creators: name: Ð¡ÑÐ¾ÑÐ½Ð¾Ð²ÑÐºÐ¸Ð¹, ÐÐµÐ²
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: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¡Ð²Ð°ÑÐ½ÑÐµ ÐºÐ¾Ð½ÑÑÑÑÐºÑÐ¸Ð¸
Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐ¼Ð¸ÑÑÐ¸Ñ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ
Ð´ÐµÑÐµÐºÑÐ¾ÑÐºÐ¾Ð¿Ð¸Ñ
acoustic emission
data analysis
clustering
flaw detection

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐµÑÐ¾Ð´ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ Ð´Ð°Ð½Ð½ÑÐ¼ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ¼Ð¸ÑÑÐ¸Ð¸ Ð¿ÑÐ¸ Ð¸Ð·Ð³Ð¾ÑÐ¾Ð²Ð»ÐµÐ½Ð¸Ð¸ Ð¸ Ð¸ÑÐ¿ÑÑÐ°Ð½Ð¸Ð¸ ÑÐ²Ð°ÑÐ½ÑÑ ÐºÐ¾Ð½ÑÑÑÑÐºÑÐ¸Ð¹Â». Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸ÑÐµÐ»ÑÐ½Ð¾ Ðº Ð½ÐµÑÐ°Ð·ÑÑÑÐ°ÑÑÐµÐ¼Ñ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ ÑÐ²Ð°ÑÐ½ÑÑ ÑÐ¾ÐµÐ´Ð¸Ð½ÐµÐ½Ð¸Ð¹ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ¼Ð¸ÑÑÐ¸Ð¸. ÐÑÐ»Ð¾ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¾ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ðµ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ðµ Ð¾Ð±Ð»Ð°ÑÑÐ¸ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ ÑÐ²Ð°ÑÐºÐ¸ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ¼Ð¸ÑÑÐ¸ÐµÐ¹. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð±ÑÐ» ÑÐ¾Ð·Ð´Ð°Ð½ ÑÐ²Ð¾Ð¹ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ° Ð´Ð»Ñ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ, Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¾Ð»Ð¾Ð´Ð½ÑÑ ÑÑÐµÑÐ¸Ð½ Ð¸ Ð¼Ð¾Ð´ÐµÑÐ½Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ð¹ Ð¼ÐµÑÐ¾Ð´ Ð´Ð»Ñ Ð¸Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð° Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð³Ð¾ÑÑÑÐ¸Ñ ÑÑÐµÑÐ¸Ð½. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð¾Ð±Ð½Ð°ÑÑÐ¶Ð¸Ð²Ð°ÑÑ Ð´ÐµÑÐµÐºÑÑ Ñ Ð²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ Ð² ÑÐµÐ¶Ð¸Ð¼Ðµ ÑÐµÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸, ÑÑÐ¾ Ð´Ð¾Ð»Ð¶Ð½Ð¾ ÑÐ¿Ð¾ÑÐ¾Ð±ÑÑÐ²Ð¾Ð²Ð°ÑÑ ÑÑÐ°Ð½Ð´Ð°ÑÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¸ ÑÐ¿ÑÐ¾ÑÐµÐ½Ð¸Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð´ÐµÑÐµÐºÑÐ¾Ð². Ð ÑÐ²Ð¾Ñ Ð¾ÑÐµÑÐµÐ´Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð³Ð¾ÑÑÑÐ¸Ñ ÑÑÐµÑÐ¸Ð½ Ð´Ð¾Ð»Ð¶ÐµÐ½ Ð´Ð°ÑÑ Ð¿ÐµÑÐ²Ð¸ÑÐ½ÑÐ¹ ÑÐ¾Ð»ÑÐ¾Ðº Ð´Ð»Ñ Ð¿Ð¾ÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐ¸Ñ Ð½Ð°ÑÑÐ½ÑÑ ÑÐ°Ð±Ð¾Ñ Ð¿Ð¾ Ð¸Ð·ÑÑÐµÐ½Ð¸Ñ Ð¸Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ.
description: The subject of the graduate qualification work is Â«Method for detecting defectsbased on machine learning using acoustic emission data during manufacture and testingof welded structuresÂ». In this paper, the use of machine learning methods in relation to non-destructivetesting of welded joints using acoustic emission is investigated. The current state of theacoustic emission welding control field was described. Based on the existing solutions,the own prototype of defect controlling tool was created, cold crack formation wassimulated, the existing method for detecting them was upgraded, and the possibility ofdetecting hot cracks was investigated. The obtained methods allow detecting defectswith high accuracy in real time, which should contribute to the standardization of signalprocessing results and simplify the detection of defects. In turn, the result of the study ofthe possibility of detecting hot cracks should give a primary impetus to the appearanceof further scientific works on the study of their detection.