Scholarlink

Abstract <p>The issue of detecting defects is explored for products made using the selective laser sintering during automated ultrasonic testing. An adaptive algorithm is proposed to detect and localize defects in automated ultrasonic quality control. It is based on combined use of recurrent neural network methods with long-term short-term memory (LSTM). It allows localizing defects based on the amplitude of the ultrasonic signal exceeding a given threshold value. A convolutional neural network (YOLOv5 architecture) is used to classify defects based on the B-scan image. The algorithm automatically determines the type, size, and location of defects, including the depth. The developed algorithm is capable of quickly matching the software settings for any type and size of control objects, provided the matching is made using artificially generated defects.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

New Algorithm to Register Defects through Automated Non-Destructive Testing in Products Obtained Using Selective Laser Sintering

  • 期刊论文

  • 发表日期：

    2026年8月4日

查看全文
  • A. S. Sergeev

摘要

Abstract

The issue of detecting defects is explored for products made using the selective laser sintering during automated ultrasonic testing. An adaptive algorithm is proposed to detect and localize defects in automated ultrasonic quality control. It is based on combined use of recurrent neural network methods with long-term short-term memory (LSTM). It allows localizing defects based on the amplitude of the ultrasonic signal exceeding a given threshold value. A convolutional neural network (YOLOv5 architecture) is used to classify defects based on the B-scan image. The algorithm automatically determines the type, size, and location of defects, including the depth. The developed algorithm is capable of quickly matching the software settings for any type and size of control objects, provided the matching is made using artificially generated defects.