增材制造现场过程监测中声学数据处理的机器学习技术综述

IF 0.5 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
H. Taheri, S. Zafar
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引用次数: 1

摘要

计量、制造和无损评估界已经做出了许多努力,以研究各种有效的增材制造过程现场监测方法。研究人员调查了各种技术和传感器的使用情况,发现每种技术和传感器都有自己独特的能力和局限性。在所有测量技术中,基于声学的增材制造工艺原位测量为工艺和零件质量评估提供了显著的数据和优势。声学信号包含关于制造过程和具有足够采样率的制造部件的关键信息。与任何其他测量技术一样,基于声学的方法在应用和数据解释方面具有特定的挑战。在处理用于现场过程监测的声学数据时,数据结构的巨大规模和复杂性是重大挑战。为了解决这个问题,研究人员探索和研究了人工智能和机器学习方法所赋予的各种数据和信号处理技术,以从声学信号中提取实用信息。本文旨在调查增材制造现场监测中声学数据处理的最新和创新的机器学习技术和方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Techniques for Acoustic Data Processing in Additive Manufacturing In Situ Process Monitoring: A Review
There have been numerous efforts in the metrology, manufacturing, and nondestructive evaluation communities to investigate various methods for effective in situ monitoring of additive manufacturing processes. Researchers have investigated the use of a variety of techniques and sensors and found that each has its own unique capabilities as well as limitations. Among all measurement techniques, acoustic-based in situ measurements of additive manufacturing processes provide remarkable data and advantages for process and part quality assessment. Acoustic signals contain crucial information about the manufacturing processes and fabricated components with a sufficient sampling rate. Like any other measurement technique, acoustic-based methods have specific challenges regarding applications and data interpretation. The enormous size and complexity of the data structure are significant challenges when dealing with acoustic data for in situ process monitoring. To address this issue, researchers have explored and investigated various data and signal processing techniques empowered by artificial intelligence and machine learning methods to extract practical information from acoustic signals. This paper aims to survey recent and innovative machine learning techniques and approaches for acoustic data processing in additive manufacturing in situ monitoring.
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来源期刊
Materials Evaluation
Materials Evaluation 工程技术-材料科学:表征与测试
CiteScore
0.90
自引率
16.70%
发文量
35
审稿时长
6-12 weeks
期刊介绍: Materials Evaluation publishes articles, news and features intended to increase the NDT practitioner’s knowledge of the science and technology involved in the field, bringing informative articles to the NDT public while highlighting the ongoing efforts of ASNT to fulfill its mission. M.E. is a peer-reviewed journal, relying on technicians and researchers to help grow and educate its members by providing relevant, cutting-edge and exclusive content containing technical details and discussions. The only periodical of its kind, M.E. is circulated to members and nonmember paid subscribers. The magazine is truly international in scope, with readers in over 90 nations. The journal’s history and archive reaches back to the earliest formative days of the Society.
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