Modular, Physically Motivated Simulation Model of an Ultrasonic Testing System

Author:

Schäfer Marius W.1ORCID,Fischer Sarah C. L.1ORCID

Affiliation:

1. Fraunhofer IZFP, Campus E3.1, 66123 Saarbrücken, Germany

Abstract

The increasing complexity of material systems requires an extension of conventional non-destructive evaluation methods such as ultrasonic testing. Many publications have worked on extending simulation models to cover novel aspects of ultrasonic transducers, but they do not cover all components of the system. This paper presents a physically motivated, modular model that describes the complete signal flow with the aim of providing a platform for optimizing ultrasonic testing systems from individual components to the whole system level. For this purpose, the ultrasonic testing system is divided into modules, which are described by models. The modules are each parameterized by physical parameters, characteristics of real components as provided by datasheets, or by measurements. In order to validate the model, its performance is presented for three different configurations of a real test system, considering both classical sinusoidal excitation and a chirp signal. The paper demonstrates the modularity of the model, which can be adapted to the different configurations by simply adapting the modified component, thus drastically reducing the complexity of modeling a complex ultrasonic system compared to State-of-the-Art models. Based on this work, ultrasonic inspection systems can be optimized for complex applications, such as operation with coded excitation, which is a major challenge for the system components.

Funder

Fraunhofer Internal Programs

Publisher

MDPI AG

Reference32 articles.

1. Ultrasonic monitoring of erosion/corrosion thinning rates in industrial piping systems;Honarvar;Ultrasonics,2013

2. Gelman, L., Martin, N., Malcolm, A.A., and Liew, C.K. (2021). Measurement of Axial Force of Bolted Structures Based on Ultrasonic Testing and Metal Magnetic Memory Testing. Advances in Condition Monitoring and Structural Health Monitoring: WCCM 2019, Springer.

3. Fischer, S.C.L., Hillen, L., and Eberl, C. (2020). Mechanical Metamaterials on the Way from Laboratory Scale to Industrial Applications: Challenges for Characterization and Scalability. Materials, 13.

4. Automated Defect Detection From Ultrasonic Images Using Deep Learning;Medak;IEEE Trans. Ultrason. Ferroelectr. Freq. Control,2021

5. A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data;Meraj;PeerJ Comput. Sci.,2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3