Comparative study of the detectability of fatigue cracks in metals.
2025 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE credits
Student thesis
Abstract [en]
Fatigue cracks are a critical concern in engineering, threatening the safety and reliability of metallic components across various industries. Detecting these cracks early and accurately is essential to prevent failures and optimize maintenance strategies. This thesis presents a comparative study of different Non-Destructive Testing (NDT) methods to assess their effectiveness in identifying fatigue-induced defects in metals. Through structured analysis and a synthesis of experimental results from literature and expert interviews, we evaluated the sensitivity, practical applicability, and limitations of key NDT techniques, including Ultrasonic Testing (UT), Eddy Current Testing (ECT), and Acoustic Emission (AE). Our investigation considered variables such as material type, crack orientation, and operational conditions. The results reveal the strengths and weaknesses of each method, offering practical guidance for selecting appropriate NDT strategies in diverse engineering contexts. In particular, the complementary use of UT and AE showed promise for both detection accuracy and continuous monitoring. This study contributes to the broader understanding of fatigue crack detection and supports the development of more robust maintenance and safety protocols. The insights provided are intended to aid engineers, researchers, and industry professionals in choosing the most suitable NDT approaches for fatigue-sensitive applications.
Place, publisher, year, edition, pages
2025. , p. 48
Keywords [en]
Fatigue Cracks Non-Destructive Testing (NDT) Ultrasonic Testing (UT) Eddy Current Testing (ECT) Acoustic Emission (AE) Phased Array Ultrasonic Testing (PAUT) Time-of-Flight Diffraction (TOFD), Digital Radiography (DR), Computed Tomography (CT), Thermographic Testing (TT), Crack Initiation, Crack Propagation, Microstructure, Signal-to-Noise Ratio (SNR), Probability of Detection (PoD), Detection Accuracy (DA)
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hh:diva-56526OAI: oai:DiVA.org:hh-56526DiVA, id: diva2:1972241
Subject / course
Mechanical Engineering
Educational program
Master's Programme in Mechanical Engineering, 60 credits
Presentation
2025-05-20, 13:39 (English)
Supervisors
Examiners
2025-06-182025-06-182025-10-01Bibliographically approved