Investigation of the influence of thermal overloads on mechanical properties of S500 high-strength structural steel using electromagnetic testing technology
Source abstract
Abstract
High-strength steels are mostly realized via a very fine-grained microstructure by a thermomechanical manufacturing process. If the temperature of the steels exceeds a critical range, e.g., due to a fire or inappropriate flame straightening, coarse grain formation, and thus local damage in the form of softening of the structure can occur. This results in a lower fatigue strength. Currently, there is no possibility to inspect the mechanical properties directly on the structure after thermal exposure. To investigate the influence of thermal overloads on mechanical properties and the qualification of non-destructive electromagnetic testing technology for the detection of thermal damage, tensile and fatigue specimens of S500G1+M high-strength structural steel were subjected to different heat treatments in a preheated chamber furnace at temperatures of 550, 1000 and 1300 °C. The resulting changes in microstructure, hardness, yield strength, tensile strength, elastic modulus, fatigue strength and electromagnetic signals were compared to the initial state. The combined results show that thermal damage leading to a degradation of the mechanical properties can be detected using harmonic analysis of eddy current signals. Referencing the initial state in the impedance plane of the third harmonic, a deviation can be detected directly on the component or structure using rapid and non-destructive testing, thus indicating a change in the fatigue strength. In addition, the amplitudes of the first and third harmonics show an inverse correlation to hardness, yield strength and tensile strength.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Material Properties and Failure Mechanisms · Microstructure and Mechanical Properties of Steels · Engineering Diagnostics and Reliability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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