27 October 2016 Evaluation of fatigue process zone dimensions in notched specimens by two-step phase shifting interferometry technique
Leonid I. Muravsky, Pascal Picart, Arkady B. Kmet’, Taras I. Voronyak, Orest P. Ostash, Ihor V. Stasyshyn
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Abstract
A method for evaluation of fatigue process zone (FPZ) dimensions near a notch root in metal and alloy specimens by using a two-step phase shifting interferometry (TS PSI) technique is proposed. In comparison with other destructive and nondestructive methods evaluating the FPZ dimensions, it possesses higher accuracy and performance. The method uses a criterion for the FPZ dimensions definition based on an assumption that the surface roughness of notched specimens after cyclic loading reaches its maximum values at the FPZ boundary. To realize this method, first, a phase map (PM) of a total surface relief near a notch root is retrieved; second, roughness and waviness PMs are extracted from the retrieved total surface relief PM by using the TS PSI; and finally, a surface roughness parameter Ra spatial distribution is calculated according to the offered criterion and the FPZ size d* is defined. The FPZ size was measured for specimens made of low-carbon steel and aluminum alloys 2024-Т6 and 7075-T3. Obtained experimental results have shown that the proposed criterion allows defining the FPZ size for notched specimens made of metals and alloys possessing high, moderate, and low plasticity.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2016/$25.00 © 2016 SPIE
Leonid I. Muravsky, Pascal Picart, Arkady B. Kmet’, Taras I. Voronyak, Orest P. Ostash, and Ihor V. Stasyshyn "Evaluation of fatigue process zone dimensions in notched specimens by two-step phase shifting interferometry technique," Optical Engineering 55(10), 104108 (27 October 2016). https://doi.org/10.1117/1.OE.55.10.104108
Published: 27 October 2016
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CITATIONS
Cited by 16 scholarly publications.
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KEYWORDS
Phase modulation

Surface roughness

Phase interferometry

Phase shifts

Aluminum

Metals

Gaussian filters

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