Numerical computation of crack growth of Low Cycle Fatigue in the 304L austenitic stainless steel

Ammar, Ons; Haddar, Nader; Rémy, Luc

doi:10.1016/j.engfracmech.2014.03.013

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…Recently there has been an enormous amount of research addressing the improvement of the mechanical properties of austenitic stainless steel [ 4 , 5 , 6 , 7 , 8 ] without lowering corrosion resistance [ 9 , 10 , 11 , 12 ]. Many experimental studies have focused on AISI 304 and AISI 304L at elevated temperatures [ 13 , 14 , 15 ], under thermo-mechanical and cycle fatigue conditions [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ], under creep conditions [ 23 ] and ductility loss of hydrogen-charged steel [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of Size on the Microstructure and Mechanical Properties of an AISI 304L Stainless Steel—A Comparison between Bulk and Fibers

et al. 2015

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In this work, the mechanical properties and microstructural features of an AISI 304L stainless steel in two presentations, bulk and fibers, were systematically studied in order to establish the relationship among microstructure, mechanical properties, manufacturing process and effect on sample size. The microstructure was analyzed by XRD, SEM and TEM techniques. The strength, Young’s modulus and elongation of the samples were determined by tensile tests, while the hardness was measured by Vickers microhardness and nanoindentation tests. The materials have been observed to possess different mechanical and microstructural properties, which are compared and discussed.

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Section: Introductionmentioning

confidence: 99%

Influence of Size on the Microstructure and Mechanical Properties of an AISI 304L Stainless Steel—A Comparison between Bulk and Fibers

et al. 2015

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“…The crack is thus propagated across the entire thickness of the specimen using a node-release technique on the negative y -surface of the model. 60–63 This was found to be a computationally inexpensive approximation and will be validated in a later section. Figure 8 shows the movement of the stress concentration past the particle for one example analysis.…”

Section: Computational Modeling Of Sensory Particle Responsementioning

confidence: 83%

Shape memory alloy sensory particles for damage detection: Experiments, analysis, and design studies

Bielefeldt

Hochhalter

Hartl

2017

Structural Health Monitoring

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Developing novel techniques for monitoring structural integrity has become an important area of research in the aerospace community. One new technique exploits the stress-induced phase transformation behavior in shape memory alloy particles embedded in a structure. By monitoring changes in the mechanical and/or electromagnetic behavior of such particles, the formation or propagation of fatigue cracks in the vicinity of these particles can be detected. This work demonstrates sensory particle response to local structural damage using finite element modeling for the first time. Using an optimization method to minimize the difference between experimentally measured strain and simulated results, a good approximation of sensory particle properties can be determined and the strong sensory response of the transforming particle demonstrated. To illustrate an application of this method, a multi-scale finite element model of sensory particles embedded in the root rib of an aircraft wing is then considered. In particular, this unique model utilizes substructure modeling to maintain computational efficiency while relating globally applied loads to local structural response, allowing for the consideration of predicted particle response to crack propagation during wing loading. The effect of particle position relative to the crack tip on particle sensory response is assessed. Finally, this work demonstrates how sensory particles can be used to approximate the location of structural damage by interpolating a stress field based on the responses of multiple sensory particles in the vicinity of a propagating crack.

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“…The model (Equation (3)) shows that surface hardness increased with an increase in surface roughness and wall thickness [26,27]. Furthermore, the square of surface roughness, R a1 , and immersion time provided a good indication that as-machined surface roughness was a major factor Metals 2017, 7, 191 9 of 13 in R a2 changes.…”

Section: Effect Of As-machined Surface Roughness Wall Thickness and mentioning

confidence: 99%

Experimental Investigation of Thermal Fatigue Die Casting Dies by Using Response Surface Modelling

Abdulhadi

Ahmad

Ismail

et al. 2017

Metals

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Mechanical and thermal sequences impact largely on thermo-mechanical fatigue of dies in a die casting operations. Innovative techniques to optimize the thermo-mechanical conditions of samples are major focus of researchers. This study investigates the typical thermal fatigue in die steel. Die surface initiation and crack propagation were stimulated by thermal and hardness gradients, acting on the contact surface layer. A design of experiments (DOE) was developed to analyze the effect of as-machined surface roughness and die casting parameters on thermal fatigue properties. The experimental data were assessed on a thermo-mechanical fatigue life assessment model, being assisted by response surface methodology (RSM). The eminent valuation was grounded on the crack length, hardness properties and surface roughness due to thermal fatigue. The results were analyzed using analysis of variance method. Parameter optimization was conducted using response surface methodology (RSM). Based on the model, the optimal results of 26.5 µm crack length, 3.114 µm surface roughness, and 306 HV 0.5 hardness properties were produced.

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Numerical computation of crack growth of Low Cycle Fatigue in the 304L austenitic stainless steel

Cited by 8 publications

References 31 publications

Influence of Size on the Microstructure and Mechanical Properties of an AISI 304L Stainless Steel—A Comparison between Bulk and Fibers

Influence of Size on the Microstructure and Mechanical Properties of an AISI 304L Stainless Steel—A Comparison between Bulk and Fibers

Shape memory alloy sensory particles for damage detection: Experiments, analysis, and design studies

Experimental Investigation of Thermal Fatigue Die Casting Dies by Using Response Surface Modelling

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