Strain rate dependence of impact properties of sintered 316L stainless steel

Lee, Woei-Shyan; Lin, Cheng Feng; Liu, Tsung-Ju

doi:10.1016/j.jnucmat.2006.09.003

Cited by 41 publications

(19 citation statements)

References 19 publications

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“…The microstructure on the fractured surfaces of the "O1" specimens with a cup-cone shape and the "D2" specimens with a flat shape were analyzed with different magnifications using a scanning electron microscope (SEM) type JSM-7610FPlus Schottky Field Emission made by JOEL ltd. (Tokyo, Japan). SEM analysis assists in determining the fracture mode for both tool steels [25]. The existence of the parabolic dimple-like structures in the SEM images revealed that both tool steels had a ductile fracture mode (Figure 9).…”

Section: Microstructurementioning

confidence: 99%

Mechanical Properties and Microstructure Characterization of AISI “D2” and “O1” Cold Work Tool Steels

Algarni

2019

Metals

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This research analyzes the mechanical properties and fracture behavior of two cold work tool steels: AISI “D2” and “O1”. Tool steels are an economical and efficient solution for manufacturers due to their superior mechanical properties. Demand for tool steels is increasing yearly due to the growth in transportation production around the world. Nevertheless, AISI “D2” and “O1” (locally made) tool steels behave differently due to the varying content of their alloying elements. There is also a lack of information regarding their mechanical properties and behavior. Therefore, this study aimed to investigate the plasticity and ductile fracture behavior of “D2” and “O1” via several experimental tests. The tool steels’ behavior under monotonic quasi-static tensile and compression tests was analyzed. The results of the experimental work showed different plasticity behavior and ductile fracture among the two tool steels. Before fracture, clear necking appeared on “O1” tool steel, whereas no signs of necking occurred on “D2” tool steel. In addition, the fracture surface of “O1” tool steel showed cup–cone fracture mode, and “D2” tool steel showed a flat surface fracture mode. The dimple-like structures in scanning electron microscope (SEM) images revealed that both tool steels had a ductile fracture mode.

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Section: Microstructurementioning

confidence: 99%

Mechanical Properties and Microstructure Characterization of AISI “D2” and “O1” Cold Work Tool Steels

Algarni

2019

Metals

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“…Density The ductile damage model available in ABAQUS/Explicit was used to model material separation during the machining simulation [34][35][36]. The Coulomb friction model (µ = 0.2), commonly used in machining simulations, was used in the present work [37].…”

Section: Property Formulamentioning

confidence: 99%

2D Finite Element Modeling of the Cutting Force in Peripheral Milling of Cellular Metals

et al. 2020

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The machining of cellular metals has been a challenge, as the resulting surface is extremely irregular, with torn off or smeared material, poor accuracy, and subsurface damage. Although cutting experiments have been carried out on cellular materials to study the influence of cutting parameters, current analytical and experimental techniques are not suitable for the analysis of heterogeneous materials. On the other hand, the finite element (FE) method has been proven a useful resource in the analysis of heterogeneous materials, such as cellular materials, metal foams, and composites. In this study, a two-dimensional finite element model of peripheral milling for cellular metals is presented. The model considers the kinematics of peripheral milling, depicting the advance of the tool into the workpiece and the interaction between the cutting edge and the mesostructure. The model is able to simulate chip separation as well as the surface and subsurface damage on the machined surface. Although the calculated average cutting force is not accurate, the model provides a reasonable estimation of maximum cutting force. The influences of mesostructure on cutting processes are highlighted and the effects in peripheral milling of cellular materials are discussed.

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“…To date, a number of studies have attempted to model the flow behavior of austenite stainless steels through various deformation experiments [ 3 , 8 , 9 , 10 , 11 , 12 ]. For example, Haj et al [ 8 ] carried out hot compression tests for 321 stainless steel at temperatures ranging from 950 to 1100 °C and strain rates ranging from 0.01 s −1 to 1 s −1 .…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Constitutive Equation, Neural Networks, and Support Vector Regression for Modeling Hot Deformation of 316L Stainless Steel

Song

2020

Materials

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In this research, hot deformation experiments of 316L stainless steel were carried out at a temperature range of 800–1000 °C and strain rate of 2 × 10−3–2 × 10−1. The flow stress behavior of 316L stainless steel was found to be highly dependent on the strain rate and temperature. After the experimental study, the flow stress was modeled using the Arrhenius-type constitutive equation, a neural network approach, and the support vector regression algorithm. The present research mainly focused on a comparative study of three algorithms for modeling the characteristics of hot deformation. The results indicated that the neural network approach and the support vector regression algorithm could be used to model the flow stress better than the approach of the Arrhenius-type equation. The modeling efficiency of the support vector regression algorithm was also found to be more efficient than the algorithm for neural networks.

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Strain rate dependence of impact properties of sintered 316L stainless steel

Cited by 41 publications

References 19 publications

Mechanical Properties and Microstructure Characterization of AISI “D2” and “O1” Cold Work Tool Steels

Mechanical Properties and Microstructure Characterization of AISI “D2” and “O1” Cold Work Tool Steels

2D Finite Element Modeling of the Cutting Force in Peripheral Milling of Cellular Metals

A Comparison Study of Constitutive Equation, Neural Networks, and Support Vector Regression for Modeling Hot Deformation of 316L Stainless Steel

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