Proposal of Surface Finish Factor on Fatigue Strength in Design Fatigue Curve

2014 ◽  
Author(s):  
Yuichi Fukuta ◽  
Hiroshi Kanasaki ◽  
Seiji Asada ◽  
Takehiko Sera
Keyword(s):  
Surface Roughness ◽  
Tensile Strength ◽  
Stainless Steel ◽  
Fatigue Strength ◽  
Surface Finish ◽  
Fatigue Curve ◽  
Low Alloy Steels ◽  
Mean Stress ◽  
Alloy Steels ◽  
Surface Finish Effect

The published papers related to the effects of surface finish on fatigue strength are reviewed in order to formulate its factor in the design fatigue curve in air environment. Firstly, some of regulations and literatures were examined to verify the surface finish effect on fatigue strength and formulation of that in design fatigue curve. The fatigue strength of carbon and low alloy steels is decreased with an increase of its surface roughness and tensile strength but that of stainless steel is not decreased except for special conditions. After screening the data of carbon and low alloy steels, a surface finish factor is formulated with these data which is a function of tensile strength, surface roughness and mean stress.

Study on Mean Stress Effects for Design Fatigue Curves

2016 ◽  
Author(s):  
Seiji Asada ◽  
Takeshi Ogawa ◽  
Makoto Higuchi ◽  
Hiroshi Kanasaki ◽  
Yasukazu Takada
Keyword(s):  
Evaluation Method ◽  
Austenitic Stainless Steels ◽  
Fatigue Curve ◽  
Carbon Steels ◽  
Low Alloy Steels ◽  
Mean Stress ◽  
Research Committee ◽  
Stress Effects ◽  
Alloy Steels ◽  
Design Factors

In order to develop new design fatigue curves for austenitic stainless steels, carbon steels and low alloy steels and a new design fatigue evaluation method that are rational and have a clear design basis, the Design Fatigue Curve (DFC) subcommittee was established in the Atomic Energy Research Committee in the Japan Welding Engineering Society. Mean stress effects for design fatigue curves are to be considered in the development of design fatigue curves. The Modified Goodman approach for mean stress effects is used in the design fatigue curves of the ASME B&PV Code. Tentative design fatigue curves were developed and studies on the effect of mean stress and design factors are on-going. Development of design fatigue curves, effect of mean stress and design factors is needed to establish a new fatigue design evaluation method. The DFC subcommittee has studied correction approaches for mean stress effects and the approaches of modified Goodman, Gerber, Peterson and Smith-Watson-Topper were compared using test data in literature. An appropriate approach for mean stress effects are discussed in this paper.

Proposal for the Implementation of Elevated Temperature Design Fatigue Curve for 2-1/4Cr-1Mo-V and 3Cr-1Mo-V Steels

2004 ◽  
Author(s):  
Tatsumi Takehana ◽  
Takeru Sano ◽  
Susumu Terada ◽  
Hideo Kobayashi
Keyword(s):  
Elevated Temperature ◽  
Fatigue Strength ◽  
High Strength ◽  
Fatigue Curve ◽  
Temperature Limit ◽  
Fatigue Tests ◽  
Low Alloy Steels ◽  
Alloy Steels ◽  
Elevated Temperature Design

2-1/4Cr-1Mo-V and 3Cr-1Mo-V steels have been used extensively as materials for elevated temperature and high-pressure hydro-processing reactors. These steels have both of high strength at elevated temperature and high resistance against elevated temperature hydrogen attack due to the addition of vanadium. The operating temperature of these reactors is between 800 and 900deg.F. The fatigue evaluations of these reactors per ASME Sec. VIII Div.2 and Div.3 can’t be performed in spite of demand for fatigue analysis because the temperature limit of design fatigue curve in ASME Sec. VIII Div.2 and Div.3 for carbon and low alloy steels is 700deg.F. Results of load and strain controlled fatigue tests conducted over the temperature range from room temperature to 932deg.F (500deg.C) are reported for 2-1/4Cr-1Mo-V and 3Cr-1Mo-V steels. These data were compared with data for 2-1/4Cr-1Mo steels available from the literatures. The fatigue strength for a 2-1/4Cr-1Mo-V steel in high cycle region is higher than that for 2-1/4Cr-1Mo steels and in low cycle region is lower. The fatigue strength for a 3Cr-1Mo-V steel is almost same as that for 2-1/4Cr-1Mo-V steels. Therefore an elevated temperature design fatigue curve for 2-1/4Cr-1Mo-V and 3Cr-1Mo-V steels is newly proposed. It is found from the case study that the different fatigue life can be predicted by using different mean stress correction procedure.

Probabilistic Parameter Sensitivity Study on Design Fatigue Curve for Carbon and Low-Alloy Steels

2012 ◽  
Author(s):  
Masahiro Takanashi ◽  
Jyunki Maeda ◽  
Shinsuke Sakai
Keyword(s):  
Surface Roughness ◽  
Size Effect ◽  
Limit State ◽  
Fatigue Curve ◽  
Sensitivity Study ◽  
Parameter Sensitivity ◽  
Low Alloy Steels ◽  
Alloy Steels ◽  
Partial Safety Factors ◽  
State Functions

In a previous paper[1], we discussed the margins of the design fatigue curve in ASME Boiler and Pressure Vessel Codes Section III using a probabilistic approach. Limit state functions of the design fatigue curve for carbon and low-alloy steels were proposed in order to investigate the physical implication of the design margin. In the limit state functions, four parameters, namely, fatigue data scatter, size effect, surface roughness, and applied stress were taken into consideration as random variables. Based on the limit state functions, reliability index contours of the design fatigue curve were obtained together with the partial safety factors (PSFs). Among these partial safety factors, the fatigue strength (or life) and the applied stress were predominant. The other parameters, the PSFs for the size effect and the surface roughness were small enough and almost constant. The parameter sensitivity, however, remains unknown and whether these parameters should be treated as constants or as random variables is also not clear. In this study, a probabilistic parameter sensitivity study on the design fatigue curve was conducted to discuss how the design fatigue curve should be. Two sensitivities were computed. One is the rate sensitivity indicating the contribution of the mean value of the parameter in the probability of failure. The other is the probabilistic sensitivity to parameter dispersion. The parameters for the size effect and the surface roughness showed lower probabilistic sensitivities. This result suggests that the parameters can be considered as constants. In general, a higher number of parameters in a probabilistic model leads to more uncertainty and the design concept tends to be more conservative. We, therefore, proposed to deal with the parameters of the size effect and the surface roughness separately in the design fatigue curve to eliminate conservativeness.

scholarly journals Influence of Activated Fluxes on the Bead Shape of A-TIG Welds on Carbon and Low-Alloy Steels in Comparison with Stainless Steel AISI 304L

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 530
Author(s):  
Jerzy Niagaj
Keyword(s):  
Stainless Steel ◽  
Penetration Depth ◽  
High Strength ◽  
Tig Welding ◽  
Low Alloy Steels ◽  
Aisi 304L ◽  
A Tig Welding ◽  
Alloy Steels ◽  
Steel Aisi ◽  
Stainless Steel Aisi

The article presents results of comparative A-TIG welding tests involving selected unalloyed and fine-grained steels, as well as high-strength steel WELDOX 1300 and austenitic stainless steel AISI 304L. The tests involved the use of single ingredient activated fluxes (Cr2O3, TiO2, SiO2, Fe2O3, NaF, and AlF3). In cases of carbon and low-alloy steels, the tests revealed that the greatest increase in penetration depth was observed in the steels which had been well deoxidized and purified during their production in steelworks. The tests revealed that among the activated fluxes, the TiO2 and SiO2 oxides always led to an increase in penetration depth during A-TIG welding, regardless of the type and grade of steel. The degree of the aforesaid increase was restricted within the range of 30% to more than 200%.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

Additive manufacturing using fine wire-based laser metal deposition

2019 ◽  
Vol 26 (3) ◽  
pp. 473-483
Author(s):  
Muhammad Omar Shaikh ◽  
Ching-Chia Chen ◽  
Hua-Cheng Chiang ◽  
Ji-Rong Chen ◽  
Yi-Chin Chou ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tensile Strength ◽  
Additive Manufacturing ◽  
High Precision ◽  
Surface Finish ◽  
Dimensional Accuracy ◽  
Laser Metal Deposition ◽  
Cross Sectional ◽  
Content Type ◽  
Fine Wire

Purpose Using wire as feedstock has several advantages for additive manufacturing (AM) of metal components, which include high deposition rates, efficient material use and low material costs. While the feasibility of wire-feed AM has been demonstrated, the accuracy and surface finish of the produced parts is generally lower than those obtained using powder-bed/-feed AM. The purpose of this study was to develop and investigate the feasibility of a fine wire-based laser metal deposition (FW-LMD) process for producing high-precision metal components with improved resolution, dimensional accuracy and surface finish. Design/methodology/approach The proposed FW-LMD AM process uses a fine stainless steel wire with a diameter of 100 µm as the additive material and a pulsed Nd:YAG laser as the heat source. The pulsed laser beam generates a melt pool on the substrate into which the fine wire is fed, and upon moving the X–Y stage, a single-pass weld bead is created during solidification that can be laterally and vertically stacked to create a 3D metal component. Process parameters including laser power, pulse duration and stage speed were optimized for the single-pass weld bead. The effect of lateral overlap was studied to ensure low surface roughness of the first layer onto which subsequent layers can be deposited. Multi-layer deposition was also performed and the resulting cross-sectional morphology, microhardness, phase formation, grain growth and tensile strength have been investigated. Findings An optimized lateral overlap of about 60-70% results in an average surface roughness of 8-16 µm along all printed directions of the X–Y stage. The single-layer thickness and dimensional accuracy of the proposed FW-LMD process was about 40-80 µm and ±30 µm, respectively. A dense cross-sectional morphology was observed for the multilayer stacking without any visible voids, pores or defects present between the layers. X-ray diffraction confirmed a majority austenite phase with small ferrite phase formation that occurs at the junction of the vertically stacked beads, as confirmed by the electron backscatter diffraction (EBSD) analysis. Tensile tests were performed and an ultimate tensile strength of about 700-750 MPa was observed for all samples. Furthermore, multilayer printing of different shapes with improved surface finish and thin-walled and inclined metal structures with a minimum achievable resolution of about 500 µm was presented. Originality/value To the best of the authors’ knowledge, this is the first study to report a directed energy deposition process using a fine metal wire with a diameter of 100 µm and can be a possible solution to improving surface finish and reducing the “stair-stepping” effect that is generally observed for wires with a larger diameter. The AM process proposed in this study can be an attractive alternative for 3D printing of high-precision metal components and can find application for rapid prototyping in a range of industries such as medical and automotive, among others.

AK STEEL 409 Ni

Alloy Digest ◽  
2021 ◽  
Vol 70 (6) ◽  
Keyword(s):  
Stainless Steel ◽  
Corrosion Resistance ◽  
Physical Properties ◽  
Cost Effective ◽  
Heat Treating ◽  
Low Alloy Steels ◽  
Service Temperature ◽  
Alloy Steels ◽  
Cost Effective Alternative ◽  
Maximum Service Temperature

Abstract AK Steel 409 Ni is a 11% chromium ferritic stainless steel microalloyed with titanium and nickel. It provides excellent weldability, toughness, and fabricating characteristics superior to those of type 409 stainless steel in thicknesses over 3.05 mm (0.120 in.). This alloy is a cost effective alternative to mild steels and low-alloy steels that also provides superior corrosion and/or oxidation resistance. The recommended maximum service temperature of AK Steel 409 Ni is 730 °C (1350 °F). This datasheet provides information on composition, physical properties, elasticity, and tensile properties. It also includes information on corrosion resistance as well as heat treating and joining. Filing Code: SS-1336. Producer or source: AK Steel Corporation.

Proposal of Fatigue Life Equations for Carbon and Low-Alloy Steels and Austenitic Stainless Steels as a Function of Tensile Strength

2013 ◽  
Author(s):  
Hiroshi Kanasaki ◽  
Makoto Higuchi ◽  
Seiji Asada ◽  
Munehiro Yasuda ◽  
Takehiko Sera
Keyword(s):  
Tensile Strength ◽  
Fatigue Life ◽  
Stainless Steels ◽  
Room Temperature ◽  
Austenitic Stainless Steels ◽  
Low Alloy Steels ◽  
Strength Based ◽  
Fatigue Data ◽  
Current Design ◽  
Alloy Steels

Fatigue life equations for carbon & low-alloy steels and also austenitic stainless steels are proposed as a function of their tensile strength based on large number of fatigue data tested in air at RT to high temperature. The proposed equations give a very good estimation of fatigue life for the steels of varying tensile strength. These results indicate that the current design fatigue curves may be overly conservative at the tensile strength level of 550 MPa for carbon & low-alloy steels. As for austenitic stainless steels, the proposed fatigue life equation is applicable at room temperature to 430 °C and gives more accurate prediction compared to the previously proposed equation which is not function of temperature and tensile strength.

Data Mining for Estimating Fatigue Strength Based on Composition and Process Parameters

2019 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake
Keyword(s):  
Data Mining ◽  
Fatigue Strength ◽  
Process Parameters ◽  
Fatigue Testing ◽  
Absolute Error ◽  
Processing Parameters ◽  
Coefficient Of Determination ◽  
Low Alloy Steels ◽  
Strength Based ◽  
Alloy Steels

Abstract Accurately estimating the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and often fatal consequences of fatigue failures. The main objective of this manuscript is to perform data mining on the fatigue dataset for steel available from the National Institute of Material Science (NIMS) MatNavi. The cross-industry process for data mining (CRISP-DM) approach was followed in the paper, in order to gain meaningful insights from the dataset and to estimate the fatigue strength of carbon and low alloy steels, using composition and processing parameters. Of the six steps of the CRISP-DM approach, special emphasis has been placed on steps 2 to 5 (i.e. data understanding, data preparation, modeling and evaluation). In step 4 (i.e. modeling), a range of machine learning (parametric and non-parametric) is explored to predict the fatigue strength, based on the composition and process parameters. Various algorithms were trained and tested on the dataset and finally evaluated, using metrics such as root mean square error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2) and Explained Variance Score (EVS).

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