Surface Roughness and Temperature in Dry Milling of an Austenitic Stainless Steel

The effects of cyclic pre-strain on low cycle fatigue properties of austenitic stainless steel were investigated, and the fatigue damage was assessed based on several parameters such as the full width at half maximum (FWHM) of diffracted X-ray profile and surface roughness of specimens. The strain-controlled tests were conducted under strain ratio Rε = −1 and various constant total strain ranges. Also the change in remnant fatigue lives were investigated when the cyclic pre-strain were applied to the specimens under the different number of cycles which were determined with reference to the usage factor UFpre ranged from 0.2 to 0.8. As a result, the remnant fatigue life of the pre-strained samples became shorter than that of the sample without pre-strain as the UFpre increased. The relationship between the pre-strain damage expressed in UFpre and the remnant fatigue damage in UFpost was roughly described by the cumulative linear damage law: UFpre + UFpost = 1. Namely, the cyclic pre-strain affected the remnant fatigue lives. In order to evaluate the effects of cyclic pre-strain on fatigue lives more precisely, the damage in the cyclic pre-straining processes was estimated by using FWHM and surface roughness. The FWHM of the specimens with pre-strain once decreased with increase in UFpre, and then increased after showing a minimum value. The surface roughness of specimens increased linearly with an increase of the number of pre-straining cycles. These results suggested that the damage due to pre-strain can be assessed by means of FWHM and surface roughness of specimens.

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Determining the influence of cutting fluids on tool wear and surface roughness during turning of AISI 304 austenitic stainless steel

Journal of Materials Processing Technology ◽

10.1016/j.jmatprotec.2008.02.068 ◽

2009 ◽

Vol 209 (2) ◽

pp. 900-909 ◽

Cited By ~ 214

Author(s):

M. Anthony Xavior ◽

M. Adithan

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Austenitic Stainless Steel ◽

Tool Wear ◽

Cutting Fluids ◽

Aisi 304 ◽

304 Austenitic Stainless Steel

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Concurrent multi-response optimization of austenitic stainless steel surface roughness driven by embedded lean and green indicators

Journal of Cleaner Production ◽

10.1016/j.jclepro.2014.05.074 ◽

2014 ◽

Vol 85 ◽

pp. 293-305 ◽

Cited By ~ 14

Author(s):

George J. Besseris ◽

Alexandros T. Kremmydas

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Austenitic Stainless Steel ◽

Steel Surface ◽

Stainless Steel Surface ◽

Lean And Green ◽

Response Optimization

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Changes in Surface Roughness during Low Cycle Fatigue Process of Austenitic Stainless Steel

Journal of Solid Mechanics and Materials Engineering ◽

10.1299/jmmp.7.372 ◽

2013 ◽

Vol 7 (3) ◽

pp. 372-380 ◽

Cited By ~ 3

Author(s):

Nao FUJIMURA ◽

Takashi NAKAMURA ◽

Hiroyuki OGUMA

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Austenitic Stainless Steel ◽

Low Cycle Fatigue ◽

Cycle Fatigue ◽

Fatigue Process

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Tool Wear, Surface Topography, and Multi-Objective Optimization of Cutting Parameters during Machining AISI 304 Austenitic Stainless Steel Flange

Metals ◽

10.3390/met9090972 ◽

2019 ◽

Vol 9 (9) ◽

pp. 972 ◽

Cited By ~ 2

Author(s):

Xiaojun Li ◽

Zhanqiang Liu ◽

Xiaoliang Liang

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Austenitic Stainless Steel ◽

Tool Wear ◽

Surface Defects ◽

Cutting Speed ◽

Cutting Parameters ◽

Aisi 304 ◽

304 Austenitic Stainless Steel ◽

Optimization Of Cutting Parameters

The application of AISI 304 austenitic stainless steel in various industrial fields has been greatly increased, but poor machinability classifies AISI 304 as a difficult-to-cut material. This study investigated the tool wear, surface topography, and optimization of cutting parameters during the machining of an AISI 304 flange component. The machining features of the AISI 304 flange included both cylindrical and end-face surfaces. Experimental results indicated that an increased cutting speed or feed aggravated tool wear and affected the machined surface roughness and surface defects simultaneously. The generation and distribution of surface defects was random. Tearing surface was the major defect in cylinder turning, while side flow was more severe in face turning. The response surface method (RSM) was applied to explore the influence of cutting parameters (e.g., cutting speed, feed, and depth of cut) on surface roughness, material removal rate (MRR), and specific cutting energy (SCE). The quadratic model of each response variable was proposed by analyzing the experimental data. The optimization of the cutting parameters was performed with a surface roughness less than the required value, the maximum MRR, and the minimum SCE as the objective. It was found that the desirable cutting parameters were v = 120 m/min, f = 0.18 mm/rev, and ap = 0.42 mm for the AISI 304 flange to be machined.

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ANALYSIS ON MICROSTRUCTURE, HARDNESS AND SURFACE ROUGHNESS OF SHOT BLASTED-PASTE BORONIZED 316 AUSTENITIC STAINLESS STEEL

Jurnal Teknologi ◽

10.11113/jt.v76.5515 ◽

2015 ◽

Vol 76 (3) ◽

Author(s):

Muhamad Hafizuddin Mohamad Basir ◽

Bulan Abdullah ◽

Siti Khadijah Alias ◽

Muhammad Hafizuddin Jumadin ◽

Muhammad Hussain Ismail

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Austenitic Stainless Steel ◽

Optical Microscope ◽

Boride Layer ◽

Surface Metrology ◽

Boron Diffusion ◽

Shot Blasting ◽

Heavy Load ◽

Hardness Tester

In this research, analysis on microstructure, hardness and surface roughness of 316 austenitic stainless steel were conducted before and after boronizing process. Boronizing treatment was conducted using a paste medium at a temperature of 8500C, with and without shot blasting. Microstructures of the specimens were observed under Olympus BX60 Optical Microscope. Vickers Micro Hardness Tester was used to determine the hardness of the specimens while Optical 3D Surface Metrology Sys was used to measure the surface roughness of the specimens. The process of boronizing diffuses boron into the surface of steel which resulted in the formation of the boride layers that consist of FeB and Fe2B. Shot blasting process increased the boron diffusion which resulted in increment of the boride layer thickness and hardness value while the surface roughness was fluctuated. Increment in the hardness value of 316 stainless steel causes the steel to be able to withstand a heavy load.

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Green machining for the dry milling process of stainless steel 304

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405419888126 ◽

2019 ◽

Vol 234 (5) ◽

pp. 881-899 ◽

Cited By ~ 7

Author(s):

Trung-Thanh Nguyen ◽

Mozammel Mia ◽

Xuan-Phuong Dang ◽

Chi-Hieu Le ◽

Michael S Packianather

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Energy Consumption ◽

Power Factor ◽

Feed Rate ◽

Cutting Speed ◽

Depth Of Cut ◽

Dry Machining ◽

Dry Milling ◽

Stainless Steel 304

Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This article presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed ( V), depth of cut ( a), feed rate ( f), and nose radius ( r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the Grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85% and 57.65%, respectively, and the power factor improves around 28.83%, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices.

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Study on Turning Parameter Optimization of Austenitic Stainless Steel

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.1829 ◽

2010 ◽

Vol 34-35 ◽

pp. 1829-1833 ◽

Cited By ~ 2

Author(s):

Deng Wan Li ◽

Hong Tao Chen ◽

Ming Heng Xu ◽

Cheng Ming Zhong

Keyword(s):

Surface Roughness ◽

Stainless Steel ◽

Austenitic Stainless Steel ◽

Cutting Forces ◽

Design Method ◽

Uniform Design ◽

Cutting Parameters ◽

Dry Cutting ◽

Machine Material ◽

Optimal Cutting Parameters

In order to explore the cutting rule of hard-to-machine material austenitic stainless steel and to optimize cutting parameters, multiple sets of parameters of austenitic stainless steel cutting were schemed out by using uniform design method. Test cutting researches of cutting forces, surface roughness and cutting efficiency with these parameters were conducted under the condition of dry cutting. On this basis, multi-objective optimization model of cutting force and surface roughness applied to austenitic stainless steel had been set up by multiple regression analysis. Variance analysis showed that these formulas have highly significant linear relationship. Verification test is done under the optimal cutting parameters, and the results of cutting forces and surface roughness are in good agreement with the calculated. Turning efficiency is improved by 23.4%, compared with the actual cutting parameters of past production.

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