Study on Turning Parameter Optimization of Austenitic Stainless Steel

2010 ◽  
Vol 34-35 ◽  
pp. 1829-1833 ◽  
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.

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Research on the Cutting Fluid Effect on Cutting Force and Surface Roughness in Rotor Milling

2012 ◽  
Vol 723 ◽  
pp. 50-55
Author(s):  
Jian Lu Wang ◽  
Liang Liang Wu ◽  
Jun Zhang ◽  
Wan Hua Zhao ◽  
Yi Fei Jiang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Design Method ◽  
Uniform Design ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Fluid ◽  
Cutting Depth ◽  
Multivariable Regression ◽  
Uniform Design Method

A series of milling experiments with and without cutting fluid, arranged by uniform design method, were carried out on rotor material. The influence of cutting fluid on cutting force and surface roughness was explored and compared for the two kinds of conditions. The associated model was established between cutting force & surface roughness and cutting parameters according to the linear multivariable regression method. The results show that the cutting force deceases with the increase of the cutting speed or with the decrease of the feed per tooth and the cutting depth. Cutting fluid has little effect on cutting force, and for surface roughness, the influence of cutting fluid is uncertain.

Experimental Study on Nano-TiAlN Coated Carbide Tools in Turning Austenitic Stainless Steel

2012 ◽  
Vol 723 ◽  
pp. 247-251
Author(s):  
Hai Dong Yang ◽  
Zhi Ding
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Tool Life ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Dry Cutting ◽  
Processing Efficiency ◽  
Machined Surface ◽  
Tool Wear Mechanism ◽  
Coated Carbide

Austenitic stainless steel has poor cutting performance, especially when the inappropriate choice of tool materials and cutting parameters, cutting tool life will be shortened and the quality of machined surface is poor. In this paper, 0Cr18Ni9 stainless steel dry cutting tests had been done with nano-TiAlN coated carbide blade YGB202, the relationship between tool life and cutting speed, tool wear mechanism had been analyzed. In order to improve the processing efficiency and tool life, process parameters were optimized.

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

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 972 ◽  
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.

scholarly journals Microstructural and Surface Texture Analysis due to Machining in Super Austenitic Stainless Steel

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Mohanad Alabdullah ◽  
A. Polishetty ◽  
G. Littlefair
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Texture Analysis ◽  
Surface Texture ◽  
Cutting Parameters ◽  
Machining Process ◽  
Super Austenitic Stainless Steel ◽  
Surface Texture Analysis ◽  
Machined Surfaces

Inferior surface quality is a significant problem faced by machinist. The purpose of this study is to present a surface texture analysis undertaken as part of machinability assessment of Super Austenitic Stainless Steel alloy-AL6XN. The surface texture analysis includes measuring the surface roughness and investigating the microstructural behaviour of the machined surfaces. Eight milling trials were conducted using combination of cutting parameters under wet machining. An optical profilometer (noncontact) was used to evaluate the surface texture at three positions. The surface texture was represented using the parameter, average surface roughness. Scanning Electron Microscope was utilised to inspect the machined surface microstructure and correlate the microstructure with the surface roughness. Results showed that maximum roughness values recorded at the three positions in the longitudinal direction (perpendicular to the machining grooves) were 1.21 μm (trial 1), 1.63 μm (trial 6), and 1.68 μm (trial 7), respectively, whereas the roughness values were greatly reduced in the lateral direction. Also, results showed that the feed rate parameter significantly influences the roughness values compared to the other cutting parameters. The microstructure of the machined surfaces was distorted by the existence of cracks, deformed edges, and bands and wear deposition due to machining process.

Statistical Analysis of Cutting Forces in Turning Inconel 718 with Coated Carbide Tools

2010 ◽  
Vol 135 ◽  
pp. 96-101 ◽  
Author(s):  
Xiao Li Zhu ◽  
Song Zhang ◽  
Tong Chao Ding ◽  
Yuan Wei Wang
Keyword(s):  
Cutting Forces ◽  
Inconel 718 ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Regression Equations ◽  
Dry Cutting ◽  
Cutting Force Components ◽  
Optimal Cutting Parameters ◽  
Coated Carbide

The experimental study presented in this paper aims to investigate the effects of cutting parameters on cutting forces, and search the optimal cutting parameters for the minimum cutting forces during turning Inconel 718 under dry cutting conditions. Based on Taguchi method, a L25 (53) array was designed to conduct the turning experiments. The experimental results indicate that the best condition for the minimum cutting force components is the combination of 45m/min cutting speed, 0.08mm/r feed rate, and 0.2mm depth of cut. The effects of the cutting parameters on cutting forces are investigated while employing the analysis of variance (ANOVA). Finally, the quadratic regression equations for cutting forces were formulated, which can well describe the relationship between cutting parameters and cutting forces.

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.

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