Techanics Experiment Study on PcBN Cutting Tool Dry Turning Austenitic Stainless Steel AISI 301

2011 ◽  
Vol 314-316 ◽  
pp. 1020-1024
Author(s):  
Yun Hai Jia
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Feed Rate ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Austenitic Stainless Steels ◽  
Cutting Parameters ◽  
Numerical Control ◽  
Dry Turning ◽  
Cut Depth

High deformation hardening, low thermal conductivity, high built-up edge tendency of austenitic stainless steels were the main factors that make their machinablity difficult. For determination of the suitable cutting parameters in machining austenitic stainless steel by PcBN cutting tools, the samples which were prepared to be used in the experiment, 300 mm in length and 60 mm in diameter, were dry machined in a numerical control lathe. During experiments, dry turning parameters, such as feed rate, cutting speed and cut depth were investigated. The suitable cutting speed and feed rate were determined according to workpieces surface roughness, cutting tools flank wear. Finally, cutting speed of 160 to 200 m/min, feed rate of 0.06 to 0.08 mm/rev and cut depth of 0.10 mm gave the satisfied results.

Techanics Experiment Study on PcBN Cutting Tool Dry Turning Austenitic Stainless Steel AISI 304

2011 ◽  
Vol 341-342 ◽  
pp. 256-260
Author(s):  
Yun Hai Jia
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Feed Rate ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Aisi 304 ◽  
Dry Turning ◽  
Steel Aisi ◽  
Cut Depth ◽  
Stainless Steel Aisi

High deformation hardening, low thermal conductivity, high built-up edge tendency of austenitic stainless steels were the main factors that make their machinablity difficult. For determination of the suitable cutting parameters in machining austenitic stainless steel AISI 304 by PcBN cutting tools, the samples which were prepared to be used in the experiment, 300 mm in length and 60 mm in diameter, were dry machined in a numerical control lathe. During experiments, dry turning parameters, such as feed rate, cutting speed and cut depth were investigated. The suitable cutting speed and feed rate were determined according to workpieces surface roughness, cutting tools flank wear. Finally, cutting speed of 180 to 200 m/min, feed rate of 0.05 to 0.06 mm/rev and cut depth of 0.10 mm gave the satisfied results.

Techanics Study on PcBN Cutting Tool Dry Turning Chilled Cast Iron

2011 ◽  
Vol 418-420 ◽  
pp. 1342-1345
Author(s):  
Yun Hai Jia ◽  
Zhi Qun Ye ◽  
Hai Zhu Wang ◽  
Hua Wei Jing
Keyword(s):  
Cast Iron ◽  
Feed Rate ◽  
Cutting Tool ◽  
Bending Strength ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Tool Material ◽  
Dry Turning ◽  
Cut Depth ◽  
Chilled Cast Iron

Chilled cast iron is a typical hard and brittle material, often be used to make all kinds of roller. According to chilled cast iron machining characteristics, cutting tool material should has high red hardness, good impact resistance and wear resistance, high bending strength and large thermal conductivity coefficient. For determination of the suitable cutting parameters in machining chilled cast iron by PcBN cutting tools dry turning, the samples which are prepared to be used in the experiment, 200 mm in length and 120 mm in diameter, are machined in lathe. During experiments, cutting tool parameters and dry turning parameters, such as edge chamfer width and angle, feed rate, cutting speed and cut depth are investigated. The suitable edge chamfer width and angle, cutting speed and feed rate are determined according to cutting tool life and cutting tools flank wear. Finally, edge chamfer width of 0.2 mm, edge chamfer angle of -15 degree, cutting speed of 90 m/min, feed rate of 0.15 to 0.2 mm/rev and cut depth of 0.3 mm gave the satisfied results.

Dry turning optimization of austenitic stainless steel 316L based on Taguchi and TOPSIS approaches

2020 ◽  
Vol 108 (4) ◽  
pp. 401
Author(s):  
Youssef Touggui ◽  
Salim Belhadi ◽  
Tarek Mabrouki ◽  
Mustapha Temmar ◽  
Mohamed Athmane Yallese
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Optimal Combination ◽  
Parameter Analysis ◽  
Aisi 316L ◽  
Stainless Steel 316L ◽  
Dry Turning

Austenitic stainless steel (AISI 316L ASS) is known as a very difficult material to cut due to its high toughness, work hardening combined with built-up-edge (BUE) formation and also poor thermal conductivity. In order to improve its machinability, it seems important to carry out experimentation helping to study effects of cutting parameters on process responses. For that both Taguchi and TOPSIS approaches were applied to determine an optimal combination of cutting parameters during dry turning of AISI 316L ASS. Cutting speed (Vc), feed (f), cutting depth (ap) and cutting time (tc) were selected as four input parameters. Flank wear (VB), tangential cutting force (Fz), surface roughness (Ra) and material removal rate (MRR) were considered as the major process responses. Nine cutting tests were carried out based on Taguchi’s L9 orthogonal array. Thus, in order to distinguish the greater significant cutting parameter, Analysis of variance (ANOVA) was applied. Ultimately, in the case of Taguchi approach results show optimal combinations in terms of (Vc, f, ap and tc) for attaining minimum VB, Fz and Ra and also reaching maximization of MRR. In addition, TOPSIS approach was exploited yielding to results that indicate optimal combination of cutting parameters for achieving simultaneously minimum VB, Fz and Ra and maximum MRR.

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.

Statistical Study and Multi-Response Optimization of Cutting Parameters for Dry Turning Stainless Steel AISI 316L Using Cermet Tool

2020 ◽  
Vol 36 ◽  
pp. 28-46
Author(s):  
Youssef Touggui ◽  
Salim Belhadi ◽  
Salah Eddine Mechraoui ◽  
Mohamed Athmane Yallese ◽  
Mustapha Temmar
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 316L ◽  
Cutting Power ◽  
Dry Turning ◽  
Optimization Of Cutting Parameters

Stainless steels have gained much attention to be an alternative solution for many manufacturing industries due to their high mechanical properties and corrosion resistance. However, owing to their high ductility, their low thermal conductivity and high tendency to work hardening, these materials are classed as materials difficult to machine. Therefore, the main aim of the study was to examine the effect of cutting parameters such as cutting speed, feed rate and depth of cut on the response parameters including surface roughness (Ra), tangential cutting force (Fz) and cutting power (Pc) during dry turning of AISI 316L using TiCN-TiN PVD cermet tool. As a methodology, the Taguchi L27 orthogonal array parameter design and response surface methodology (RSM)) have been used. Statistical analysis revealed feed rate affected for surface roughness (79.61%) and depth of cut impacted for tangential cutting force and cutting power (62.12% and 35.68%), respectively. According to optimization analysis based on desirability function (DF), cutting speed of 212.837 m/min, 0.08 mm/rev feed rate and 0.1 mm depth of cut were determined to acquire high machined part quality

Dimensional Deviation Affected by Cutting Parameters and Machine Tool Rigidity in Dry Turning of S45C Steel

2013 ◽  
Vol 315 ◽  
pp. 749-754 ◽  
Author(s):  
M.A. Rahman ◽  
A.B. Baharudin ◽  
S. Adi ◽  
Nur Izan Syahriah Hussein ◽  
H. Isa ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Machine Tool ◽  
Regression Model ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Numerical Control ◽  
Depth Of Cut ◽  
Dry Turning ◽  
Tool Vibration ◽  
Dimensional Deviation

Performance of machining processes is assessed by dimensional and geometrical accuracy which is mentioned in this paper as dimensional deviation. A part quality does not depend solely on the depth of cut, feed rate and cutting speed. Other variable such as excessive machine tool vibration due to insufficient dynamic rigidity can be deleterious to the desired results. The focus of the present study is to find a correlation between dimensional deviation against cutting parameters and machine tool vibration in dry turning. Hence cutting parameters and vibration-based regression model can be established for predicting the part dimensional deviation. Experiments are conducted using a Computerized Numerical Control (CNC) lathe with carbide insert cutting tool. Vibration data are collected through a data acquisition system, then tested and analyzed through statistical analysis. The analysis revealed that machine tool vibration has significant effect on dimensional deviation where statistical analysis of individual regression coefficients showed p<0.05. The developed regression model has been validated through experimental tests and found to be reliable to predict dimensional deviation.

Optimization of Surface Roughness by Using Different Cutting Tools for Aluminum Alloy 6063

2020 ◽  
Vol 17 (2) ◽  
pp. 961-966
Author(s):  
Allina Abdullah ◽  
Afiqah Azman ◽  
B. M. Khirulrizwan
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Optimum Condition ◽  
Feed Rate ◽  
Optimum Parameter ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Numerical Control ◽  
Depth Of Cut

This research outlines an experimental study to determine the optimum parameter of cutting tool for the best surface roughness (Ra) of Aluminum Alloy (AA) 6063. For the experiment in this research, cutting parameters such as cutting speed, depth of cut and feed rate are used to identify the effect of both cutting tools which are tungsten carbide and cermet towards the surface roughness (Ra) of material AA6063. The machining operation involved to cut the material is turning process by using Computer Numerical Control (CNC) Lathe machine. The experimental design was designed by Full Factorial. The experiment that had been conducted by the researcher is 33 with 2 replications. The total number of the experiments that had been run is 54 runs for each cutting tool. Thus, the total number of experiments for both cutting tools is 108 runs. ANOVA analysis had been analyzed to identify the significant factor that affect the Ra result. The significant factors that affect the Ra result of AA6063 are feed rate and cutting speed. The researcher used main effect plot to determine the factor that most influenced the surface roughness of AA6063, the optimum condition of surface roughness and the optimum parameter of cutting tool. The factor that most influenced the surface roughness of AA6063 is feed rate. The optimum condition of surface roughness is at the feed rate of 0.05 mm/rev, cutting speed of 600 rpm and depth of cut of 0.10 mm. While the optimum parameter of cutting tool is cermet insert with the lowest value of surface roughness (Ra) result which is 0.650 μm.

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.

Influence of Cutting Parameters on Surface Morphology of Austenitic Stainless Steel after Turning

2014 ◽  
Vol 657 ◽  
pp. 23-27
Author(s):  
M. Grzegorz Krolczyk ◽  
Stanisław Legutko ◽  
W. Radoslaw Maruda
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Surface Morphology ◽  
Stainless Steels ◽  
Austenitic Stainless Steels ◽  
Cutting Parameters ◽  
Geometrical Parameters ◽  
Turning Process ◽  
Deep Well ◽  
Coated Carbide

The study presents the contribution in engineering of surfaces particularly in surface morphology of Austenitic Stainless Steels. The objective of the investigation was to determine the surface morphology of austenitic stainless steel after turning with coated carbide tool point. The investigation included geometrical parameters of SI for different cutting parameters in dry turning process of austenitic stainless steel. The study has been performed within a production facility during the production of electric motor parts and deep-well pumps.

scholarly journals Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and Desirability Approach

2020 ◽  
Vol 65 (1) ◽  
pp. 10-26
Author(s):  
Septi Boucherit ◽  
Sofiane Berkani ◽  
Mohamed Athmane Yallese ◽  
Riad Khettabi ◽  
Tarek Mabrouki
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Cutting Force ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Desirability Approach ◽  
Optimization Of Cutting Parameters ◽  
Coated Carbide

In the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc = 350 m/min, f = 0.088 mm/rev, and ap = 0.9 mm).

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