scholarly journals Multi-response Optimization of Surface Roughness Roundness and MRR in Precision Turn-Boring of 15-5PH Stainless Steel Using Taguchi-Grey Approach

2017 ◽  
Author(s):  
Shen-Jenn Hwang ◽  
Yi-Hung Tsai
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Feed Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Experimental Results ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Fluid Temperature ◽  
Grey Relational

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for 15-5PH Stainless steel by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as concentration of cutting fluid , temperature of cutting fluid , feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error and material removal rate, the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, concentration of cutting fluid, radial depth of cut and temperature of cutting fluid. Finally, confirmation tests were carried out to make a comparison between the experimental results and developed model. Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

scholarly journals Multi-Objective Optimization of in Precision Turn-Boring Parameters for AA7050-T7451 with Multiple Performance Characteristics

2017 ◽  
Author(s):  
Shen Jenn Hwang ◽  
Yi-Hung Tsai
Keyword(s):  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Experimental Results ◽  
Depth Of Cut ◽  
Machining Performance ◽  
Radial Depth ◽  
Grey Relational Grade ◽  
Grey Relational

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for AA7050-T7451 by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of cutting variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is proved that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were performed to make a comparison between the experimental results. Experimental results have shown that machining performance in precision turn-boring process can be improved effectively through this approach

scholarly journals Multi-Optimization of Performance Parameters in Turn-Boring for Difficult-to-Cut Ti-6Al-4V

2017 ◽  
Author(s):  
Shen-Jenn Hwang ◽  
Xin-Tang Li
Keyword(s):  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Performance Characteristics ◽  
Experimental Results ◽  
Depth Of Cut ◽  
Machining Performance ◽  
Radial Depth ◽  
Grey Relational Grade ◽  
Grey Relational

The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for Ti-6Al-4V by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as, feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error, material removal rate and power consumption the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, radial depth of cut. Finally, confirmation tests were carried out to make a comparison between the experimental results. Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

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.

Investigation of Chip and Surface Roughness when Milling AISI304 Stainless Steel

2017 ◽  
Vol 889 ◽  
pp. 152-158
Author(s):  
K. Kadirgama ◽  
K. Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Feed Rate ◽  
Prediction Models ◽  
Cutting Speed ◽  
Experimental Result ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Axial Depth

Stainless steel was used for many engineering applications. The optimum parameters needs to be identify to save the cutting tool usage and increase productivity. The purpose of this study is to develop the surface roughness mathematical model for AISI 304 stainless steel when milling using TiN (CVD) carbide tool. The milling process was done under various cutting condition which is cutting speed (1500, 2000 and 2500 rpm), feed rate (0.02, 0.03 and 0.04 mm/tooth) and axial depth (0.1, 0.2 and 0.3 mm). The first order model and quadratic model have been developed using Response Surface Method (RSM) with confident level 95%. The prediction models were comparing with the actual experimental results. It is found that quadratic model much fit the experimental result compare to linear model. In general, the results obtained from the mathematical models were in good agreement with those obtained from the machining experiments. Besides that, it is shown that the influence of cutting speed and feed rate are much higher on surface roughness compare to depth of cut. The optimum cutting speed, feed rate and axial depth is 2500 rpm, 0.0212 mm/tooth and 0.3mm respectively. Besides that, continues chip is produced at cutting speed 2500 rpm meanwhile discontinues chip produced at cutting speed 1500 rpm.

scholarly journals Green machining for the dry milling process of stainless steel 304

2019 ◽  
Vol 234 (5) ◽  
pp. 881-899 ◽  
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.

An Experimental Study on Finish Dry Milling of AISI 321 Stainless Steel

2016 ◽  
Vol 861 ◽  
pp. 26-31 ◽  
Author(s):  
Peng Guo ◽  
Chuan Zhen Huang ◽  
Bin Zou ◽  
Jun Wang ◽  
Han Lian Liu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Tool Wear ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Dry Milling ◽  
Aisi 321 ◽  
Coated Cemented Carbide

The milling of AISI 321 stainless steel which has wide engineering applications particularly in automobile, aerospace and medicine is of great importance especially in the conditions where high surface quality is required. In this paper, L16 orthogonal array design of experiments was adopted to evaluate the machinability of AISI 321 stainless steel with coated cemented carbide tools under finish dry milling conditions, and the influence of cutting speed ( V ), feed rate ( f ) and depth of cut ( ap ) on cutting force, surface roughness and tool wear was analysed. The experimental results revealed that the cutting force decreased with an increase in the cutting speed and increased with an increase in the feed rate or the depth of cut. The tool wear was affected significantly by the cutting speed and the depth of cut, while the effect of the feed rate on the tool wear was insignificant. With the cutting speed increased up to 160 m/min, a decreasing tendency in the surface roughness was observed, but when the cutting speed was further increased, the surface roughness increased. The effect of the feed rate and the depth of cut on the surface roughness was slight.

scholarly journals Comparative Analysis of Machining Stainless Steel using Soluble and Vegetable Oils as Cutting Fluids

2019 ◽  
Vol 4 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Temitayo Samson Ogedengbe ◽  
Peter Awe ◽  
Ojotu Ijiwo Joseph
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Vegetable Oil ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Fluid ◽  
Cutting Fluids ◽  
Machining Parameters ◽  
Groundnut Oil ◽  
Machining Parameter

In this study, the performance of groundnut oil as an alternate cutting fluid was compared with that of soluble oil during machining of stainless steel. The temperature at the cutting zone, surface roughness and the chip formation were monitored under the two cutting conditions (soluble oil and vegetable oil). The machining parameters used were cutting speed (75 – 135 rev/min), feed rate (0.01 – 0.05 mm3/mm) and depth of cut (0.01 – 0.08 mm). The experiment was designed using Taguchi orthogonal array of Minitab 18 which generated a 9 run machining parameter mix for the experimentation. The Physiochemical properties of the various fluids were also analyzed to determine the properties and constituent elements of the cutting fluids. The actual machining of the stainless steel bar was done using a Colchester mastiff lathe machine. Results show that feed rate and cutting speed had the most significant effect on surface roughness during machining of stainless steel both with groundnut oil and soluble oil. Soluble oil was a better coolant but poorer in lubrication as vegetable oil reduced surface roughness more when used. Surface roughness value improved from 9.21μm during machining with soluble oil to 3.84μm during machining with groundnut oil which represented a 58.3% improvement. Hence, vegetable oil is therefore recommended as good alternative cutting fluid to soluble oil during machining of stainless steel.

Modeling the Surface Roughness for Fine Turning of AISI304 Stainless Steel

2007 ◽  
Vol 364-366 ◽  
pp. 644-648 ◽  
Author(s):  
Wei Shin Lin
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Prediction Model ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Variable Surface ◽  
Fine Turning

High ductility, high strength, high work hardening rate and low thermal conductivity of stainless steels are the main factors that make their machinability difficult. In this study, determination of the optimum cutting condition has been aimed at when fine turning an AISI 304 austenitic stainless steel using ceramic cutting tools. The cutting speeds for the turning test were from 80 to 320 m / min, feed rates were from 0.04 to 0.10 mm / rev and the depth of cut was fixed at 0.1 mm. According to the test results, we can find that the values of surface roughness were decreased when the cutting speed was increasing, and decrease with the decrease of feed rate. But when the cutting speed was greater than 360 m / min, or the feed rate was smaller than 0.02 mm / rev，the surface roughness would be deteriorated because of the chatter phenomenon. In this paper, a polynomial network is adopted to construct a prediction model on surface roughness for fine turning of AISI304 austenitic stainless steel. The polynomial network is composed of a number of functional nodes. These functional nodes are self-organized to form an optimal network architecture by using a predicted square error (PSE) criterion. It is shown that the polynomial network can correctly correlate the input variables (cutting speed and feed rate) with the output variable (surface roughness). Based on the surface roughness prediction model constructed, the surface roughness of the workpiece can be predicted with reasonable accuracy if the turning conditions are given and it is also consistent with the experimental results very well.

scholarly journals Modeling and optimization of face milling process parameters for AISI 4140 steel

2018 ◽  
Vol 12 (1) ◽  
pp. 5-10 ◽  
Author(s):  
Gokhan Basar ◽  
Funda Kahraman
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Aisi 4140 Steel ◽  
Control Factors ◽  
Aisi 4140

In this study, the effect of cutting parameters such as the depth of cut, feed rate, cutting speed and the number of inserts on surface roughness were investigated in the milling of the AISI 4140 steel. The optimal control factors for surface quality were detected by using the Taguchi technique. Experimental trials were designed according to the Taguchi L18 (21x33) orthogonal array. The statistical effects of control factors on surface roughness have been established by using the analysis of variance (ANOVA). Optimal cutting parameters were obtained by using the S/N ratio values. The ANOVA results showed that the effective factors were the number of inserts and the feed rate on surface roughness. However, the depth of cut and the cutting speed showed an insignificant effect. Additionally, the First-order and Second-order regression analysis were conducted to estimate the performance characteristics of the experiment. The acquired regression equation results matched with the surface roughness measurement results. The optimal performance characteristics were obtained as a 0.5 mm depth of cut, 0.08 mm/rev feed rate, 325 m/min cutting speed and 1 number of inserts by using the Taguchi method. Additionally, the confirmation test results indicated that the Taguchi method was very prosperous in the optimization of the machining parameters to obtain the minimum surface roughness in the milling of the AISI 4140 steel.

scholarly journals Influence of Cutting Fluid Flow Rate and Cutting Parameters on the Surface Roughness and Flank Wear of TiAlN Coated Tool In Turning AISI 1015 Steel Using Taguchi Method

2017 ◽  
Vol 62 (3) ◽  
pp. 1827-1832 ◽  
Author(s):  
C. Moganapriya ◽  
R. Rajasekar ◽  
K. Ponappa ◽  
R. Venkatesh ◽  
R. Karthick
Keyword(s):  
Surface Roughness ◽  
Fluid Flow ◽  
Tool Wear ◽  
Flow Rate ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Fluid Flow Rate

AbstractThis paper presents the influence of cutting parameters (Depth of cut, feed rate, spindle speed and cutting fluid flow rate) on the surface roughness and flank wear of physical vapor deposition (PVD) Cathodic arc evaporation coated TiAlN tungsten carbide cutting tool insert during CNC turning of AISI 1015 mild steel. Analysis of Variance has been applied to determine the critical influence of cutting parameters. Taguchi orthogonal test design has been employed to optimize the process parameters affecting surface roughness and tool wear. Depth of cut was found to be the most dominant factor contributing to high surface roughness (67.5%) of the inserts. However, cutting speed, feed rate and flow rate of cutting fluid showed minimal contribution to surface roughness. On the other hand, cutting speed (45.6%) and flow rate of cutting fluid (23%) were the dominant factors influencing tool wear. The optimum cutting conditions for desired surface roughness constitutes the following parameters such as medium cutting speed, low feed rate, low depth of cut and high cutting fluid flow rate. Minimal tool wear was achieved for the following process parameters such as low cutting speed, low feed rate, medium depth of cut and high cutting fluid flow rate.

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