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.

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.

scholarly journals Experimental Investigation on Surface Roughness and Tool Wear in Dry Machining of TiC Reinforced Aluminium LM6 Composite

2013 ◽  
Vol 773-774 ◽  
pp. 339-347 ◽  
Author(s):  
Muhammad Yusuf ◽  
M.K.A. Ariffin ◽  
N. Ismail ◽  
S. Sulaiman
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Carbide Tool ◽  
Dry Machining ◽  
Workpiece Surface ◽  
High Cutting Speed

With increasing quantities of applications of Metal Matrix Composites (MMCs), the machinablity of these materials has become important for investigation. This paper presents an investigation of surface roughness and tool wear in dry machining of aluminium LM6-TiC composite using uncoated carbide tool. The experiments carried out consisted of different cutting models based on combination of cutting speed, feed rate and depth of cut as the parameters of cutting process. The cutting models designed based on the Design of Experiment Response Surface Methodology. The objective of this research is finding the optimum cutting parameters based on workpiece surface roughness and cutting tool wear. The results indicated that the optimum workpiece surface roughness was found at high cutting speed of 250 m min-1 with various feed rate within range of 0.05 to 0.2 mm rev-1, and depth of cut within range of 0.5 to 1.5 mm. Turning operation at high cutting speed of 250 m min-1 produced faster tool wear as compared to low cutting speed of 175 m min-1 and 100 m min-1. The wear minimum (VB = 42 μm ) was found at cutting speed of 100 m min-1, feet rate of 0.2 mm rev-1, and depth of cut of 1.0 mm until the length of cut reached 4050 mm. Based on the results of the workpiece surface roughness and the tool flank wear, recommended that turning of LM6 aluminium with 2 wt % TiC composite using uncoated carbide tool should be carried out at cutting speed higher than 175 m min-1 but at feed rate of less than 0.05 mm rev-1 and depth of cut less than 1.0 mm.

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.

Hard Turning of Hot Work Steel X38CRMOV5-3: Evaluation of Surface Roughness and Current Values Using Cutting Parameters

2020 ◽  
Author(s):  
Ali Kemal Cakir
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Feed Rate ◽  
Hard Turning ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Dry Machining ◽  
Motor Current ◽  
Design Factors

This study evaluates the surface roughness and current values using cutting parameters in the turning of AISI H11 being hot work tool steel under dry machining conditions. The selected design factors are the depth of cut, feed rate, cutting speed. A design of experiments was used to carry out this research. The obtained results were analyzed to determine the effects of input parameters on the resultant surface roughness, current using the analysis of variance (ANOVA) and the Response Surface Methodology (RSM). The experimental results showed that increasing feed rate increased the surface roughness, and current values. The most effective cutting parameter on all the output parameters was found to be the feed rate on the surface roughness. Also, the motor current values were influenced by the 38,48% depth of cut, 23,98% cutting speed, 25,52% feed rate, respectively.

scholarly journals Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool

2021 ◽  
Vol 13 (13) ◽  
pp. 7321
Author(s):  
Md. Rezaul Karim ◽  
Juairiya Binte Tariq ◽  
Shah Murtoza Morshed ◽  
Sabbir Hossain Shawon ◽  
Abir Hasan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Function Analysis ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Dry Machining ◽  
Pcd Tool ◽  
Desirability Function Analysis ◽  
Zr Alloy

Clean technological machining operations can improve traditional methods’ environmental, economic, and technical viability, resulting in sustainability, compatibility, and human-centered machining. This, this work focuses on sustainable machining of Al-Mg-Zr alloy with minimum quantity lubricant (MQL)-assisted machining using a polycrystalline diamond (PCD) tool. The effect of various process parameters on the surface roughness and cutting temperature were analyzed. The Taguchi L25 orthogonal array-based experimental design has been utilized. Experiments have been carried out in the MQL environment, and pressure was maintained at 8 bar. The multiple responses were optimized using desirability function analysis (DFA). Analysis of variance (ANOVA) shows that cutting speed and depth of cut are the most prominent factors for surface roughness and cutting temperature. Therefore, the DFA suggested that, to attain reasonable response values, a lower to moderate value of depth of cut, cutting speed and feed rate are appreciable. An artificial neural network (ANN) model with four different learning algorithms was used to predict the surface roughness and temperature. Apart from this, to address the sustainability aspect, life cycle assessment (LCA) of MQL-assisted and dry machining has been carried out. Energy consumption, CO2 emissions, and processing time have been determined for MQL-assisted and dry machining. The results showed that MQL-machining required a very nominal amount of cutting fluid, which produced a smaller carbon footprint. Moreover, very little energy consumption is required in MQL-machining to achieve high material removal and very low tool change.

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.

Comparison of Process Parameters on Mild Steel and SS304 Through Statistical Analysis of Optimization Technique

2020 ◽  
Vol 12 (7) ◽  
pp. 888-893
Author(s):  
Vinit Kumar ◽  
Mazhar Hussain ◽  
Rajnish Singh ◽  
Shashank Kumar
Keyword(s):  
Stainless Steel ◽  
Mild Steel ◽  
Process Parameters ◽  
Feed Rate ◽  
Cutting Tool ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Stainless Steel 304

The present study concentrated on the variation of process parameters on metal removal rate (MRR) used in turning of widely used material (stainless steel 304 and Mild steel). Turning is essential and robust process of material removal in the form of chips. The Turning process involved lots of process parameters as tool geometry, feed rate, rotational speed of job and rigidity of machine tools etc. In the present work study was done on the following cutting parameters as cutting speed (85,150 and 250 rpm), feed rate (0.13, 0.28 and 0.15, 0.09 mm/sec), depth of cut (0.4, 0.7 and 1 mm). The three label orthogonal array for process parameters were selected for metal removal rate analysis. The carbide tipped cutting tool was selected as cutting tool of positive rake angle. The analysis of process parameters was done through Minitab 17 software. The orthogonal array was selected 3*3; by the use of signal to noise (S/N) ratio is to minimise the variation due to uncontrolled parameters with the help of Taguchi method. Total nine experiments were performing on stainless steel and other set of nine experiments were perform on the mild steel. The experimental results reveals that moderate cutting speed 150 rpm, 0.09 mm/sec feed rate and 1 mm depth of cut yield good results for stainless steel 304 grade and mild steel.

scholarly journals Effect of Cutting Insert Type on Surface Roughness of Hardened AISI 420 Stainless Steel

2021 ◽  
Vol 4 (1) ◽  
pp. 171-185
Author(s):  
Anıl Berk Dalkıran ◽  
Furkan Yılmaz ◽  
Samet Emre Bilim
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Feed Rate ◽  
Cutting Speed ◽  
High Strength ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Aisi 420 ◽  
Cutting Insert ◽  
Coated Carbide

AISI 420 stainless steel is one of the alloys that can be used in various applications due to its malleability, high strength, and weldability. In this study, the effects of cutting parameters (feed rate, depth of cut, and cutting speed) on the surface roughness were investigated during the turning of AISI 420 under dry test conditions using coated carbide and ceramic cutting inserts. Response surface methodology, analysis of variance, and statistical methods of the main effect plot were applied to investigate the effects of input parameters on response values. The results of this study showed that feed rate followed by the depth of cut had the most significant effect on output parameters. According to the experimental data, as the feed rate and depth of cut increase, the surface roughness increases.

scholarly journals Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)

2021 ◽  
Vol 17 (2) ◽  
pp. 8-17
Author(s):  
Osamah F. Abdulateef
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Feed Rate ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Process Conditions ◽  
The Impact

Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with,  an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.

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