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.

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 The Impact of Independent Variables Surface Machining Duplex Stainless Steel on the Flank Wear

2020 ◽  
Vol 26 (1) ◽  
pp. 27-30
Author(s):  
Tomasz Dyl ◽  
Adam Charchalis ◽  
Mirosław Szyfelbain
Keyword(s):  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Surface Engineering ◽  
Flank Wear ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Surface Machining ◽  
The Impact

Surface engineering is important for carried out to improve the quality of the surface layer of the material. It is important that in special applications of corrosion resistant steel, low surface roughness is obtained. Duplex stainless steel is becoming more widely used for example in the petrochemical industry or shipbuilding. Duplex stainless steel is a material classified as difficult-to-cut. It is therefore important to investigate the impact of machining parameters on the durability and wear of a cutting tool. In the paper has determined the influence of variables machining: feed rate, depth of cut, cutting speed, on the maximum tool flank wear. Surface machining was carry out with carbide tipped inserts. The criterion of the smallest roughness and the highest wear was proposed.

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 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 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.

Possible examinations of stone machining focused on the relationship between technological parameters and surface quality

2013 ◽  
Vol 4 (1) ◽  
pp. 63-68 ◽  
Author(s):  
Zs. Kun ◽  
I. G. Gyurika
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Theoretical Background ◽  
Motion Path ◽  
Depth Of Cut ◽  
Manufacturing Cost ◽  
Technological Parameters ◽  
The Relationship ◽  
Manufacturing Time

Abstract The stone products with different sizes, geometries and materials — like machine tool's bench, measuring machine's board or sculptures, floor tiles — can be produced automatically while the manufacturing engineer uses objective function similar to metal cutting. This function can minimise the manufacturing time or the manufacturing cost, in other cases it can maximise of the tool's life. To use several functions, manufacturing engineers need an overall theoretical background knowledge, which can give useful information about the choosing of technological parameters (e.g. feed rate, depth of cut, or cutting speed), the choosing of applicable tools or especially the choosing of the optimum motion path. A similarly important customer's requirement is the appropriate surface roughness of the machined (cut, sawn or milled) stone product. This paper's first part is about a five-month-long literature review, which summarizes in short the studies (researches and results) considered the most important by the authors. These works are about the investigation of the surface roughness of stone products in stone machining. In the second part of this paper the authors try to determine research possibilities and trends, which can help to specify the relation between the surface roughness and technological parameters. Most of the suggestions of this paper are about stone milling, which is the least investigated machining method in the world.

Nature instigated Grey wolf algorithm for parametric optimization during machining (Milling) of polymer nanocomposites

2021 ◽  
pp. 089270572199320
Author(s):  
Prakhar Kumar Kharwar ◽  
Rajesh Kumar Verma
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Polymer Nanocomposites ◽  
Feed Rate ◽  
Fitness Function ◽  
Depth Of Cut ◽  
Multiwall Carbon Nanotube ◽  
Grey Wolf ◽  
Milling Characteristics ◽  
The Impact

The new era of engineering society focuses on the utilization of the potential advantage of carbon nanomaterials. The machinability facets of nanocarbon materials are passing through an initial stage. This article emphasizes the machinability evaluation and optimization of Milling performances, namely Surface roughness (Ra), Cutting force (Fc), and Material removal rate (MRR) using a recently developed Grey wolf optimization algorithm (GWOA). The Taguchi theory-based L27 orthogonal array (OA) was employed for the Machining (Milling) of polymer nanocomposites reinforced by Multiwall carbon nanotube (MWCNT). The second-order polynomial equation was intended for the analysis of the model. These mathematical models were used as a fitness function in the GWOA to predict machining performances. The ANOVA outcomes efficiently explore the impact of machine parameters on Milling characteristics. The optimal combination for lower surface roughness value is 1.5 MWCNT wt.%, 1500 rpm of spindle speed, 50 mm/min of feed rate, and 3 mm depth of cut. For lower cutting force, 1.0 wt.%, 1500 rpm, 90 mm/min feed rate and 1 mm depth of cut and the maximize MRR was acquired at 0.5 wt.%, 500 rpm, 150 mm/min feed rate and 3 mm depth of cut. The deviation of the predicted value from the experimental value of Ra, Fc, and MRR are found as 2.5, 6.5 and 5.9%, respectively. The convergence plot of all Milling characteristics suggests the application potential of the GWO algorithm for quality improvement in a manufacturing environment.

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