Optimization of Cutting Parameters on Surface  Roughness and Productivity when Milling Wood Materials

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.36.28 ◽

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

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Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning

Materials & Design (1980-2015) ◽

10.1016/j.matdes.2006.01.008 ◽

2007 ◽

Vol 28 (4) ◽

pp. 1379-1385 ◽

Cited By ~ 326

Author(s):

M. Nalbant ◽

H. Gökkaya ◽

G. Sur

Keyword(s):

Surface Roughness ◽

Taguchi Method ◽

Cutting Parameters ◽

Optimization Of Cutting Parameters

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Optimization of Cutting Parameters Affecting the Surface Roughness of Al 6061 Dry Milling Machining Using Taguchi Method

Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions (2nd Edition) - Environmental Science and Engineering ◽

10.1007/978-3-030-51210-1_46 ◽

2021 ◽

pp. 279-283

Author(s):

S. Sulaiman ◽

A. Aldeehani ◽

M. Alhajji ◽

W. N. W. Isahak

Keyword(s):

Surface Roughness ◽

Taguchi Method ◽

Cutting Parameters ◽

Dry Milling ◽

Al 6061 ◽

Optimization Of Cutting Parameters

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Optimization of Cutting Parameters for Trade-off Among Carbon Emissions, Surface Roughness, and Processing Time

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-019-0408-9 ◽

2019 ◽

Vol 32 (1) ◽

Cited By ~ 1

Author(s):

Zhipeng Jiang ◽

Dong Gao ◽

Yong Lu ◽

Xianli Liu

Keyword(s):

Surface Roughness ◽

Carbon Emissions ◽

Processing Time ◽

Cutting Parameters ◽

Machining Process ◽

Trade Off ◽

Pareto Fronts ◽

Optimization Of Cutting Parameters ◽

1045 Steel ◽

Optimal Cutting Parameters

AbstractAs the manufacturing industry is facing increasingly serious environmental problems, because of which carbon tax policies are being implemented, choosing the optimum cutting parameters during the machining process is crucial for automobile panel dies in order to achieve synergistic minimization of the environment impact, product quality, and processing efficiency. This paper presents a processing task-based evaluation method to optimize the cutting parameters, considering the trade-off among carbon emissions, surface roughness, and processing time. Three objective models and their relationships with the cutting parameters were obtained through input–output, response surface, and theoretical analyses, respectively. Examples of cylindrical turning were applied to achieve a central composite design (CCD), and relative validation experiments were applied to evaluate the proposed method. The experiments were conducted on the CAK50135di lathe cutting of AISI 1045 steel, and NSGA-II was used to obtain the Pareto fronts of the three objectives. Based on the TOPSIS method, the Pareto solution set was ranked to find the optimal solution to evaluate and select the optimal cutting parameters. An S/N ratio analysis and contour plots were applied to analyze the influence of each decision variable on the optimization objective. Finally, the changing rules of a single factor for each objective were analyzed. The results demonstrate that the proposed method is effective in finding the trade-off among the three objectives and obtaining reasonable application ranges of the cutting parameters from Pareto fronts.

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Research on Optimization of Cutting Parameters Based on Genetic Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.4640 ◽

2011 ◽

Vol 121-126 ◽

pp. 4640-4645 ◽

Cited By ~ 1

Author(s):

Shu Ren Zhang ◽

Xue Guang Li ◽

Jun Wang ◽

Hui Wei Wang

Keyword(s):

Genetic Algorithm ◽

Surface Roughness ◽

Surface Quality ◽

Bp Neural Network ◽

Prediction Method ◽

Cutting Parameters ◽

Multi Objective Optimization ◽

Experiment Data ◽

Optimization Of Cutting Parameters ◽

Working Efficiency

Three elements of Cutting dosages have a great effect on parts surface quality and working efficiency, the best organization of cutting three elements for parts surface quality must be found before machining, in order to surface roughness and machining cost of parts, multi-objective optimization model is established in this paper, model is solved by using genetic algorithm. Based on the BP neural network of three layers, forecasting model of surface roughness is established. According to existing experiment data and optimized cutting dosages, analysis and prediction of surface roughness is done. Machining experiment is done by using optimized data. The experiment result verifies feasibility of this optimistic method and prediction method of surface roughness.

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

Metals ◽

10.3390/met9090972 ◽

2019 ◽

Vol 9 (9) ◽

pp. 972 ◽

Cited By ~ 2

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.

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INVESTIGATION OF SURFACE MORPHOLOGY OF DRILLED CFRP PLATES AND OPTIMIZATION OF CUTTING PARAMETERS

Surface Review and Letters ◽

10.1142/s0218625x19502093 ◽

2020 ◽

Vol 27 (09) ◽

pp. 1950209

Author(s):

ÖMER ERKAN ◽

GÖKHAN SUR ◽

ENGIN NAS

Keyword(s):

Surface Roughness ◽

Feed Rate ◽

Design Method ◽

Cutting Speed ◽

Cutting Parameters ◽

Feed Speed ◽

Reinforced Polymer ◽

Cfrp Composite ◽

Optimization Of Cutting Parameters ◽

Full Factorial

In this study, the carbon fiber reinforced polymer (CFRP) composite material was drilled using different parameters ([Formula: see text] and [Formula: see text] Point Angle, 30, 60 and [Formula: see text] cutting speed and 0.06, 0.08 and [Formula: see text] feed rate). Experimental parameters were designed according to full factorial design method and the results were analyzed using Taguchi L18 experimental design. The results of the study show that the lowest surface roughness values are 0.1958 and [Formula: see text]m with the cutting speed of 90 [Formula: see text] and feed rate of [Formula: see text] in the Point angles of [Formula: see text] and [Formula: see text], respectively. When the results of Anova analysis were evaluated, parameters (feed speed, cutting speed and end point angle) according to the effect ratios on surface roughness were formed at the rates of 41.06%, 33.13% and 5.07%, respectively. The most suitable parameters according to S/N ratios were determined using A2B3C1 factors for the average surface roughness.

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MEASUREMENT OF SURFACE QUALITY AND OPTIMIZATION OF CUTTING PARAMETERS IN SLOT MILLING OF GFRP COMPOSITE MATERIALS WITH DIFFERENT FIBER RATIOS PRODUCED BY PULTRUSION METHOD

Surface Review and Letters ◽

10.1142/s0218625x21500955 ◽

2021 ◽

pp. 2150095

Author(s):

HARUN YAKA

Keyword(s):

Surface Roughness ◽

Regression Models ◽

Cutting Tools ◽

Cutting Parameters ◽

Fiber Reinforced Polymer ◽

Fiber Reinforced ◽

Damage Factor ◽

Reinforced Polymer ◽

Optimization Of Cutting Parameters ◽

Delamination Damage

The final shape of fiber-reinforced polymer (FRP) materials is usually given by machining. However, machining FRP materials is complex and difficult. Appropriate cutting tools and cutting parameters should be determined to overcome these difficulties. In this work, glass fiber-reinforced polymer (GFRP) materials with 60% and 68% fiber ratio, produced by pultrusion method, were machined. End of the milling, surface roughness (Ra), delamination damage factor (Fdd), sound and vibration results were analyzed. Experiments were carried out with Taguchi L[Formula: see text] mixed design and the results were analyzed by Taguchi, ANOVA and Pareto charts. In Taguchi and Pareto analyses, the most effective parameter for surface roughness and delamination damage factor was the feed rate, and the cutting velocity for sound and vibration. Different regression models have been tried. Linear regression was found as most suitable. The significance values of the regression models are 92.04% for surface roughness, 87.12% for delamination damage factor, 98.64% for sound and 73.27% for vibration.

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