OPTIMIZATION OF PROCESS PARAMETERS AFFECTING CUTTING FORCE, POWER CONSUMPTION AND SURFACE ROUGHNESS USING TAGUCHI-BASED GRAY RELATIONAL ANALYSIS IN TURNING AISI 1040 STEEL

2021 ◽  
Author(s):  
MAHIR AKGÜN
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Cutting Speed ◽  
System Optimization ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Significance Level ◽  
Relational Analysis

This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

scholarly journals Assessments of Process Parameters on Cutting Force and Surface Roughness during Drilling of AA7075/TiB2 In Situ Composite

Materials ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1726
Author(s):  
S. Parasuraman ◽  
I. Elamvazuthi ◽  
G. Kanagaraj ◽  
Elango Natarajan ◽  
A. Pugazhenthi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Basic Class ◽  
Tib2 Particles ◽  
The Impact ◽  
Loss Of Strength

Reinforced aluminum composites are the basic class of materials for aviation and transport industries. The machinability of these composites is still an issue due to the presence of hard fillers. The current research is aimed to investigate the drilling topographies of AA7075/TiB2 composites. The samples were prepared with 0, 3, 6, 9 and 12 wt.% of fillers and experiments were conducted by varying the cutting speed, feed, depth of cut and tool nose radius. The machining forces and surface topographies, the structure of the cutting tool and chip patterns were examined. The maximum cutting force was recorded upon increase in cutting speed because of thermal softening, loss of strength discontinuity and reduction of the built-up-edge. The increased plastic deformation with higher cutting speed resulted in the excess metal chip. In addition, the increase in cutting speed improved the surface roughness due to decrease in material movement. The cutting force was decreased upon high loading of TiB2 due to the deterioration of chips caused by fillers. Further introduction of TiB2 particles above 12 wt.% weakened the composite; however, due to the impact of the microcutting action of the fillers, the surface roughness was improved.

Intelligent Monitoring and Estimation of Surface Roughness on CNC Turning

2010 ◽  
Vol 443 ◽  
pp. 376-381 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Monitoring ◽  
Prediction Interval ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Roughness Model ◽  
Force Ratio

In order to realize an intelligent machine tool, an in-process monitoring system is developed to estimate the in-process surface roughness. The objective of this research is to propose a method to estimate the surface roughness during the in-process cutting by utilizing the in-process monitoring of cutting forces. The proposed in-process surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the cutting speed, the feed rate, the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction interval of the in-process surface roughness model has been also presented to monitor and control the in-process estimated surface roughness at 95% confident level. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be effectively used to monitor and estimate the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy achieved.

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

scholarly journals Application of Gray Relation Analysis for Multi-Response Optimization of Plasma Heat Assisted Turning Performance

2018 ◽  
Vol 7 (2.32) ◽  
pp. 143
Author(s):  
Thella Babu Rao ◽  
Venu Pilli ◽  
Nallamotu Revanth Sai Venkat ◽  
Nagandla Pavan ◽  
Thalari Shiva Ram
Keyword(s):  
Surface Roughness ◽  
Plasma Heating ◽  
Cutting Speed ◽  
Gray Relational Analysis ◽  
Machining Performance ◽  
Relational Analysis ◽  
Gray Relation Analysis ◽  
Cutting Length ◽  
Response Optimization ◽  
Hardened En24

This paper presents optimization of plasma heat assisted turning process for machining hardened EN24 die steel (53HRC) by using gray relational analysis. Flank wear and surface roughness (Ra) are experimentally measured as the process performance characteristics under varying conditions of preheating temperature, cutting speed and cutting length. The plasma heating approach was implemented to preheat the workpiece. The machining experiments were conducted according to the L16 design of experiments. Since the chosen machining performance indicators are found with confliction for the chosen process variables, the problem is treated as multi-response optimization problem to minimize the tool wear and surface roughness simultaneously. Therefore, the problem was solved by implementing the gray relational analysis and the derived optimal machining conditions were analysed and reported.  

scholarly journals Optimization of Power Consumption Associated with Surface Roughness in Ultrasonic Assisted Turning of Nimonic-90 Using Hybrid Particle Swarm-Simplex Method

Materials ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3418 ◽  
Author(s):  
Khanna ◽  
Airao ◽  
Gupta ◽  
Song ◽  
Liu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Fitness Function ◽  
Particle Swarm ◽  
Cutting Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Performance ◽  
Hybrid Particle ◽  
Ultrasonic Assisted

These days, power consumption and energy related issues are very hot topics of research especially for machine tooling process industries because of the strict environmental regulations and policies. Hence, the present paper discusses the application of such an advanced machining process i.e., ultrasonic assisted turning (UAT) process with the collaboration of nature inspired algorithms to determine the ideal solution. The cutting speed, feed rate, depth of cut and frequency of cutting tool were considered as input variables and the machining performance of Nimonic-90 alloy in terms of surface roughness and power consumption has been investigated. Then, the experimentation was conducted as per the Taguchi L9 orthogonal array and the mono as well as bi-objective optimizations were performed with standard particle swarm and hybrid particle swarm with simplex methods (PSO-SM). Further, the statistical analysis was performed with well-known analysis of variance (ANOVA) test. After that, the regression equation along with selected boundary conditions was used for creation of fitness function in the subjected algorithms. The results showed that the UAT process was more preferable for the Nimconic-90 alloy as compared with conventional turning process. In addition, the hybrid PSO-SM gave the best results for obtaining the minimized values of selected responses.

scholarly journals OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
Muhammad Yanis ◽  
Amrifan Saladin Mohruni ◽  
Safian Sharif ◽  
Irsyadi Yani
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Ann Model ◽  
Green Machining ◽  
Radial Depth ◽  
Thin Walled

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. 

Machining parameters effect in dry turning of AISI 316L stainless steel using coated carbide tools

2015 ◽  
Vol 231 (4) ◽  
pp. 676-683 ◽  
Author(s):  
Rusdi Nur ◽  
MY Noordin ◽  
S Izman ◽  
D Kurniawan
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Power Consumption ◽  
Cutting Force ◽  
Tool Life ◽  
Cutting Speed ◽  
Cutting Fluid ◽  
Machining Parameters ◽  
Aisi 316L ◽  
Coated Carbide

Austenitic stainless steel AISI 316L is used in many applications, including chemical industry, nuclear power plants, and medical devices, because of its high mechanical properties and corrosion resistance. Machinability study on the stainless steel is of interest. Toward sustainable manufacturing, this study also includes the power consumption during machining along with other machining responses of cutting force, surface roughness, and tool life. Turning on the stainless steel was performed using coated carbide tool without using cutting fluid. The turning was performed at various cutting speeds (90, 150, and 210 m/min) and feeds (0.10, 0.16, and 0.22 mm/rev). Response surface methodology was adopted in designing the experiments to quantify the effect of cutting speed and feed on the machining responses. It was found that cutting speed was proportional to power consumption and was inversely proportional to tool life, and showed no significant effect on the cutting force and the surface roughness. Feed was proportional to cutting force, power consumption, and surface roughness and was inversely proportional to tool life. Empirical equations developed from the results for all machining responses were shown to be useful in determining the optimum cutting parameters range.

Taguchi Method Based Experiments of Titanium TC11 Flank Milling Parameters

2009 ◽  
Vol 407-408 ◽  
pp. 608-611 ◽  
Author(s):  
Chang Yi Liu ◽  
Cheng Long Chu ◽  
Wen Hui Zhou ◽  
Jun Jie Yi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Flank Milling ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Mill Machining

Taguchi design methodology is applied to experiments of flank mill machining parameters of titanium alloy TC11 (Ti6.5A13.5Mo2Zr0.35Si) in conventional and high speed regimes. This study includes three factors, cutting speed, feed rate and depth of cut, about two types of tools. Experimental runs are conducted using an orthogonal array of L9(33), with measurement of cutting force, cutting temperature and surface roughness. The analysis of result shows that the factors combination for good surface roughness, low cutting temperature and low resultant cutting force are high cutting speed, low feed rate and low depth of cut.

Optimization of Machining Parameters of Valve Steel SUH03 (X45CrSiMo10-2) Using Gray Based Taguchi Method

2015 ◽  
Vol 813-814 ◽  
pp. 376-381 ◽  
Author(s):  
B. Yazhini ◽  
S. Rajeswari ◽  
Sivasakthivel
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Nose Radius ◽  
Valve Steel ◽  
Optimal Cutting Parameters

This paper embarks the machining parameters of Turning by optimization using Taguchi’s approach. The optimization is very essential in order to obtain the expected surface quality. The results of cutting parameters of optimization is seen in the Surface Roughness, Tool wear and MRR of the material. The L18 Orthogonal array has been chosen for the optimization of Valve Steel SUH03.The uncoated carbide inserts were used and the four parameters Speed, Feed, Depth of Cut and Nose Radius has been taken as input parameters. The Signal to Noise ratio and Analysis of Variance software has been analyzed using Minitab software through which the optimal cutting parameters of the best surface roughness, tool wear and MRR has been obtained. The final results have been compared by the Gray relational analysis to find the optimum machining conditions of all the parameters.

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