Optimizing Multi Response Green Machining Using Taguchi Method Based on Grey Relational Analysis

2015 ◽  
Vol 747 ◽  
pp. 277-281 ◽  
Author(s):  
M. Imron Mustajib ◽  
Teguh Prasetyo ◽  
Heri Awalul Ilhamsah ◽  
Rudy Soenoko ◽  
Sugiono
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Power Consumption ◽  
Cutting Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Control Factors ◽  
Green Machining ◽  
Early Survey ◽  
Grey Relational

This paper presents an optimization of multi response green machining of aluminum 6061 valve. The research is started with study literature and early survey to identify various factors that may likely influence in the green machining.The next step is investigating and collecting experiment data of the control factors (working in 3 levels) for depth of cut, feeding, and cutting speed factor on two responses; power consumption and surface roughness (Ra). The data were evaluated using Taguchi method based on grey relational anaylisis. Statistic tools coupled together with Taguchi design to process the output of the experiment. Finally, the research has successfully to deliver knowledges of the cutting speed and feeding factors have a dominant influence in power consumption and surface roughness of the green machining process

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.

Machining Apprenticeship Based on Experimental Training Practice

2011 ◽  
Vol 692 ◽  
pp. 83-92
Author(s):  
Pedro Jose Arrazola ◽  
A. Villar ◽  
R. Fernández ◽  
J. Aperribay
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Feed Rate ◽  
Cutting Forces ◽  
Theoretical Calculations ◽  
Cutting Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Practical Training ◽  
Input Parameters

This article describes a practical machining training aiming that the students acquire the theoretical-practical knowledge of chip formation process. The training takes place after theoretical lessons of machining processes. Thus, this practice allows strengthening the knowledge gained during the lessons. The practical training lasts for five hours, and the student assisted by the teacher analyses the influence of some machining entry parameters (cutting speed, feed rate...) on exit parameters like: (I) cutting forces and power consumption, (II) surface roughness, and (III) chip typology. The practical session is carried out on an experimental set-up (Lathe CNC Danobar 65) equipped with sensors and devices to measure forces (sensor Kistler 9121) and power consumption. In addition, a portable rugosimeter (Hommelwerke) is employed to perform surface roughness measurements. No especial devices are needed for the chip typology analysis. In the case of cutting forces and power consumption, the following input parameters influences are analysed: feed rate, depth of cut and cutting speed. In the case of surface roughness analysis, the following input parameters influences are analysed: feed rate and nose radius of the cutting insert. Finally, regarding chip typology feed rate and depth of cut are examined. The experimental results are compared with model predictions (theoretical calculations) for the three issues studied. The students have to compare both results: theoretical an empirical and they need to explain the reasons when discrepancies appear. Results obtained during the last years demonstrate the student acquires better knowledge of the machining process, and at the same time realises of the process complexity.

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 Optimization with Taguchi Method of Influences on Surface Roughness of Cutting Parameters in CNC Turning Processing

Mechanika ◽  
2019 ◽  
Vol 25 (5) ◽  
pp. 397-405
Author(s):  
Mustafa özdemir
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Feed Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Cnc Machine ◽  
Control Factors ◽  
Steel Material ◽  
Validation Tests

In this study, the effect of cutting speed, feed rate, and depth of cut on surface roughness was experimentally examined in the processing of AISI 409 (ferritic chromium stainless steel) material. As cutting parameters, three cutting speeds (200, 300, and 400 m/min), three feed rates (0.1, 0.2, and 0.3 mm/rev), and three depths of cut (1, 2 and 3 mm) were selected. Turning tests in CNC machine were made according to Taguchi L27 orthogonal array and the signal/noise (S/N) ratios were used in the evaluation of the experimental results. By using Taguchi method, cutting parameters giving the optimum surface roughness (Ra and Rz) values were determined. The effect of control factors on the results was found with the help of Analysis of Variance (ANOVA). According to ANOVA results, the most important parameters affecting the surface roughness were determined as feed rate, depth of cut, and cutting speed, respectively. By conducting validation tests, the optimization was observed to be applied successfully.

Analysis of tool vibration and surface roughness during turning process of tempered steel samples using Taguchi method

2021 ◽  
pp. 095440892110019
Author(s):  
Menderes Kam ◽  
Mustafa Demirtaş
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Turning Process ◽  
Machining Time ◽  
Tool Vibration ◽  
Control Factors ◽  
Tempered Steel

This study analyzed the tool vibration (Vib) and surface roughness (Ra) during turning of AISI 4340 (34CrNiMo6) tempered steel samples using Taguchi Method. In this context, Taguchi design L18 (21 × 32) was used to analyze the experimental results. The vibration amplitude values from cutting tools were recorded for different machining parameters, control factors; two different sample hardness (46 and 53 HRc), three different cutting speeds (180, 220, 260 m.min−1), and feed rates (0.08, 0.14, 0.20 mm.rev−1) were selected. The machining parameters giving optimum Vib and Ra values were determined. Regression analysis is applied to predict values of Vib and Ra. Analysis of variance was used to determine the effects of machining parameters on the Vib and Ra values. The most important machining parameters were found to be the feed rate, sample hardness, and cutting speed for Vib and Ra, respectively. The lowest Vib and Ra values were obtained in 46 HRc sample as 0.0022 gRMS and 0.255 µm, respectively. The surface quality can be improved by reducing the sources of vibration by using appropriate machining parameters. As a result, there is a significant relationship between Ra and Vib. The lower Ra values were found during turning process of tempered steel samples according to the literature studies. It is suggested that the process can be preferred as an alternative process to grinding process due to lower cost and machining time. In application of the turning of experiment samples by ceramic cutting tool, a substantial technological and economical benefit has been observed.

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%.

OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Prof. Hemant k. Baitule ◽  
Satish Rahangdale ◽  
Vaibhav Kamane ◽  
Saurabh Yende
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Multiple Time ◽  
Turning Process ◽  
Workpiece Material ◽  
Hot Turning

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

Optimization of Cutting Parameters of Multiple Performance Characteristics in End Milling of AlSi/AIN MMC – Taguchi Method and Grey Relational Analysis

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
S. H. Tomadi ◽  
J. A. Ghani ◽  
C. H. Che Haron ◽  
M. S. Kasim ◽  
A. R. Daud
Keyword(s):  
Taguchi Method ◽  
Grey Relational Analysis ◽  
Volume Fraction ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

The main objective of this paper is to investigate and optimize the cutting parameters on multiple performance characteristics in end milling of Aluminium Silicon alloy reinforced with Aluminium Nitride (AlSi/AlN MMC) using Taguchi method and Grey relational analysis (GRA). The fabrication of AlSi/AlN MMC was made via stir casting with various volume fraction of particles reinforcement (10%, 15% and 20%). End milling machining was done under dry cutting condition by using two types of cutting tool (uncoated & PVD TiAlN coated carbide). Eighteen experiments (L18) orthogonal array with five factors (type of tool, cutting speed, feed rate, depth of cut, and volume fraction of particles reinforcement) were implemented. The analysis of optimization using GRA concludes that the better results for the combination of lower surface roughness, longer tool life, lower cutting force and higher material removal could be achieved when using uncoated carbide with cutting speed 240m/min, feed 0.4mm/tooth, depth of cut 0.3mm and 15% volume fraction of AlN particles reinforcement. The study confirmed that with a minimum number of experiments, Taguchi method is capable to design the experiments and optimized the cutting parameters for these performance characteristics using GRA for this newly develop material under investigation.

Characterisation, Performance and Optimisation of Nanocellulose Metalworking Fluid (MWF) for Green Machining Process

2021 ◽  
Vol 18 (4) ◽  
pp. 9188-9207
Author(s):  
Mahendran Samykano ◽  
J. Kananathan ◽  
K. Kadirgama ◽  
A. K. Amirruddin ◽  
D. Ramasamy ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Surface Roughness ◽  
Tool Wear ◽  
Feed Rate ◽  
Cutting Speed ◽  
Machining Process ◽  
Alumina Nanoparticles ◽  
Working Fluid ◽  
Depth Of Cut ◽  
Nanoparticle Concentration

The present research attempts to develop a hybrid coolant by mixing alumina nanoparticles with cellulose nanocrystal (CNC) into ethylene glycol-water (60:40) and investigate the viability of formulated hybrid nanocoolant (CNC-Al2O3-EG-Water) towards enhancing the machining behavior. The two-step method has been adapted to develop the hybrid nanocoolant at various volume concentrations (0.1, 0.5, and 0.9%). Results indicated a significant enhancement in thermal properties and tribological behaviour of the developed hybrid coolant. The thermal conductivity improved by 20-25% compared to the metal working fluid (MWF) with thermal conductivity of 0.55 W/m℃. Besides, a reduction in wear and friction coefficient was observed with the escalation in the nanoparticle concentration. The machining performance of the developed hybrid coolant was evaluated using Minimum Quantity Lubrication (MQL) in the turning of mild steel. A regression model was developed to assess the deviations in the tool flank wear and surface roughness in terms of feed, cutting speed, depth of the cut, and nanoparticle concentration using Response Surface Methodology (RSM). The mathematical modeling shows that cutting speed has the most significant impact on surface roughness and tool wear, followed by feed rate. The depth of cut does not affect surface roughness or tool wear. Surface roughness achieved 24% reduction, 39% enhancement in tool length of cut, and 33.33% improvement in tool life span. From this, the surface roughness was primarily affected by spindle cutting speed, feed rate, and then cutting depth while utilising either conventional water or composite nanofluid as a coolant. The developed hybrid coolant manifestly improved the machining behaviour.

Surface Roughness and Chip Formation of AlSi/AIN Metal Matrix Composite by End Milling Machining using the Taguchi Method

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
M. S. Said ◽  
J. A. Ghani ◽  
R. Othman ◽  
M. A. Selamat ◽  
N. N. Wan ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
Matrix Composite ◽  
Surface Finish ◽  
Chip Formation ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Taguchi Optimization ◽  
Cutting Speeds

The purpose of this research is to demonstrate surface roughness and chip formation by the machining of Aluminium silicon alloy (AlSic) matrix composite, reinforced with aluminium nitride (AlN), with three types of carbide inserts present. Experiments were conducted at various cutting speeds, feed rates, and depths of cut, according to the Taguchi method, using a standard orthogonal array L9 (34). The effects of cutting speeds, feed rates, depths of cut, and types of tool on surface roughness during the milling operation were evaluated using Taguchi optimization methodology, using the signal-to-noise (S/N) ratio. The surface finish produced is very important in determining whether the quality of the machined part is within specification and permissible tolerance limits. It is understood that chip formation is a fundamental element that influences tool performance. The analysis of chip formation was done using a Sometech SV-35 video microscope. The analysis of results, using the S/N ratio, concluded that a combination of low feed rate, low depth of cut, medium cutting speed, and an uncoated tool, gave a remarkable surface finish. The chips formed from the experiment varied from semi–continuous to discontinuous. 

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