scholarly journals Machinability Studies and Optimization of AA 6082/Fly Ash/Carbonized Eggshell Matrix Composite

2021 ◽  
Vol 31 (4) ◽  
pp. 207-216
Author(s):  
Ndudim H. Ononiwu ◽  
Chigbogu G. Ozoegwu ◽  
Nkosinathi Madushele ◽  
Esther T. Akinlabi
Keyword(s):  
Fly Ash ◽  
Function Analysis ◽  
Removal Rate ◽  
Desirability Function ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Chip Morphology ◽  
Influential Factor ◽  
Depth Of Cut ◽  
Desirability Function Analysis

Machinability studies of aluminium matrix composites (AMCs) is a necessary investigation required to understand their behaviour during machining to produce components effectively and efficiently. This established need has led to the investigation into the machinability of AA 6082 reinforced with 2.5 wt.% fly ash and 2.5 wt.% carbonized eggshell fabricated via stir casting. The studied machinability indices were material removal rate (MRR), cutting temperature, built-up edges (BUE) formation and chip morphology while the selected inputs were cutting speed (100 mm/min, 200 mm/min, 300 mm/min), feed (0.1 mm/rev, 0.2 mm/rev, 0.3 mm/rev) and depth of cut (0.5 mm, 1 mm, 1.5 mm). For the experimental design, the L9 orthogonal array was preferred to create 9 experimental runs. The analysis of the built-up edges showed that it increased at lower cutting speeds and increased feed and depth of cut. The examination of the produced chips after each experimental run showed the presence of c-shaped, helically shaped and ribbon-shaped chips. The analysis of variance (ANOVA) for both MRR and cutting temperature indicated that the depth of cut was the most influential factor on both responses. Multi-objective optimization using desirability function analysis showed that the optimum combination of parameters was 300 mm/min, 0.2 mm/rev and 1.0 mm for the cutting speed, feed and depth of cut respectively. The ANOVA of the composite desirability indicated that the cutting speed was the most contributing factor.

Multi-Response Optimization in Turning of AA6061 T6 Using Desirability Function Analysis

2015 ◽  
Vol 812 ◽  
pp. 124-129 ◽  
Author(s):  
P. Jayaraman ◽  
L. Mahesh Kumar
Keyword(s):  
Function Analysis ◽  
Removal Rate ◽  
Desirability Function ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Multiple Responses ◽  
Ideal Approach ◽  
Response Optimization ◽  
Desirability Function Analysis

This paper presents an ideal approach for the optimization of machining parameters on turning of AA6061 T6 aluminium alloy with multiple responses based on orthogonal array with desirability function analysis. In this study, turning parameters namely cutting speed, feed rate and depth of cut are optimized with the considerations of multiple responses such as surface roughness (Ra), roundness (Ø) and material removal rate (MRR). Multi response optimization of machining parameters was done through desirability function analysis. The optimum machining parameters have been identified by a composite desirability value obtained from desirability function analysis. The performance index and significant contribution of process parameters were determined by analysis of variance.

Optimization in Hot Turning of Nickel Based Alloy Using Desirability Function Analysis

2016 ◽  
Vol 24 ◽  
pp. 64-70 ◽  
Author(s):  
A.K. Parida ◽  
K.P. Maity
Keyword(s):  
Feed Rate ◽  
Function Analysis ◽  
Desirability Function ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Cutting Velocity ◽  
Nickel Based Alloy ◽  
Desirability Function Analysis ◽  
Hot Turning

This paper presents a desirability function approach in order to find out an optimal combination of Machining parameters for multi-response parameters in hot turning operation of nickel based alloy. Taguchi’s L9 orthogonal array is used for experimental design. The machining parameters such as cutting velocity, feed rate, depth of cut and temperature are optimized by multi-response considerations namely power, flank wear, and MRR. ANOVA test was carried out and it was found that cutting speed is most influence parameter followed by feed rate, depth of cut and workpiece temperature. The optimization of machining parameters was found at 5.8 m/min of cutting speed, 30 °C preheating temperature, 0.2 mm depth of cut and 0.15 mm/rev feed rate

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.

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

2020 ◽  
Vol 17 (1) ◽  
pp. 7629-7647
Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Coconut Oil ◽  
Chip Morphology ◽  
Weight Fraction ◽  
Cutting Fluid ◽  
Depth Of Cut

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

Application of Desirability Function Analysis and Utility Concept for Selection of Optimum Cutting Parameters in Turning Operation

2016 ◽  
Vol 15 (01) ◽  
pp. 1-11 ◽  
Author(s):  
B. Singaravel ◽  
T. Selvaraj
Keyword(s):  
Function Analysis ◽  
Removal Rate ◽  
Desirability Function ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Relative Significance ◽  
Utility Concept ◽  
Coated Carbide ◽  
Desirability Function Analysis ◽  
Selection Of

Multi-objective optimization method is used to simultaneously maximize and minimize the various criteria involved in complex industrial problems. In the present work, the optimum combination of cutting parameters is estimated in the turning of EN25 steel with coated carbide tools by performing desirability function analysis and utility concept. The experiments were designed as per L18 Taguchi mixed level orthogonal array with each trial performed under different conditions. These methods are employed for minimization of cutting force, surface roughness and maximization of material removal rate. The optimized results are compared and utility concept gave good combination of input and output parameters. Finally, Analysis of Variance (ANOVA) on overall desirability and utility value was employed to identify the relative significance of factors in terms of their percentage contribution to the responses.

scholarly journals Modeling of cutting performances in turning process using artificial neural networks

2017 ◽  
Vol 9 ◽  
pp. 184797901771898 ◽  
Author(s):  
Samya Dahbi ◽  
Latifa Ezzine ◽  
Haj EL Moussami
Keyword(s):  
Network Architecture ◽  
Removal Rate ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Performance Criteria ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Turning Process ◽  
Average Percentage ◽  
Artificial Neural

In this article, we present the modeling of cutting performances in turning of 2017A aluminum alloy under four turning parameters: cutting speed, feed rate, depth of cut, and nose radius. The modeled performances include surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a computer numerically controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop an artificial neural network that models the pre-cited cutting performances by following a specific methodology. The adequate network architecture was selected using three performance criteria: correlation coefficient ( R2), mean squared error (MSE), and average percentage error (APE). It was clearly seen that the selected network estimates the cutting performances in turning process with high accuracy: R2 > 99%, MSE < 0.3%, and APE < 6%.

scholarly journals EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS, POWER CONSUMPTION, AND MATERIAL REMOVAL RATE OF ALUMINIUM 6065-SI-MWCNT METAL MATRIX COMPOSITE IN TURNING OPERATIONS

2021 ◽  
Vol 22 (2) ◽  
pp. 283-293
Author(s):  
Savina Jaddinagadhe Puttaswamy ◽  
Raghavendra Bommanahalli Venkatagiriyappa
Keyword(s):  
Surface Roughness ◽  
Metal Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Stir Casting ◽  
Influential Factor ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Lathe Machine ◽  
Tungsten Carbide Tool

Nanocomposites were prepared with Al-6065-Si and multi walled carbon nanotubes of 1 wt.% as reinforcement through the stir-casting method. Fabricated nanocomposites were machined on a lathe machine using a tungsten carbide tool. The study investigated the multi-objective optimization of the turning operation. Cutting velocity, feed, and depth of cut were considered for providing minimum Surface Roughness of the workpiece. Also, the power consumed by the lathe machine with maximum metal removal rate was examined by surface response methodology. The design of experiments was developed based on rotational central composite design. Analysis of variance was executed to investigate the adequacy and the suitable fit of the developed mathematical models. Multiple regression models were used to represent the relationship between the input and the desired output variables. The analysis indicates that the feed is the most influential factor that effects the surface roughness of the workpiece. Cutting speed and the depth of cut are two other important factors that proportionally influence the power consumed by the lathe tool as compared to the feed rate. ABSTRAK: Komposit nano disediakan bersama Al-6065-Si dan karbon nanotiub berbilang dinding sebanyak 1 wt.% sebagai bahan penguat melalui kaedah kacauan-tuangan. Komposit nano yang terhasil melalui mesin pelarik ini menggunakan alat tungsten karbida. Kajian ini merupakan pengoptimuman pelbagai objektif operasi pusingan. Kelajuan potongan, suapan dan kedalaman potongan diambil kira sebagai pemberian minimum pada kekasaran permukaan bahan kerja. Tenaga yang digunakan bagi mesin pelarik dengan kadar maksimum penyingkiran logam diteliti melalui kaedah tindak balas permukaan. Rekaan eksperimen yang dibangunkan ini adalah berdasarkan rekaan komposit pusingan tengah. Analisis varian telah dijalankan bagi mengkaji kecukupan dan penyesuaian lengkap bagi model matematik yang dibangunkan. Model regresi berganda digunakan bagi menunjukkan hubungan antara input dan pembolehubah output yang dikehendaki. Analisis menunjukkan pemberian suapan merupakan faktor mempengaruhi keberkesanan kekasaran permukaan bahan kerja. Kelajuan pemotongan dan kedalaman potongan adalah dua faktor penting lain yang mempengaruhi kadar langsung ke atas tenaga yang digunakan oleh mesin pelarik dibandingkan kadar pemberian suapan.

Optimization of machining parameters under MQL turning of Ti-6Al-4V alloy with textured tool using multi-attribute decision-making methods

2019 ◽  
Vol 16 (5) ◽  
pp. 648-659 ◽  
Author(s):  
Rupinder Singh ◽  
Jasminder Singh Dureja ◽  
Manu Dogra ◽  
Jugraj Singh Randhawa
Keyword(s):  
Decision Making ◽  
Desirability Function ◽  
Cutting Tools ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Content Type ◽  
Multi Attribute Decision Making

Purpose This paper aims to focus on the application of multi-attribute decision-making methods (MADMs) to ascertain the optimal machining parameters while turning Ti-6Al-4V alloy under minimum quantity lubrication (MQL) conditions using Jatropha-curcas oil (JCO) bio-based lubricant. Design/methodology/approach The experiments were designed and performed using Taguchi L27 design of experiments methodology. A total of 27 experiments were performed under MQL conditions using textured carbide cutting tools on which different MADMs like Analytic hierarchy process (AHP), Technique for order preference by similarity to ideal solution (TOPSIS) and Simple additive weighting (SAW) were implemented in an empirical manner to extract optimize machining parameters for turning of Ti-6Al-4V alloy under set of constrained conditions. Findings The results evaluated through MADMs exhibit the optimized set of machining parameters (cutting speed Vc = 80 m/min, feed rate f = 0.05 mm/rev. and depth of cut ap = 0.10 mm) for minimizing the average surface roughness (Ra), maximum flank wear (Vbmax), tangential cutting force (Fc) and cutting temperature (T). Further, analysis of variance (ANOVA) and traditional desirability function approach was applied and results of TOPSIS and SAW methods having optimal setting of parameters were compared as well as confirmation experiments were conducted to verify the results. A SEM analysis at lowest and highest cutting speeds was performed to investigate the tool wear patterns. At the highest speed, large cutting temperature generated, thereby resulted in chipping as well as notching and fracturing of the textured insert. Originality/value The research paper attempted in exploring the optimized machining parameters during turning of difficult-to-cut titanium alloy (Ti-6AL-4V) with textured carbide cutting tool under MQL environment through combined approach of MADMs techniques. Ti-6Al-4V alloy has been extensively used in important aerospace components like fuselage, hydraulic tubing, bulk head, wing spar, landing gear, as well as bio-medical applications.

Improvement of CNC Lathe Performances by Tungsten-Carbide Tool Using Desirability Function Analysis for Fabrication of Miniature Component

2020 ◽  
Vol 1002 ◽  
pp. 3-11
Author(s):  
Azzam Sabah Hameed ◽  
Mohaned S. Jafar ◽  
Bijan Mallick
Keyword(s):  
Surface Roughness ◽  
Wear Rate ◽  
Tool Wear ◽  
Function Analysis ◽  
Desirability Function ◽  
Depth Of Cut ◽  
Tool Wear Rate ◽  
Cnc Lathe ◽  
Tungsten Carbide Tool ◽  
Desirability Function Analysis

Computer numerical control (CNC) machine has greater utility in the modern advanced industrial field. This paper deals with the parametric effects such as spindle speed (1500-2100 rpm) (N) (X1), depth of cut (DOC) (0.15-0.55 mm) (X2) and feed rate (f) (30-50 mm/min) (X3) on machining characteristics like tool wear rate (TWR) and surface roughness (Ra) during fabrication of IS-617 Aluminum miniature component by advanced CNC lathe using Tungsten-carbide tool. The article analyzes the second-order mathematical model development with co-relation of co-efficient of regression (COR) and analysis of variances (ANOVA) using desirability function analysis during the production of the miniature segment. The paper also consists of multi-criteria optimization for achieving the optimal parametric combination for minimum surface roughness and tool wear rate for this manufacturing operation. The paper also shows the fabricated micro-product of Aluminum at the optimal parametric conditions using CNC programming. It is found that spindle speed has a greater effect on the tool wear rate and depth of cut has dominating effects on surface roughness of job specimen. Desirability parametric combination for minimized surface roughness as well as tool wear rate has been found 1523 rpm/0.15mm/30mmmin-1.

Optimization of Machining Hybrid Metal Matrix Composites Using Desirability Analysis

2014 ◽  
Vol 875-877 ◽  
pp. 1412-1420 ◽  
Author(s):  
R.R. Jai Preetham ◽  
Joel Morris ◽  
Kaushik Rajasekaran
Keyword(s):  
Tool Wear ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Function Analysis ◽  
Desirability Function ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Matrix Composites ◽  
Hybrid Metal Matrix Composites ◽  
Desirability Function Analysis

This paper presents the detailed discussions on fabrication of Aluminium - silicon carbide (10% by weight of particles) and boron carbide (5% by weight of particles) Hybrid Metal Matrix Composites (Al/SiC/B4C MMC) using stir casting method. SiC and a B4C particle range from 30μm to 50 μm. The cylindrical rods of diameter 60 mm and length 250 mm are fabricated and subsequently machined using medium duty lathe of 2 kW spindle power to study the machinability issues of Hybrid MMC using Poly Crystalline Diamond (PCD) insert of 1600 grade. The optimum machining parameters have been identified by a composite desirability value obtained from desirability function analysis as the performance index, and significant contribution of parameters can then be determined by analysis of variance. Confirmation test is also conducted to validate the test result. Experimental results have shown that machining performance can be improved effectively through this approach. Results show at higher cutting speeds, good surface finish is obtained with faster tool wear. It is concluded that, tool wear and cutting force are directly proportional to the cutting speed, where as surface roughness is inversely proportional to the cutting speed. Percentage of error obtained between experimental value and predicted value is within the limit.

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