Application of Taguchi Method with Grey Fuzzy Logic for the Optimization of Machining Parameters in Machining Composites

2012 ◽  
pp. 219-241 ◽  
Author(s):  
K. Palanikumar ◽  
B. Latha ◽  
J. Paulo Davim
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Composite Materials ◽  
Taguchi Method ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Glass Fiber Reinforced Plastic ◽  
Glass Fiber Reinforced ◽  
Optimization Of Process Parameters

Glass fiber reinforced plastic (GFRP) composite materials are continuously displacing the traditional engineering materials and are finding increased applications in many fields, such as automobile, marine, sport goods, et cetera. Machining of these materials is needed to achieve near-net shape. In machining of composite materials, optimization of process parameters is an important concern. This chapter discusses the use of Taguchi method with Grey-fuzzy logic for the optimization of multiple performance characteristics considering material removal rate, surface roughness, and specific cutting pressure. Experiments were planned using Taguchi’s orthogonal array with the cutting conditions prefixed. The cutting parameters considered are workpiece (fiber orientation), cutting speed, feed, depth of cut, and machining time. The machining tests were performed on a lathe using coated cermet cutting tool. The results indicated that the optimization technique is greatly helpful in achieving better surface roughness and tool wear simultaneously in machining of GFRP composites.

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. 

Machining characteristics of Inconel 718 under several cutting conditions based on Taguchi method

2012 ◽  
Vol 227 (9) ◽  
pp. 1889-1897 ◽  
Author(s):  
R Thirumalai ◽  
JS Senthilkumaar ◽  
P Selvarani ◽  
S Ramesh
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
Flank Wear ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machining Parameters ◽  
Dry Machining ◽  
Noise Factors

Extensive researchers have conducted several experiments in the past for selecting the optimum parameters in machining nickel based alloy – Inconel 718. These experiments conducted so far are dealt with dry machining and flooded coolant machining of nickel alloy Inconel 718. In this research study, the usage of refrigerated coolant is also dealt with and it is compared with dry machining and flooded coolant machining. Cutting speed, feed and depth of cut are considered as the machining parameters. The effectiveness of the refrigerated coolant in machining the heat resistant super alloy material Inconel 718 with respect to these machining parameters are described in this article. The machinability studies parameters were generated with surface roughness and flank wear. The performance of uncoated carbide cutting tool was investigated at various cutting condition under dry, flooded coolant and refrigerated coolant machining. The relationship between the machining parameters and the performance measures were established and using analysis of variance significant machining parameters determined. This article made an attempt to Taguchi optimization technique to study the machinability performances of Inconel 718. Taguchi approach is an efficient and effective experimental method in which a response variable can be optimized, given various control and noise factors, using fewer experiments than a factorial design. Taguchi’s optimization analysis indicates that the factors level, its significance to influence the surface roughness and flank wear for the machining processes. Confirmation tests were conducted at an optimal condition to make a comparison between the experimental results foreseen from the mentioned correlations.

Fuzzy Modeling and Analysis on the Turning Parameters for Machining Force and Specific Cutting Pressure in CFRP Composites

2013 ◽  
Vol 766 ◽  
pp. 77-97
Author(s):  
T. Rajasekaran ◽  
V.N. Gaitonde ◽  
J. Paulo Davim
Keyword(s):  
Fuzzy Logic ◽  
Composite Materials ◽  
Prediction Models ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machined Surface ◽  
Sem Images ◽  
Machining Force ◽  
Soft Computing Techniques

Analysis of the cutting force and specific cutting pressure play vital roles in machining of the composite materials. The present experimental work describes the modeling of machining parameters using one of the soft computing techniques i.e. fuzzy logic for machining force and specific cutting pressure. The basic idea is to machine the carbon fiber reinforced plastic (CFRP) composite materials and measuring the cutting forces and then determining the machining force and specific cutting pressure. 27 experiments based on Taguchis L27 orthogonal array were carried out involving three machining parameters, namely, cutting speed, feed and depth of cut, each defined at three levels. Subsequently the prediction models were developed using three different fuzzy logic membership functions, namely, triangular, trapezoidal and bell shape. It is found that the predicted values of proposed responses such as machining force and specific cutting pressure are very close to the experimental values within the chosen ranges of the process parameters. The statistical analysis using ANOVA on machining parameters are also presented and discussed. The machined surface analyzed through SEM images revealed the damages encountered during turning process.

EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS FOR ALUMINIUM MATRIX COMPOSITE BY USING TAGUCHI METHOD WITH DECISION TREE ALGORITHM

2021 ◽  
pp. 2150021
Author(s):  
P. RAVEENDRAN ◽  
S. V. ALAGARSAMY ◽  
M. RAVICHANDRAN ◽  
M. MEIGNANAMOORTHY
Keyword(s):  
Surface Roughness ◽  
Decision Tree ◽  
Taguchi Method ◽  
Cutting Speed ◽  
Experimental Result ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Cnc Turning ◽  
Cnc Turning Process ◽  
Taguchi’S Design Of Experiments

The intend of this research work is to explore the effect of various parameters in a CNC turning process like cutting speed ([Formula: see text]), feed ([Formula: see text]), and depth of cut ([Formula: see text]) on surface roughness (Ra) of turning AA7075 filled with 10[Formula: see text]wt.% of TiO2 composite fabricated through stir casting method. Taguchi method and decision tree (DT) algorithm were utilized to foresee the surface roughness (Ra) of the proposed composite. The microstructure of composite was ensured with the presence of TiO2 particles dispersed in a homogeneous manner within the matrix material. The machining of composite was carried out by using the CNC turning center and tungsten carbide insert as tool material. This experimental work was designed on L27 (33) orthogonal array using Taguchi’s design of experiments. From its signal-to-noise (S/N) ratio study, the minimum surface roughness (Ra) was obtained at the optimum level of parameters with the cutting speed at 1500[Formula: see text]rpm, feed at 0.15[Formula: see text]mm/rev and depth of cut at 0.3[Formula: see text]mm. Analysis of variance (ANOVA) and decision tree (DT) algorithm were used to identify the significant effect of parameters. The experimental result shows that depth of cut was the major significant parameter on surface roughness (Ra) when compared to cutting speed and feed.

scholarly journals Optimization and Modeling of Process Parameters in Multi-Hole Simultaneous Drilling Using Taguchi Method and Fuzzy Logic Approach

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 680 ◽  
Author(s):  
Muhammad Aamir ◽  
Shanshan Tu ◽  
Majid Tolouei-Rad ◽  
Khaled Giasin ◽  
Ana Vafadar
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Taguchi Method ◽  
Process Parameters ◽  
Cutting Speed ◽  
Hole Drilling ◽  
Hole Size ◽  
Drilling Parameters ◽  
Fuzzy Logic Approach ◽  
Logic Approach

In industries such as aerospace and automotive, drilling many holes is commonly required to assemble different structures where machined holes need to comply with tight geometric tolerances. Multi-spindle drilling using a poly-drill head is an industrial hole-making approach that allows drilling several holes simultaneously. Optimizing process parameters also improves machining processes. This work focuses on the optimization of drilling parameters and two drilling processes—namely, one-shot drilling and multi-hole drilling—using the Taguchi method. Analysis of variance and regression analysis was implemented to indicate the significance of drilling parameters and their impact on the measured responses i.e., surface roughness and hole size. From the Taguchi optimization, optimal drilling parameters were found to occur at a low cutting speed and feed rate using a poly-drill head. Furthermore, a fuzzy logic approach was employed to predict the surface roughness and hole size. It was found that the fuzzy measured values were in good agreement with the experimental values; therefore, the developed models can be effectively used to predict the surface roughness and hole size in multi-hole drilling. Moreover, confirmation tests were performed to validate that the Taguchi optimized levels and fuzzy developed models effectively represent the surface roughness and hole size.

scholarly journals Effect of Minimum Quantity Lubrication on Surface Roughness and Temperature in Milling of EN31 Steel for Die Making

2019 ◽  
Vol 69 (1) ◽  
pp. 61-68
Author(s):  
Bhosetty Keerthana ◽  
Gurram Vijaya Kumar ◽  
Kumba Anand Babu
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Tool Material ◽  
Minimum Quantity Lubrication ◽  
Depth Of Cut ◽  
Swarm Optimization ◽  
Minimum Quantity ◽  
Particle Swarm Optimization Technique

AbstractMinimum Quantity Lubrication has enormous influence on the process parameters in machining. The main aim of the present work is to study the effects of spindle speed, depth of cut, tool material, amount of coolant dispensed and type of coolant on surface roughness and tool temperature in EN31 steel die making including Minimum Quantity Lubrication (MQL) by introducing a self-designed MQL setup and to optimize the responses using fuzzy-logic and Particle Swarm Optimization technique.

scholarly journals Machinability, Modelling and Statistical Analysis of In-Situ Al–Si–TiB2 Composites

2019 ◽  
Vol 3 (1) ◽  
pp. 28 ◽  
Author(s):  
Jimmy Karloopia ◽  
Shaik Mozammil ◽  
Pradeep Jha
Keyword(s):  
Surface Roughness ◽  
Statistical Analysis ◽  
Tool Wear ◽  
Cutting Forces ◽  
Elevated Temperatures ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Conventional Machining

Aluminum and its alloys have numerous applications in manufacturing, aerospace, and automotive industries. At elevated temperatures, they start to fail in fulfilling their roles and functions. Aluminum-based metal matrix composites (MMCs) are good alternatives for metal and alloys due to their excellent properties. However, the conventional machining of several composites shows complications for a number of reasons, such as high tool wear, poor surface roughness, high machining cost, cutting forces, etc. Numerous studies have already been conducted on the machinability of various MMCs, but the machinability of Al–Si–TiB2 composite is still not well studied. It is of utmost importance that several process parameters of conventional machining are precisely controlled as well as optimized. In this study an effort was made to optimize input parameters such as cutting speed, depth of cut, and feed to obtain well-finished final components with the minimum cutting force and tool wear. These progressions are involved with multiple response characteristics, therefore the exploration of an appropriate multi-objective optimization technique was indeed essential. The performance characteristics of cutting forces and surface roughness were considered for optimization of the machining parameters. Analysis of variance (ANOVA) was employed for the optimization and statistical analysis.

Experimental investigation of machinability characteristics in Al-TiB2 metal matrix composite (MMC) based on the Taguchi method with fuzzy logics

2016 ◽  
Vol 12 (1) ◽  
pp. 177-193 ◽  
Author(s):  
M.P. Jenarthanan ◽  
A. Ram Prakash ◽  
R. Jeyapaul
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Taguchi Method ◽  
Metal Matrix ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Fuzzy Logics ◽  
Content Type ◽  
Five Factors

Purpose – The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input machining parameters (cutting speed, feed rate, helix angle, depth of cut and wt% on the responses in milling of aluminium-titanium diboride metal matrix composite (MMC) with solid carbide end mill cutter coated with nano-crystals. Design/methodology/approach – Taguchi OA is used to optimise the material removal rate (MRR) and Surface Roughness by developing a mathematical model. End Milling is used to create slots by combining various input parameters. Five factors, three-level Taguchi method is employed to carry out the experimental investigation. Fuzzy logic is used to find the optimal cutting factors for surface roughness (Ra) and MRR. The factors considered were weight percentage of TiB2, cutting speed, depth of cut and feed rate. The plan for the experiments and analysis was based on the Taguchi L27 orthogonal array with five factors and three levels. MINITAB 17 software is used for regression, S/N ratio and analysis of variance. MATLAB 7.10.0 is used to perform the fuzzy logics systems. Findings – Using fuzzy logics, multi-response performance index is generated, with which the authors can identify the correct combination of input parameters to get higher MRR and lower surface roughness value with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained. Originality/value – Machinability characteristics in Al-TiB2 MMC based on the Taguchi method with fuzzy logic has not been analysed previously.

scholarly journals Optimization of Machining Parameters on 7075 Aluminum Alloy using Taguchi and ANOVA for Surface Roughness

2020 ◽  
Vol 8 (5) ◽  
pp. 90-95
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Electrical Discharge Machining ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Anova Analysis ◽  
Tool Wear Rate ◽  
Made In

Optimization of the parameter to provide best solution to reduce the tool wear , surface roughness, cutting forces presented using optimization technique .In present work an experimental study is made. In this Taguchi design of experiment methodology for optimization of parameters on 7075Aluminium alloy using tungsten coated electrode . Experiments were conducted based on L27 standard orthogonal array with three processes parameters are cutting speed, feed, depth of cut . Electrical discharge machining is generally calculated on the basis of Surface Roughness (SR),Tool wear rate (TWR) and cutting force (CF) .The ANOVA(Analysis Of Variance) is used to study the performance characteristics in turning operation . ANOVA placed an important role for producing higher roughness . Finally the software ,MINITAB 17 was used and results obtained

scholarly journals Effect of Machining Parameters on Tool Wear and Surface Roughness In Dry and Wet Machining of Aisi 316 Austenitic Stainless Steel by Taguchi Method

2019 ◽  
Vol 8 (9) ◽  
pp. 3292-3298
Keyword(s):  
Surface Roughness ◽  
Wear Rate ◽  
Taguchi Method ◽  
Cutting Speed ◽  
Dimensional Accuracy ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Response Factors ◽  
Aisi 316 ◽  
Optimization Of Cutting Parameters

Stainless steels are widely used to manufacture mechanical components due to excellent mechanical properties. Machining is considered as one of the most critical manufacturing processes in mechanical industry to produce desired shapes and dimensional accuracy of the components. It also affects the performance of the components in its functional requirement. This paper deals with the optimization of cutting parameters in machining operation for AISI 316 austenitic steel with dry and wet environment conditions. The chosen machining parameters in this research are cutting speed, feed rate, and depth of cut as input variables, whereas the response factors are surface roughness and wear rate. Taguchi method with the L9 orthogonal array was used to analyze the process parameters based in dry and wet machining conditions. The Taguchi approach provides the best setting with lower values of surface roughness and wear rate. The regression analysis is performed to obtain a mathematical model of responses in terms of the process parameter. The composite regression optimization gives best setting for dry condition (cutting speed 173 rpm, feed 0.25 mm/rev, and 0.87 mm of the depth of cut) and for wet condition (cutting speed 173 rpm, feed 0.3 mm/rev, and 0.57 mm of the depth of cut). The results show that surface roughness and wear rate are lower in the wet environment than the dry environment.

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