Optimization of Process Parameters Using Taguchi Coupled Genetic Algorithm

2017 ◽  
pp. 67-93 ◽  
Author(s):  
Supriyo Roy ◽  
J. Paulo Davim ◽  
Kaushik Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Nsga Ii ◽  
Optimization Of Process Parameters ◽  
Cnc Lathe

In the era of mass manufacturing, Material removal rate and Surface Roughness are of primary concern even in manufacturing using contemporary CNC machines. In this work, L27 Orthogonal Array of Taguchi method is selected for three parameters (Depth of cut, Feed and Speed) with three different levels to optimize the turning parameters for Material Removal Rate and Surface Roughness on an EMCO Concept Turn 105 CNC lathe for machining SAE 1020 material with carbide tool. The MRR and SR are observed as the objective to develop the combination of optimum cutting parameters. The objectives were optimized using Taguchi, Grey Taguchi and NSGA-II. The result from these techniques was compared to identify the optimal values of cutting parameters for maximum MRR, minimum SR and best combination of both. This study also produced a predictive equation for determining MRR and SR for a given set of parameters outside the considered values. Thus, with the proposed optimal parameters it is possible to increase the efficiency of machining process and decrease production cost in CNC Lathe.

scholarly journals Multi-Objective Process Parameter Optimization for Surface Roughness and Material Removal Rate in Face Milling of Al 6061 T6 Alloy

2019 ◽  
Vol 9 (2) ◽  
pp. 137-142
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Face Milling ◽  
Machining Process ◽  
Depth Of Cut ◽  
Al 6061

This study uses Taguchi methodology and Gray Relational Analysis approach to explore the optimization of face milling process parameters for Al 6061 T6 alloy.Surface Roughness (Ra), Material Removal Rate (MRR) has been identified as the objective of performance and productivity.The tests were performed by selecting cutting speed (mm / min), feed rate (mm / rev) and cutting depth (mm) at three settings on the basis of Taguchi's L9 orthogonal series.The grey relational approach was being used to establish a multiobjective relationship between both the parameters of machining and the characteristics of results. To find the optimum values of parameters in the milling operation, the response list and plots are used and found to be Vc2-f1-d3. To order to justify the optimum results, the confirmation tests are performed.The machining process parameters for milling were thus optimized in this research to achieve the combined goals such as low surface roughness and high material removal rate on Aluminum 6061 t6.It was concluded that depth of cut is the most influencing parameter followed by feed rate and cutting velocity.

scholarly journals Optimization of Turning Parameter of Composite Materials Using Response Surface Method in Minitab

2021 ◽  
Author(s):  
S. S Kulkarni ◽  
Sarika Sharma
Keyword(s):  
Surface Roughness ◽  
Composite Material ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Research Work ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Rsm Optimization

This paper represents the optimization method utilized in machining process for figuring out the most advantageous manner design. Typically, the technique layout parameters in machining procedures are noticeably few turning parameters inclusive of reducing velocity, feed and depth. The optimization of speed, feed depth of cut is very tough because of several other elements associated with processing situations and form complexities like surface Roughness, material removal rate (MRR) that are based Parameters. On this task a new fabric glass fibre composite is introduced through which could lessen costing of manufacturing and time and additionally it will boom the technique of productiveness. Composite substances have strength, stiffness, light weight, which gives the large scope to engineering and technology. The proposed research work targets to analyze turning parameters of composite material. The machining parameters are very important in manufacturing industries. The present research work is optimized surface roughness of composite material specifically in turning procedure with the aid of changing parameter including intensity of reduce, slicing velocity and feed price and additionally expect the mechanical houses of composite material. The RSM optimization is important because it evaluates the effects of multiple factors and their interactions on one or more responsive variables. It is observed that the material removal rate increases and surface roughness decreases as per the increase of Spindle speed and feed rate.

Study of the Cutting Parameters on Surface Roughness and Material Removal Rate in Hard Turning of UHMWPE

2020 ◽  
Author(s):  
César Oswaldo Aguilera-Ojeda ◽  
Alberto Saldaña-Robles ◽  
Agustín Vidal-Lesso ◽  
Israel Martínez-Ramírez ◽  
Eduardo Aguilera-Gómez
Keyword(s):  
Surface Roughness ◽  
Predictive Model ◽  
Material Removal Rate ◽  
Surface Finish ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Turning Process

Abstract The surface finish of industrial components has an important role in their performance and lifetime. Therefore, it is crucial to find the cutting parameters that provide the best surface finish. In this work, an experimental study of the effect of cutting parameters on ultra-high molecular weight polyethylene (UHMWPE) by a turning process was carried out. Today, the UHMWPE polymer continues to find applications mainly in the automotive industry and biomechanics because it is resistant to impact and corrosive materials to use. A face-centered Central Composite Design (CCD) and Response Surface Methodology (RSM) were applied to evaluate the influence of the cutting speed (Vc), feed rate (f) and depth of cut (ap) of the turning operation on the Average Surface Roughness (Ra) and Material Removal Rate (MRR). Results allowed obtaining an adjusted multivariable regression model that describes the behavior of the Ra that depends on the cutting parameters in the turning process. The predictive model of Ra showed that it fits well with a correlation coefficient (R2) around 0.9683 to the experimental data for Ra. The ANOVA results for Ra showed that the feed is the most significant factor with a contribution of 42.3 % for the term f 2, while the speed and depth of cut do not affect Ra with contributions of 0.19% and 0.18%, respectively. A reduction of feed from 0.30 to 0.18 mm·rev−1 produces a decrease in surface roughness from 6.68 to 3.81 μm. However, if the feed continued to reduce an increase in surface roughness, a feed of 0.05 mm·rev−1 induces a surface roughness of 14.93 μm. Feeds less than 0.18 mm·rev−1 cause a heat generation during turning that increases the temperature in the process zone, producing surface roughness damage of the UHMWPE polymer. Also, the results for MRR demonstrated that all of the cutting parameters are significant with contributions of 31.4%, 27.4% and 15.4% to feed, speed, and depth of cut, respectively. The desirability function allowed optimizing the cutting parameters (Vc = 250 m·min−1, ap = 1.5 mm y f = 0.27 mm·rev−1) to obtain a minimum surface roughness (Ra = 4.3 μm) with a maximum material removal rate (MMR = 97.1 cm3·min−1). Finally, the predictive model of Ra can be used in the industry to obtain predictions on the experimental range analyzed, reducing the surface roughness and the manufacturing time of UHMWPE cylindrical components.

scholarly journals Multiobjective Optimization of Milling Parameters for Ultrahigh-Strength Steel AF1410 Based on the NSGA-II Method

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jin Xu ◽  
Fuwu Yan ◽  
Yan Li ◽  
Zhenchao Yang ◽  
Long Li
Keyword(s):  
Surface Roughness ◽  
Multiobjective Optimization ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Milling Force ◽  
Nsga Ii ◽  
Milling Parameters ◽  
Ultrahigh Strength Steel

In this paper, ultrahigh-strength steel AF1410 was milled with the carbide tool, and a total of thirty experiments were performed based on central composite design (CCD) of response surface methodology. The prediction models of milling force and surface roughness are established, respectively. The influence of milling parameters (milling speed, each tooth feed, radial depth of cut, and axial depth of cut) on milling force and surface roughness is studied by ANOVA and established prediction model. Multiobjective optimization of milling parameters is accomplished based on nondominated sorting genetic algorithm II (NSGA-II) with milling force, surface roughness, and material removal rate as optimization objectives. The surface roughness, cutting force, and material removal rate are important indexes to measure the energy consumed in the process of product, the surface machining quality, and machining efficiency of processing, respectively. In order to minimize milling force and surface roughness and maximize material removal rate, NSGA-II was used for multiobjective optimization to obtain the optimal fitness value of the objective function. The NSGA-II has been applied to obtain a set of optimal combination of parameters from the Pareto-optimal solution set to enhance the machining conditions.

scholarly journals Application Potential of Fuzzy and Regression in Optimization of MRR and Surface Roughness during Machining of C45 Steel

2019 ◽  
Vol 63 (2) ◽  
pp. 132-139
Author(s):  
Santosh Madival ◽  
Manjunath Lingappa Halappa ◽  
Mohammed Riyaz Ahmed ◽  
Lokesha Marulaiah
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Regression Model ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cryogenic Treatment ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters

In the machining industry, coolant has an important role due to their lubrication, cooling and chip removal functions. Using coolant can improve machining process efficiency, tool life, surface quality and it can reduce cutting forces and vibrations. However, health and environmental problems are encountered with the use of coolants. Hence, there has been a high demand for deep cryogenic treatment to reduce these harmful effects. For this purpose, −196 °C LN2 gas is used to improve machining performance. This study focuses on the prediction of surface roughness and material removal rate with cryogenically treated M2 HSS tool using fuzzy logic and regression model. The turning experiments are conducted according to Taguchi's L9 orthogonal array. Surface roughness and material removal rate during machining of C45 steel with HSS tool are measured. Cutting speed, feed rate, and depth of cut are considered as machining parameters. A model depended on a regression model is established and the results obtained from the regression model are compared with the results based on fuzzy logic and experiment. The effectiveness of regression models and fuzzy logic has been determined by analyzing the correlation coefficient and by comparing experimental results. Regression model gives closer values to experimentally measured values than fuzzy logic. It has been concluded that regression-based modeling can be used to predict the surface roughness successfully.

Effects of Process Parameters on Surface Roughness and MRR in the hard turning of EN 36 steel

2018 ◽  
pp. 102-110
Author(s):  
Amritpal Singh ◽  
Rakesh Kumar
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Material Removal ◽  
Hard Turning ◽  
Control Parameter ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting

In the present study, Experimental investigation of the effects of various cutting parameters on the response parameters in the hard turning of EN36 steel under the dry cutting condition is done. The input control parameters selected for the present work was the cutting speed, feed and depth of cut. The objective of the present work is to minimize the surface roughness to obtain better surface finish and maximization of material removal rate for better productivity. The design of experiments was done with the help of Taguchi L9 orthogonal array. Analysis of variance (ANOVA) was used to find out the significance of the input parameters on the response parameters. Percentage contribution for each control parameter was calculated using ANOVA with 95 % confidence value. From results, it was observed that feed is the most significant factor for surface roughness and the depth of cut is the most significant control parameter for Material removal rate.

Optimization of Cutting Parameters in Milling Process Using Genetic Algorithm and ANOVA (March 2020)

2020 ◽  
Vol 38 (10A) ◽  
pp. 1489-1503
Author(s):  
Marwa Q. Ibraheem
Keyword(s):  
Genetic Algorithm ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Optimal Value ◽  
Optimization Of Cutting Parameters

In this present work use a genetic algorithm for the selection of cutting conditions in milling operation such as cutting speed, feed and depth of cut to investigate the optimal value and the effects of it on the material removal rate and tool wear. The material selected for this work was Ti-6Al-4V Alloy using H13A carbide as a cutting tool. Two objective functions have been adopted gives minimum tool wear and maximum material removal rate that is simultaneously optimized. Finally, it does conclude from the results that the optimal value of cutting speed is (1992.601m/min), depth of cut is (1.55mm) and feed is (148.203mm/rev) for the present work.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Investigation of Surface Roughness and Material Removal Rate for High Speed Micro End Milling on PMMA

2015 ◽  
Vol 1115 ◽  
pp. 12-15
Author(s):  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
Abdul Rahman Mohamed ◽  
Md. Sazzad Hossein Chowdhury
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Micro End Milling

Micro end milling is one of the most important micromachining process and widely used for producing miniaturized components with high accuracy and surface finish. This paper present the influence of three micro end milling process parameters; spindle speed, feed rate, and depth of cut on surface roughness (Ra) and material removal rate (MRR). The machining was performed using multi-process micro machine tools (DT-110 Mikrotools Inc., Singapore) with poly methyl methacrylate (PMMA) as the workpiece and tungsten carbide as its tool. To develop the mathematical model for the responses in high speed micro end milling machining, Taguchi design has been used to design the experiment by using the orthogonal array of three levels L18 (21×37). The developed models were used for multiple response optimizations by desirability function approach to obtain minimum Ra and maximum MRR. The optimized values of Ra and MRR were 128.24 nm, and 0.0463 mg/min, respectively obtained at spindle speed of 30000 rpm, feed rate of 2.65 mm/min, and depth of cut of 40 μm. The analysis of variance revealed that spindle speeds are the most influential parameters on Ra. The optimization of MRR is mostly influence by feed rate. Keywords:Micromilling,surfaceroughness,MRR,PMMA

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