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![Fig.1: Steps in Response Surface Methodology (RSM). [13]](profile/Ranganath-Singari/publication/279061190/figure/fig1/AS:391915968188416@1470451456886/Steps-in-Response-Surface-Methodology-RSM-13_Q320.jpg)
![Fig.2: To generate contour surface [5]](profile/Ranganath-Singari/publication/279061190/figure/fig2/AS:391915972382720@1470451457088/To-generate-contour-surface-5_Q320.jpg)
Due to the widespread use of highly automated machine tools in the metal cutting industry, Manufacturing requires highly reliable models and methods for the prediction of output performance (surface roughness) in the machining process. In this study prediction model of surface roughness has been developed for turning EN-8 steel with uncoated carbid...
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... The screening study, as shown in Figure 4, consists of the identification of the problem, the determination of the response variable (s), the determination of input variables, and the decision of factor levels ( Figure 4; Ranganath, 2015). After the determination of the response (s) and its input variables, the level of influence of each of the variables on the response(s) is estimated using linear and quadratic models such as full factorial or fractional factorial designs (Olivero et al., 1998). ...
This paper aimed at reviewing current researches on the use of Response Surface Methodology in the optimisation of Biogas processes. It explored the performance of RSM in biogas process optimization, the most effective technique and the attendant effective software used in such processes. It attempted to review literature in the area. 55 articles were systematically reviewed. The online databases included were Google Scholar, Scopus and other statistics-based optimization research databases with keywords from Response Surface Methodology in Biogas Optimization. The review finds that RSM proves to be an effective statistical tool. It has achieved optimum objectives for biogas production: increased biodegradability, optimum biogas yield and methane production, increased Total Solid and reduced Volatile Solids and an increased COD removal. The key advantage of RSM was found to be a reduced number of experimental trials, making it time and cost-effective. 37 process parameters have been optimised using RSM, over the last two decades. Five of these parameters are dominant. Namely,: Temperature, pH, Retention time, Pre-treatment and Loading rate. The major challenges associated with the use of RSM in biogas production process optimization are the limited experimental range. Techniques to combine RSM with other optimization methods such as the Taguchi, Kriging or the Artificial Neural Network (ANN) are being developed to address these challenges. Design Expert software is the most used software because of its low cost of use. However, Statistica offers a better efficiency.
... Other than the Taguchi method, the response surface methodology (RSM) is a mathematical and statistical techniques collection that are useful in modelling and problem analysing in which a response of interest is influenced by several variables and the objective is to optimize this response [11]. Thus, in a turning process, RSM is capable to solve multivariable equations that relate the dependent factors (or responses such as cutting force, tool life, surface roughness and power consumption) with independent parameters (input variables of cutting speed, depth of cut and feed rate) [25]. Besides effectively overcome the limitations of the Taguchi method in failing to test all the interacting factor impacts, identifying non-linear relationships, and formulating regression equations, RSM also contributes to cost and time-saving by reducing the number of trials. ...
Turning process is one of the most important process in conventional machining process which using single-point cutting tool to remove unwanted materials however hybrid of multi-point cutting tool also available. This paper reviews the independent variable of turning parameters toward various dependent variables. From previous studies on AISI 1045 steel, it was found that cutting speed was the most independent variable applied followed by feed rate, depth of cut, side cutting edge angle and tool nose radius. Meanwhile, surface roughness, material removal rate and tool wear were the most output dependent variables. Others output dependent variables found in the research studied were cutting force, feed force, tool tip temperature, power consumption, chip morphology, specific cutting energy, surface integrity and sustainability aspects. Taguchi method, RSM and full factorial method were the most optimization approached in design of experiment. From the review, it was found that the range of independent variables studied by most previous researchers for cutting speed from 100 to 250 m/min, feed rate from 0.10 to 0.25 mm/rev and depth of cut ranges from 0.25 to 0.75 mm. Thus, the enlarge range of the independent variables can be suggested for future experimental work.
... Moreover, number of studies have been performed in terms of tool nose radius besides main parameters [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Effect of the tool's radius were studied during the machining results for tough materials [23,24]. ...
... The surface finish decreased by increasing speed and reducing by enhanced nose radius. Ranganath et al. [27] predicted the surface finish in cutting EN8 steel by uncoated carbide inserts with RSM. Second order model indicated a quite good result for predicted and measured surface finish. ...
This experimantel study describes the development of surface roughness model with main parameters including tool radius using full-factorial design approach and artificial neural network (ANN). Cutting tests and analysis of variance were used in cutting AISI 4140 steels by coated cutting tools. Factorial design/multi quadratic regression (MQR) were compared to ANN model. The results indicated that surface finish decreased with decreasing feed rate and increasing nose radius. It is showed that both feed rate and tool nose radius were effective while other factors were insignificant effect. For testing stage of both methods, data was selected randomly from the existing experimental runs. Further, both randomly selected ANN and MQR indicated no significant differences for prediction the surface roughness because PE and RMSE were 2.73%, 2.21%, 0.063 and 0.046 for MQR and ANN, respectively. Both approaches can used effectively for prediction of any machinability studies in manufacturing engineering due to high accuracy of results. In the future work, other nonlinear models like support vector machine and principal component analysis would be conducted to improve performance accuracy.KeywordsSurface finishAlloy steelCoated carbide toolsCuttingFull factorial designANN
... These parameters have a great influence on the turning input parameters, and these parameters will greatly affect the response variables. In CNC turning, various response variables such as surface roughness, material removal rate, chipping method, etc. participate [13,14]. ...
Due to its high precision, productivity, and surface quality, computer numerical control turning (CNC) is a desirable processing tool in the traditional processing area. CNC machining procedures have a huge number of process parameters, making it challenging to find the best combination of parameters for increased accuracy. In this research work, the Taguchi method and ANOVA were used to study the effects of CNC machining parameters in EN8 steel turning: Surface roughness (Ra) value of component affected due to cutting speed, depth of cut and feed rate. Three-level three-parameter experimental design, using Minitab 17 software using L9 orthogonal array, using coated carbide insert cutting tools, using signal-to-noise ratio (S/N) to study the performance characteristics of EN8 steel turning. In this study, statistical approaches such as the signal-to-noise ratio (S/N ratio) and analysis of variance (ANOVA) were used to explore the effects of cutting speed, depth of cut, and feed rate on surface roughness. Nature-inspired algorithms play a vital role in solving real life. In this study, the bat algorithm can be used to predict the optimal surface value (Ra) and process parameters. Verify the results by conducting confirmation experiments. The current research shows that the feed rate is the most important factor affecting the surface roughness (Ra) of EN8 steel turning.
... Response Surface Methodology (RSM) is a widley used statistical and mathematical techniques for modeling, developing, improving and optimizing processes. Using the method, it is possible to predict the relationship, interaction between the values of a measured response variable and the values of a set of experimental factors that affect the response, predict the response value under different process conditions and find the values of the factors that produces the best value or values of the response (Ranganath et al., 2015). ...
... The work done by brake system parts manufacturers tells that cost mold brake master cylinder made of cast iron was used universally in all the old car and light trucks and after that there has been increased research done on improving the mileage of the vehicle by reducing the weight. The research made a way to concentrate on reducing the weight of brake master cylinder by changing the materials [27,28]. ...
Braking system is a means of converting momentum into heat energy by creating friction in the wheel brakes. The braking system which works with the help of hydraulic principles is known as hydraulic braking systems. The most frequently used system operates hydraulically, by pressure applied through a liquid. These are the foot operated brakes that the driver normally uses to slow or stop the car. Our special interest in hydraulics is related to the actions in automotive systems that result from pressure applied to a liquid. This is called hydraulic pressure. Since liquid is not compressible, it can transmit motion. A typical braking system includes two basic parts. These are the master cylinder with brake pedal and the wheel brake mechanism. The other parts are the connecting tubing, or brake lines, and the supporting arrangements. The present paper is about designing of Twin master cylinder system for and all-terrain vehicle and doing a feasibility study of its strength using ANSYS. Our work is focused on reducing weight which is one of the factors to increase the efficiency. Reduction in weight and space, due to its compactness. The twin Master cylinder system is a great advancement in braking system for an ATV. 3-D CAD modeling is done using SOLIDWORKS 2017, whereas the analysis of its strength is done using ANSYS.
... Ranganath M S et. al [35] had predicted the surface roughness model for CNC turning of EN 8 steel using response surface methodology. In this prediction model of surface roughness had been developed for turning EN-8 steel with uncoated carbide inserts using response surface methodology. ...
The work and study presented in this paper aims to investigate the effect of nose radius on surface roughness, in CNC turning of Aluminium (6061) in dry condition. The effect of cutting conditions (speed, feed and depth of cut) and tool geometry (nose radius) on surface roughness were studied and analysed. Design of Experiments (DOE) were conducted for the analysis of the influence of the turning parameter on the surface roughness by using Response Surface Methodology (RSM) and then followed by optimization of the results using Analysis of Variance (ANOVA) to minimize surface roughness. The nose radius was identified as the most significant parameter. Surface roughness value decreased with increase in nose radius .
... From the ANOVA it was observed that feed had maximum significance in case of R a and R z . Ranganath M S et al. [10] developed a prediction model of surface roughness for turning EN-8 steel with uncoated carbide inserts using Response Surface Methodology. The model was developed in the form of multiple regression equations correlating dependent parameter surface roughness with cutting speed, feed rate and depth of cut, in a turning process. ...
Surface roughness is of great importance for product quality and its function in manufacturing industries; also it is a common indicator of the quality characteristics for machining processes. The effect of surface roughness has been analyzed using Taguchi and Response Surface Methodology (RSM). L27 Orthogonal array has been employed for analysis. The results revealed that feed is the most significant factor in minimizing the surface roughness followed by depth of cut and then speed. Response surface methodology is a better tool for optimization and can better predict the effect of parameters on response.
... From the ANOVA the feed had maximum significance in case of Ra and Rz. Ranganath M S et al. [15] developed a prediction model of surface roughness for turning EN-8 steel with uncoated carbide inserts using Response Surface Methodology (RSM). A multiple regression model was developed in the form of correlating dependent parameter surface roughness with cutting speed, feed rate and depth of cut, in a turning process. ...
Abstarct : Surface Roughness is one of the major attributes that define and evaluate the quality characteristics of the product after it is machined. Consequently, it is an important criterion of determining the quality of the product post machining processes and analysis of the response becomes important. The paper delves the parameters that affect the surface roughness in CNC Turning of Aluminum 6061.The process factors taken are rake angle, nose radius, cutting speed, feed rate and depth of cut and the analysis of influence of these parameters is carried out using Taguchi Method. An L27 orthogonal array has been employed to carry out the analysis and the influence of the factors are studied using Analysis of Variance (ANOVA) method. Feed Rate is found to be the most influential and significant factor followed by rake angle of the tool.
High content of free fatty acids in palm oil is undesirable and is unhealthy for direct consumption and limits its quality for the production of some industrial products such as oleochemicals and biofuels. In this study, response surface methodology (RSM) was used to optimize the free fatty acids adsorption onto a silica-smectite composite for the first time. The regression model was found to be able to predict more than 99% of the targeted response variation in the studied range (R2 = 99.31%). ANOVA test was used to evaluate the accuracy of the regression model, and it was found to predict more than 99% of the response variation. The Pareto charts of standardized effects showed that almost all the explanatory factors were influential. The factorial plots evidenced that time/temperature interaction was most effective and the corresponding contour and surface plots were drawn. The second-order kinetic model was better followed with the highest value of the determination coefficient (R2 = 0.996) and the lowest value of root mean square deviation of 0.555. Response optimizer function showed an optimum for FFAs removal of more than 90% (27.18 mg of KOH/g) at the adsorbent dosage of 2%, contact time of 60 min, and temperature of 90 °C. A good correlation between the predicted and experimental values was observed within reasonable limits. RSM provided a facile mean to optimize the process compared to some classical methods of optimization. Compared with other silica-based adsorbents, the silica-smectite composite is more suitable and efficient for the removal of FFAs from palm oil.
