Reducing the Roughness and Sound Intensity by Optimization of Cutting Parameters in Processing of AISI 2714 Steel Material on CNC Milling Machine

Within the scope of this study, optimization of cutting parameters (feed rate, cutting speed and depth of cut) was aimed in order to reduce the noise level that occurs during the processing of AISI 2714 steel on CNC milling machine without compromising the surface roughness. Experimental design was examined in three variables, three levels and two target functions. In order to investigate the contribution of these parameters to the target function, the experiments were carried out in accordance with the experiment plan determined by using the "Central Composite Design (CCD)" of the "Response Surface Method (RSM)". Mathematical models have been developed in order to predict sound intensity and surface roughness by applying regression analysis to the experimental results. As a result, it has been observed that the most effective parameter in reducing the surface roughness is the feed rate, followed by the depth of cut. While the depth of cut was the most effective parameter in reducing the sound intensity, it was determined that the next effective parameter was the feed rate

MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY

In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso- firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate of 100 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.5 mm. Analysis of variances shows that the most influential parameters was feed rate. Afte

MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY

In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso-firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate of 100 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.5 mm. Analysis of variances shows that the most influential parameters was feed rate. After the predicted SR has been obtained by using the two methods, average percentage deviation was calculated, the result obtained using least square approximation method (that is the mathematical expression) show the accuracy of 99% and Response Surface Methodology (RSM) mathematical model shows accuracy of 99.6% which is viable and appropriate in prediction of SR. When either of these models are applied this will enhance the rate of production

Modeling and optimization of surface roughness and vibration amplitude in heat assisted end milling of SKD 11 tool steel using ball nose tool

Tool steel - SKD 11 is frequently used in industries for making dies and molds. This grade is chosen for its toughness, strength, and hardness maintained up to high temperature. However, the same properties make the steel extremely difficult and expensive to machine using conventional approaches. Heat assisted machining has been found wide spread application in recent years to improve machinability of difficult-to-cut materials. This research paper presents the outcome of an investigation on heat assisted end milling of SKD 11 conducted on a vertical machining center using ball nose coated carbide inserts. The Design of Experiments (DoE) was done using the Response Surface Methodology, in order to develop empirical mathematical models of surface roughness and vibration in terms of cutting speed, feed, axial depth of cut, and heating temperature. The models were checked for significance using Analysis of Variance (ANOVA). 3-D response surface graphs of the interactions of primary cutting parameters with the responses were plotted. Optimization was then performed by using the desirability function approach. From the graphs and optimized results it was concluded that the primary input parameters could be controlled in order to reduce vibration amplitude and produce semi-finished machined surfaces applying induction heat assisted technique

Optimization of Dimensional Tolerances and Material Removal Rate in the Orthogonal Turning of AISI 4340 Steel

Turning is one of the most used metal removal operations in the industry. It can remove material faster, giving reasonably good surface quality apart from geometrical requirements. Conformity of geometry is one of the most significant requirements of turned components to perform their intended functions. Apart from dimensional requirements, the important geometrical necessities are Circularity, Straightness, Cylindricity, Perpendicularity, etc. Since they have a direct influence on the functioning of the components, the effect of the cutting parameters on them has greater significance. In this paper experiments are carried out to examine the effect of turning parameters such as cutting speed, feed rate, and depth of cut on responses like; straightness, roundness, surface roughness, and material removal rate during turning of AISI 4340 steel. Analysis of Variance (ANOVA) is performed and the influence of parameters on each response is studied. The optimal values of parameters obtained from the study are further confirmed by conducting experiments

MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN END MILLING OF ALUMINIUM USING LEAST SQUARE APPROXIMATION METHOD AND RESPONSE SURFACE METHODOLOGY

In end milling, accurate setting of process parameters is extremely important to obtained enhanced surface roughness (SR). Due to a recent innovation in mechanization made it possible to produce high quality manufacturing products. The perceptions of quality in mechanical products are their physical look that is the surface roughness (SR). The aim of this research work is to develop mathematical expression (M.E) and mathematical model using least square approximation method and Response Surface Methodology (RMS) to predict the SR for end milling of Al 6061 alloy. The process parameters that were selected as predictors for the SR are Spindle speed (V), axial depth of cut (a), feed rate (f) and radial depth of cut (d). 30 samples of Al 6061 alloy were carried out using SIEG 3/10/0010 CNC machines and each of the experimental result was measured using Mitutoyo surface roughness tester and Presso-firm. The minimum SR of 0.5 μm were obtained at a spindle speed of 2034.608 rpm, feed rate of 100 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.5 mm. Analysis of variances shows that the most influential parameters was feed rate. After the predicted SR has been obtained by using the two methods, average percentage deviation was calculated, the result obtained using least square approximation method (that is the mathematical expression) show the accuracy of 99% and Response Surface Methodology (RSM) mathematical model shows accuracy of 99.6% which is viable and appropriate in prediction of SR. When either of these models are applied this will enhance the rate of production

Analysis and Optimization of Process Parameters in Wire Electrical Discharge Machining Based on RSM: A Case Study

In this book chapter a review and critical analysis on current research trends in wire electrical discharge machining (WEDM) and relation between different process parameters including pulse on time, pulse off time, servo voltage, peak current, dielectric flow rate, wire speed, wire tension on different process responses include material removal rate (MRR), surface roughness (Ra), sparking gap, wire lag and wire wear ration (WWR) and surface integrity factors was investigated. On the basis of critical evaluation of the available literature following conclusions are summarized. In addition, different modeling and optimization methods used in WEDM were discussed and a case study based on response surface method (RSM) including design of experiment (DoE) carried out to find optimal process parameters effect on surface roughness was conducted. In the final part of the present study was presented some recommendations about the trends for future WEDM researches

Turning of polymers: a novel multi-objective approach for parametric optimization

Engineering problems often embodying with multi-response optimization may be confiscatory in nature. Multi-response optimization problems basically correspond to choosing the ‘best’ alternative from a set of available alternatives (where ‘best’ can be interpreted as ‘the most preferred alternative’ from the set of alternative solutions). Manufacturing process often involves optimization of machining parameters in order to improve product quality as well as to enhance productivity. Quality and productivity are two important but contradictory parameters while performing machining operations. Quality mainly concerns on surface roughness of the machined part whereas productivity is directly related to Material Removal Rate (MRR) during machining. As surface finish (roughness average value) is seemed inversely related to MRR, hence it becomes essential to evaluate the optimal cutting parameters setting in order to satisfy contradicting requirements of quality and productivity. The aim of this study is to propose an integrated methodology to state the machining characteristics in order that it may be competitive as regards of productivity and quality. Owing to this issue, in the present reporting two integrated multi-response optimization philosophies viz. (i) PCA coupled with TOPSIS and (ii) utility based fuzzy approach combined with Taguchi framework has been adopted for assessing favorable (optimal) machining condition during the machining of polymers (Nylon and Teflon, as case studies)

Optimizing the Process Parameters for Eco-Friendly Minimum Quantity Lubrication-Turning of AISI 4340 Alloy with Nano-Lubricants Using a Grey Wolf Optimization Approach

Optimization of turning process parameters in minimum quantity lubrication (MQL)-assisted mode is obligatory for enhanced efficiency and product integrity. However, little attention has been paid to analyzing situations where high search precision is needed when evaluating the optimal turning process parameters. This article applies the grey wolf optimization (GWO) approach to optimize the turning of parameters AISI 4340 alloy to enhance cutting force, surface roughness and tool wear. Based on the literature data, turning was conducted with MQL-assisted CuO and Al2O3 nanofluids. The problem was formulated by mimicking six wolves in six different objective functions. The objective functions have the responses as the dependent variables and the parameters including cutting speed, feed and cutting depth as independent variables. The hunting behavior of the wolves as they encircle the prey is interpreted to the machining task optimization. It involves three hierarchically-evaluated guides- the alpha, beta and delta wolves- positioned optimally and other wolves are updated accordingly. The cutting speed, feed and cutting depth are bound in the lower and upper limits as 80 and 140m/min, 0.05 and 0.20m/m/rev and 0.1 and 0.4mm, respectively. The grey wolf optimization algorithm optimizes the parameters to yield the cutting force, surface roughness and tool wear using Al2O3 as 199.50N, -23.54mm and 0.06mm, respectively. For the CuO, the corresponding cutting force, surface roughness and tool wear, the CuO, Al2O3 and CuO nano lubricants produced the best results. However, for mass production, selective use of CuO and Al2O3 should be made. The usefulness of this research endeavor is to help process engineers to make decisions in producing low-cost components in manufacturing