Parametric Optimization of Drilling Parameters in Aluminum 6061T6 Plate to Minimize the Burr (original) (raw)
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The International Journal of Advanced Manufacturing Technology, 2010
This investigation presents the use of Taguchi and response surface methodologies for minimizing the burr height and the surface roughness in drilling Al-7075. The Taguchi method, a powerful tool to design optimization for quality, is used to find optimal cutting parameters. Response surface methodology is useful for modeling and analyzing engineering problems. The purpose of this paper was to investigate the influence of cutting parameters, such as cutting speed and feed rate, and point angle on burr height and surface roughness produced when drilling Al-7075. A plan of experiments, based on L 27 Taguchi design method, was performed drilling with cutting parameters in Al-7075. All tests were run without coolant at cutting speeds of 4, 12, and 20 m/min and feed rates of 0.1, 0.2, and 0.3 mm/rev and point angle of 90°, 118°, and 135°. The orthogonal array, signal-to-noise ratio, and analysis of variance (ANOVA) were employed to investigate the optimal drilling parameters of Al-7075. From the analysis of means and ANOVA, the optimal combination levels and the significant drilling parameters on burr height and surface roughness were obtained. The optimization results showed that the combination of low cutting speed, low feed rate, and high point angle is necessary to minimize burr height. The best results of the surface roughness were obtained at lower cutting speed and feed rates while at higher point angle. The predicted values and measured values are quite close to each other; therefore, this result indicates that the developed models can be effectively used to predict the burr height and the surface roughness on drilling of Al-7075.
Burr formation minimization in drilling process using experimental study with statistical analysis
IJARIIT, 2018
Burrs are generally plastic deformation of the work piece after machining process. Deburring can reduce burr formation but it is time consuming and increase the production cost. By changing some of the input parameters like Spindle Speed, Feed Rate, Depth of Cut, the formation of burr can be reduced. This research work presents an experimental study on minimizing the formation of burr in machining like drilling. In this thesis, the Universal Radial machine has been used to make holes. By changing machining variables like feed, cutting velocity and speed different sizes and the type of the burrs created in aluminium are studied. Taguchi analysis has been done to analyze the predicted minimum burr height. ANOVA has also been done to analyze the maximum contribution of the parameters to form the burr. Signal to Noise(S/N) ratio plots has been shown in this research. Response surface methodology also has been conducted. In the design optimization, the application of RSM is aimed to reduce the cost of the expensive analysis methods (e.g. finite element method or CFD analysis) and their associated numerical noise.
Optimization of Machining Parameters during Drilling of 7075 Aluminium Alloy
Applied Mechanics and Materials, 2013
Drilling is a hole making process on machine components at the time of assembly work, which are identify everywhere. In precise applications, quality and accuracy play a wide role. Nowadays' industries suffer due to the cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a serious topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. The effect of alteration of drill geometry on thrust force and burr size of drilled hole was investigated by the Taguchi design of experiments and found an optimum combination of the most significant input parameters from ANOVA to get optimum reduction in terms of burr size by design expert software. Drill thrust influences more on burr size. The clearance angle of the drill bit causes variation in thrust. The burr height is observed in this study. These output results are compared with the neural network software @easy NN plus. Finally, it is concluded that by increasing the number of nodes the computational cost increases and the error in nueral network decreases. Good agreement was shown between the predictive model results and the experimental responses.
Optimization Drilling Parameters of Aluminum Alloy Based on Taguchi Method
Al-Khwarizmi Engineering Journal, 2019
This paper focuses on the optimization of drilling parameters by utilizing “Taguchi method” to obtain the minimum surface roughness. Nine drilling experiments were performed on Al 5050 alloy using high speed steel twist drills. Three drilling parameters (feed rates, cutting speeds, and cutting tools) were used as control factors, and L9 (33) “orthogonal array” was specified for the experimental trials. Signal to Noise (S/N) Ratio and “Analysis of Variance” (ANOVA) were utilized to set the optimum control factors which minimized the surface roughness. The results were tested with the aid of statistical software package MINITAB-17. After the experimental trails, the tool diameter was found as the most important factor that has effect on the surface roughness. The optimal drilling factors that minimized the surface roughness are (20mm/min cutting speed, 0.2 mm/rev feed rate, and 10mm tool diameter).
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2012
The exit burr in drilling degrades the precision of products and causes additional cost of deburring. Therefore, it is essential to minimize burr size at the exit of holes in drilling at the manufacturing stage. Taguchi's quality loss function approach, a multi-response optimization method, has been employed to determine the best combination values of cutting speed, feed, point angle and lip clearance angle for specified drill diameters to simultaneously minimize burr height and burr thickness during drilling of AISI 316L stainless steel workpieces. The experiments were planned as per L 9 orthogonal array and multi-response signal to noise (S/N) ratio was applied to measure the performance characteristics. Analysis of means (ANOM) and analysis of variance (ANOVA) were performed to determine the optimal levels and to identify the level of importance of parameters. The confirmation tests with the optimal levels of parameters were carried out to illustrate the effectiveness of Taguchi optimization.
IOP Conference Series: Materials Science and Engineering, 2018
In present work, drilling of a through a hole in an aluminium bar has been observed the formation of a burr. The unwanted material raised beyond the work piece called burr. The minimization of the burr is important for manufacturing aspect which reduces cost and increases the life of the product. In this paper drilling on aluminium work piece experimental test has been conducted three parameters drill diameter, Point Angle and spindle speed and each of the parameter three different level (maximum, intermediate and minimum)value has been chosen. The each set of an experiment the burr height and thickness has been measured. The effect of each parameter which reduces the burr height and thickness has been identified. In this paper, artificial neural networks (ANN) model are developed for comparining the experimental results.The ANN modeled values show very close matching with the experimental results.
Optimization of Drilling Parameters using Genetic Algorithms
— Surface roughness in drilling process is important aspect so there is needs to minimize surface roughness while drilling. This paper investigates the influence of drilling parameters such as spindle speed, feed rate and cutting tools on surface roughness while drilling titanium alloy. The experiments have been conducted by employing L27 orthogonal array by considering HSS, Coated HSS and carbide drills. After experimentation second order empirical modelling was done using regression method. Finally, optimization of drilling parameters for surface roughness was done through genetic algorithms. The genetic algorithms give minimum value of surface roughness was 0.0689 corresponding optimize drilling parameters were 576.069, 0.03, and carbide as spindle speed, feed rate and cutting tool respectively.
FPA based optimization of drilling burr using regression analysis and ANN model
Measurement, 2019
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Optimization of Drilling Parameters to Minimize Burr by Providing Back-up Support on Aluminium Alloy
Procedia Engineering, 2014
Drilling a hole usually leaves behind a undesirable burr at the exit work surface. Application of the method suggested by Taguchi is made in this work to minimize drilling burr of an aluminium alloy using HSS drill within the domain of experiments considered. Parameters used are cutting velocity, feed and machining environment. The effect of process variables on burr height is explored, and the optimum condition for minimizing burr height using a back-up support is determined by the analysis. Experimental runs were chosen followingL 27 orthogonal array of Taguchi. Analysis of variance was undertaken to find out the influence of process parameters on the response noted. Predicted values are finally checked for accuracy through a confirmation test. It is found out that back-up support yields much better result than that of normal drilling process. Moderate cutting velocity, low feed and wet condition with water cooling were observed to minimize burr height using a back-up support. Machining environment is found to be the most significant parameter for reducing burr height.
The International Journal of Advanced Manufacturing Technology, 2007
This paper illustrates the methodology of genetic algorithm (GA) based multi-objective drilling process optimization. The optimal values of cutting speed, feed, point angle and lip clearance angle for a specified drill diameter were determined using GA, which simultaneously minimize burr height and burr thickness at the exit of holes during drilling of AISI 316L stainless steel. The burr size models required for GA optimization were developed using response surface methodology (RSM) with drilling experiments planned as per Box-Behnken design. The GA optimization results reveal that point angle has a significant role in controlling the burr size.