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Öğe Comparison with Experimental Results of Models and Modelling with Fuzzy Logic of the Effect on Surface Roughness of Cutting Parameters in Machining of Co28Cr6Mowrought Steels(IOP PUBLISHING LTD, 2017) Asilturk, Ilhan; AlperInce, MehmetThis study includes comparison with experimental results of models and modelling with fuzzy logic of the effect on surface roughness of cutting parameters (rotational speed (n), feed rate (f), depth of cut (a) and tool tip radius (r)) in CNC turning of Co28Cr6Mo wrought steels. Fuzzy logic models were established that can determine the optimum rotational speed, feed rate, depth of cut and tool tip radius for surface roughness (Ra) according to the hardness of material and type of cutting tool. In the model created using fuzzy logic, membership functions and foot widths of input parameters and output parameter were utilized. In the rule base, triangular (trim-f) membership functions were selected by the Mamdani approach. The results obtained using this fuzzy model and experimental results were interpreted and compared with 2 dimensional graphics.Öğe Determining the optimum process parameter for grinding operations using robust process(KOREAN SOC MECHANICAL ENGINEERS, 2012) Neseli, Suleyman; Asilturk, Ilhan; Celik, LeventWe applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine optimum parameters for minimum surface roughness (Ra) and vibration (Vb) in external cylindrical grinding. First, an experiment was conducted in a CNC cylindrical grinding machine. The TM using L (27) orthogonal array was applied to the design of the experiment. The three input parameters were workpiece revolution, feed rate and depth of cut; the outputs were vibrations and surface roughness. Second, to minimize wheel vibration and surface roughness, two optimized models were developed using computer-aided single-objective optimization. The experimental and statistical results revealed that the most significant grinding parameter for surface roughness and vibration is workpiece revolution followed by the depth of cut. The predicted values and measured values were fairly close, which indicates (R (Ra) (2) =94.99 and R (Vb) (2) =92.73) that the developed models can be effectively used to predict surface roughness and vibration in the grinding. The established model for determination of optimal operating conditions shows that a hybrid approach can lead to success of a robust process.Öğe Effects of Cutting Tool Parameters on Vibration(E D P SCIENCES, 2016) Ince, Mehmet Alper; Asilturk, IlhanThis paper presents of the influence on vibration of Co28Cr6Mo medical alloy machined on a CNC lathe based on cutting parameters (rotational speed, feed rate, depth of cut and tool tip radius). The influences of cutting parameters have been presented in graphical form for understanding. To achieve the minimum vibration, the optimum values obtained for rpm, feed rate, depth of cut and tool tip radius were respectively, 318 rpm, 0.25 mm/rev, 0.9 mm and 0.8 mm. Maximum vibration has been revealed the values obtained for rpm, feed rate, depth of cut and tool tip radius were respectively, 636 rpm, 0.1 mm/rev, 0,5 mm and 0.8 mm.Öğe An intelligent system approach for surface roughness and vibrations prediction in cylindrical grinding(TAYLOR & FRANCIS LTD, 2012) Asilturk, Ilhan; Tinkir, Mustafa; El Monuayri, Hazim; Celik, LeventThis work aims to develop an adaptive network-based fuzzy inference system (ANFIS) for surface roughness and vibration prediction in cylindrical grinding. The system uses a piezoelectric accelerometer to generate a signal related to grinding features and surface roughness. To accomplish such a goal, an experimental study was carried out and consisted of 27 runs in a cylindrical grinding machine operating with an aluminium oxide grinding wheel and AISI 8620 steel workpiece. The workpiece speed, feed rate and depth of cut were used as an input to ANFIS, which in turn outputs surface roughness (Ra) and vibration (a(z)). Different neuro-fuzzy parameters were adopted during the training process of the system in order to improve online monitoring and prediction. Experimental validation runs were conducted to compare the measured surface roughness values with the values predicted online. The comparison shows that the gauss-shaped membership function achieved an online prediction accuracy of 99%.Öğe Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Asilturk, Ilhan; Cunkas, MehmetMachine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Noncontact Surface Roughness Measurement Using a Vision System(SPIE-INT SOC OPTICAL ENGINEERING, 2015) Kocer, Erdinc; Horozoglu, Erhan; Asilturk, IlhanSurface roughness measurement is one of the basic measurement that determines the quality and performance of the final product. After machined operations, tracer end tools are commonly used in industry in order to measure the surface roughness that occurred on the surface. This measurement technique has disadvantages such as user errors because it requires calibration of the device occurring during measurement. In this study, measuring and evaluation techniques were conducted by using display devices over surface image which occurred on the processed surfaces. Surface measurement which performed by getting image makes easier measurement process because it is non-contact, and does not cause any damage. Measurement of surface roughness, and analysis was conducted more precise and accurate. Experimentally obtained results of the measurements on the parts in contact with the device is improved compared with the results of the non- contact image processing software, and satisfactory results were obtained.Öğe ON-LINE A PREDICTIVE MODEL OF CUTTING FORCE IN TURNING WITH 3 AXIS ACCELERATION TRANSDUCER USING NEURAL NETWORK(AMER SOC MECHANICAL ENGINEERS, 2009) Asilturk, Ilhan; Yilmaz, NihatIn this study cutting forces prediction was modeled using back propagation (BP) neural network algorithm. Experimental turning dataset is used in this study to train and evaluate the model. The Input dataset includes speed, feed rate, depth of cut, vibration levels along the three axes on tool holder (ax,ay,az). The Output dataset includes feed force, vertical force, and radial force. Marginally acceptable results were given by early experiments of this study and when data was examined, high non-linearity can be seen from the prepared graphic.. In the previous work, a fine development of reliability of predicting the cutting forces can be observed by the help of results. To compare the estimated results of cutting force from this method with the cutting force signal can be measured directly by dynamometer; it is found that the difference between measured and estimated cutting forces is less than 0.2% in all case.Öğe Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods(ELSEVIER SCI LTD, 2016) Asilturk, Ilhan; Neseli, Suleyman; Ince, Mehmet AlperThis study involves modelling of experimental data of surface roughness of Co28Cr6Mo medical alloy machined on a CNC lathe based on cutting parameters (spindle rotational speed, feed rate, depth of cut and tool tip radius). In order to determine critical states of the cutting parameters variance analysis (ANOVA) was applied while optimisation of the parameters affecting the surface roughness was achieved with the Response Surface Methodology (RSM) that is based on the Taguchi orthogonal test design. The validity of the developed models necessary for estimation of the surface roughness values (Ra, Rz), was approximately 92%. It was found that for Ra 38% of the most effective parameters is on the tool tip radius, followed by 33% on the feed rate whereas for Rz tool tip radius occupied 43% with the feed being at 33% rate. To achieve the minimum surface roughness, the optimum values obtained for spindle rpm, feed rate, depth of cut and tool tip radius were respectively, 318 rpm, 0.1 mm/rev, 0.7 mm and 0.8 mm. (C) 2015 Elsevier Ltd. All rights reserved.Öğe Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression(SPRINGER LONDON LTD, 2012) Asilturk, IlhanIn this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R (a)) and total (R (t)) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R (a) and R (t) values for each case are measured. Input-output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.Öğe Regression Modeling of Surface Roughness in Grinding(TRANS TECH PUBLICATIONS LTD, 2011) Asilturk, Ilhan; Celik, Levent; Canli, Eyub; Onal, GurolGrinding is a widely used manufacturing method in state of art industry. By realizing needs of manufacturers, grinding parameters must be carefully selected in order to maintain an optimum point for sustainable process. Surface roughness is generally accepted as an important indicator for grinding parameters. In this study, effects of grinding parameters to surface roughness were experimentally and statistically investigated. A complete factorial experimental flow was designed for three level and three variable. 62 HRC AISI 8620 cementation steel was used in grinding process with 95-96% Al2O3 grinding wheel. Surface roughness values (Ra, Rz) were measured at the end of process by using depth of cut, feed rate and workpiece speed as input parameters. Experimental results were used for modeling surface roughness values with linear, quadric and logarithmic regressions by the help of MINITAB 14 and SPSS 16 software. The best results according to comparison of models considering determination coefficient were achieved with quadric regression model (84.6% for Ra and 89% for Rz). As a result, a reliable model was developed in grinding process which is a highly complex machining operation and depth of cut was determined as the most effective parameter on grinding by variance analysis (ANOVA). Obtained theoretical and practical acquisitions can be used in various areas of manufacturing sector in the future.
