Grinding is a widely used manufacturing method in state of art industry. By realizing needs of ma... more Grinding 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.
Expert Systems With Applications an International Journal, May 1, 2011
Machine parts during their useful life are significantly influenced by surface roughness quality.... more Machine 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
Journal of Materials Processing Technology, Mar 1, 2009
In bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with an appropria... more In bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with an appropriate feeding force in order to perform an efficient cutting operation. This can be accomplished by controlling the feed rate and thrust force by accurately detecting the cutting resistance against the bandsaw blade during cutting operation. In this study, a neural-fuzzybased force model for controlling band sawing process was established. Cutting parameters were continuously updated by a secondary neural network, to compensate the effect of environmental disturbances. Required feed rate and cutting speed were adjusted by developed fuzzy logic controller. Results of cutting experiments using several steel specimens show that the developed neural-fuzzy system performs well in real time in controlling cutting speed and feed rate during band sawing. A material identification system was developed by using the measured cutting forces. Materials were identified at the beginning of the cutting operation and cutting force model was updated by using the detected material type. Consequently, cutting speed and feed rate were adjusted by using the updated model. The new methodology is found to be easily integrable to existing production systems.
Journal of Selcuk University Natural and Applied Science, Dec 11, 2013
This study aims to optimize milling parameters depend on the Taguchi method for minimizing surfac... more This study aims to optimize milling parameters depend on the Taguchi method for minimizing surface roughness (Ra). The experiments were conducted using the L18 orthogonal array in a CNC milling machine with respect to three different cutting parameters. These parameters were cutting depth, cutting speed and feed rate. Dry milling tests were performed on hardened AISI 1045 (42HRC) with APTK 1705 LT 30 carbide inserts as a cutting condition in order to reduce optimization parameters such as the coolant. Each experiment was repeated five times and a new insert was used for each test to ensure accurate readings of surface roughness. In addition, the statistical methods of signal to noise ratio (SNR) and analysis of variance (ANOVA) were carried out to investigate the effects of cutting parameters on surface roughness. As a result of the experiment, the feed rate has the most dominant effect on average surface roughness. The effects of interactions of factors appear to be important, especially cutting speed-feed rate pair. These statistical methods can be used in manufacturing industries to optimize the manufacturing processes.
2010 IEEE International Conference on Electro/Information Technology, 2010
In this work, a technique is proposed to predict surface roughness by using neural network. Surfa... more In this work, a technique is proposed to predict surface roughness by using neural network. Surface roughness could be predicted within a reasonable degree of accuracy by taking feed rate, cutting speed, depth of cut and three orthogonal axis (x,y,z) signals of vibrations of tool holder as input parameters. 27 experiments were performed by using a CNC lathe with a carbide cutting tool. Experimental data obtained from turning process were used for training and testing of neural network architecture based prediction system. When experimental and prediction results were compared, it has been seen that a mean accuracy of 91,17% was achieved.
2008 13th International Power Electronics and Motion Control Conference, 2008
In this paper, we propose that a neuro-fuzzy based adaptive controller for control of band sawing... more In this paper, we propose that a neuro-fuzzy based adaptive controller for control of band sawing process. The system composed of two different kinds of back propagation networks and a fuzzy logic controller. The first network accomplishes the reference forces that are compared with the measured cutting force values in the real time. The required feed rate and cutting speed
This study presents a new method to determine multi-objective optimal cutting conditions and math... more This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.
Journal of Mechanical Science and Technology, 2012
We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine ... more We 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 27 L 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 2
International Journal of Computer Integrated Manufacturing, 2012
This work aims to develop an adaptive network-based fuzzy inference system (ANFIS) for surface ro... more This 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
This article proposes for predicting the surface roughness of AISI 1040 steel material using the ... more This article proposes for predicting the surface roughness of AISI 1040 steel material using the artificial intelligent. Cutting speed, feed rate, depth of cut, and nose radius have been taken into consideration as input factors and corresponding surface roughness values (R a , R t) as output. A series of experiments have been carried out in accordance with a full factorial design on the CNC lathe to obtain the data used for the training and testing of an artificial neural network (ANN). The developed MATLAB TM interface was used to predict surface roughness. Multilayer perceptron structure, which is a kind of feed forward ANNs, is applied to model and prediction of the surface roughness in turning operations. The number of iterations used was 20,000. MSE = 1E-4 and R 2 = 0.9991 were achieved using the developed ANN Model. The obtained results indicate that the ANN algorithm coupled with back propagation neural network is an efficient and accurate method in predicting of surface roughness in turning.
In this study, the average surface roughness values obtained when turning AISI 4140 grade tempere... more In this study, the average surface roughness values obtained when turning AISI 4140 grade tempered steel with a hardness of 51 HRC, were modeled using fuzzy logic, artificial neural networks (ANN) and multi-regression equations. Input variables consisted of cutting speed (V), feed ...
This study presents a new method to determine multi-objective optimal cutting conditions and math... more This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicte...
Machine parts during their useful life are significantly influenced by surface roughness quality.... more Machine 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 ...
Grinding is a widely used manufacturing method in state of art industry. By realizing needs of ma... more Grinding 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.
Expert Systems With Applications an International Journal, May 1, 2011
Machine parts during their useful life are significantly influenced by surface roughness quality.... more Machine 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
Journal of Materials Processing Technology, Mar 1, 2009
In bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with an appropria... more In bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with an appropriate feeding force in order to perform an efficient cutting operation. This can be accomplished by controlling the feed rate and thrust force by accurately detecting the cutting resistance against the bandsaw blade during cutting operation. In this study, a neural-fuzzybased force model for controlling band sawing process was established. Cutting parameters were continuously updated by a secondary neural network, to compensate the effect of environmental disturbances. Required feed rate and cutting speed were adjusted by developed fuzzy logic controller. Results of cutting experiments using several steel specimens show that the developed neural-fuzzy system performs well in real time in controlling cutting speed and feed rate during band sawing. A material identification system was developed by using the measured cutting forces. Materials were identified at the beginning of the cutting operation and cutting force model was updated by using the detected material type. Consequently, cutting speed and feed rate were adjusted by using the updated model. The new methodology is found to be easily integrable to existing production systems.
Journal of Selcuk University Natural and Applied Science, Dec 11, 2013
This study aims to optimize milling parameters depend on the Taguchi method for minimizing surfac... more This study aims to optimize milling parameters depend on the Taguchi method for minimizing surface roughness (Ra). The experiments were conducted using the L18 orthogonal array in a CNC milling machine with respect to three different cutting parameters. These parameters were cutting depth, cutting speed and feed rate. Dry milling tests were performed on hardened AISI 1045 (42HRC) with APTK 1705 LT 30 carbide inserts as a cutting condition in order to reduce optimization parameters such as the coolant. Each experiment was repeated five times and a new insert was used for each test to ensure accurate readings of surface roughness. In addition, the statistical methods of signal to noise ratio (SNR) and analysis of variance (ANOVA) were carried out to investigate the effects of cutting parameters on surface roughness. As a result of the experiment, the feed rate has the most dominant effect on average surface roughness. The effects of interactions of factors appear to be important, especially cutting speed-feed rate pair. These statistical methods can be used in manufacturing industries to optimize the manufacturing processes.
2010 IEEE International Conference on Electro/Information Technology, 2010
In this work, a technique is proposed to predict surface roughness by using neural network. Surfa... more In this work, a technique is proposed to predict surface roughness by using neural network. Surface roughness could be predicted within a reasonable degree of accuracy by taking feed rate, cutting speed, depth of cut and three orthogonal axis (x,y,z) signals of vibrations of tool holder as input parameters. 27 experiments were performed by using a CNC lathe with a carbide cutting tool. Experimental data obtained from turning process were used for training and testing of neural network architecture based prediction system. When experimental and prediction results were compared, it has been seen that a mean accuracy of 91,17% was achieved.
2008 13th International Power Electronics and Motion Control Conference, 2008
In this paper, we propose that a neuro-fuzzy based adaptive controller for control of band sawing... more In this paper, we propose that a neuro-fuzzy based adaptive controller for control of band sawing process. The system composed of two different kinds of back propagation networks and a fuzzy logic controller. The first network accomplishes the reference forces that are compared with the measured cutting force values in the real time. The required feed rate and cutting speed
This study presents a new method to determine multi-objective optimal cutting conditions and math... more This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.
Journal of Mechanical Science and Technology, 2012
We applied combined response surface methodology (RSM) and Taguchi methodology (TM) to determine ... more We 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 27 L 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 2
International Journal of Computer Integrated Manufacturing, 2012
This work aims to develop an adaptive network-based fuzzy inference system (ANFIS) for surface ro... more This 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
This article proposes for predicting the surface roughness of AISI 1040 steel material using the ... more This article proposes for predicting the surface roughness of AISI 1040 steel material using the artificial intelligent. Cutting speed, feed rate, depth of cut, and nose radius have been taken into consideration as input factors and corresponding surface roughness values (R a , R t) as output. A series of experiments have been carried out in accordance with a full factorial design on the CNC lathe to obtain the data used for the training and testing of an artificial neural network (ANN). The developed MATLAB TM interface was used to predict surface roughness. Multilayer perceptron structure, which is a kind of feed forward ANNs, is applied to model and prediction of the surface roughness in turning operations. The number of iterations used was 20,000. MSE = 1E-4 and R 2 = 0.9991 were achieved using the developed ANN Model. The obtained results indicate that the ANN algorithm coupled with back propagation neural network is an efficient and accurate method in predicting of surface roughness in turning.
In this study, the average surface roughness values obtained when turning AISI 4140 grade tempere... more In this study, the average surface roughness values obtained when turning AISI 4140 grade tempered steel with a hardness of 51 HRC, were modeled using fuzzy logic, artificial neural networks (ANN) and multi-regression equations. Input variables consisted of cutting speed (V), feed ...
This study presents a new method to determine multi-objective optimal cutting conditions and math... more This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicte...
Machine parts during their useful life are significantly influenced by surface roughness quality.... more Machine 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 ...
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