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Evolutionary approach for cutting forces prediction in milling
Miha Kovačič, Jože Balič, Miran Brezočnik, 2004, original scientific article

Abstract: Knowing cutting forces is important for choosing cutting parameters for milling. Traditionally, cutting forces are calculated by equation which includes empirically measured specific cutting forces. In the article modelling of cutting forces with genetic programming is proposed, which imitates principles of living beings. Measurements have been made for two materials (aluminium alloy AlMgSi1 and steel 1.2343) and two different types of milling (conventional milling and STEP milling). For each material and type of milling parameters, tensile strength and hardness of workpiece, tool diameter, cutting depth, spindle speed, feeding and type of milling were monitored, and for each combination of milling parameters cutting forces were measured. On the basis of the experimental data, different models for cutting forces prediction were obtained by genetic programming. Research shows that genetically developed models fit the experimental data.
Keywords: milling, simulation, milling cutting forces prediction, genetic programming
Published: 01.06.2012; Views: 947; Downloads: 63
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Tool cutting force modeling in ball-end milling using multilevel perceptron
Uroš Župerl, Franc Čuš, 2004, original scientific article

Abstract: This paper uses the artificial neural networks (ANNs) approach to evolve an efficient model for estimation of cutting forces, based on a set of input cutting conditions. A neural network algorithms are developed for use as a direct modeling method, to predict forces for ball-end milling operation. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling process. The training of the networks is preformed with experimental machining data. The predictive capability of using analytical and neural network approaches are compared using statistics, which showed that neural network predictions for three cutting force components were for 4% closer to the experimental measurements, compared to 11% using analytical method. Exhaustive experimentation is conduced to develop the model and to validate it. The milling experiments prove that this model can predict accurately the cutting forces in three Cartesian directions.The force model can be used for simulation purposes and for defining threshold values in cutting tool condition monitoring system.
Keywords: ball end milling, cutting forces, modelling, artificial intelligence, neural networks
Published: 01.06.2012; Views: 1738; Downloads: 83
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An overview of data acquisition system for cutting force measuring and optimization in milling
Matjaž Milfelner, Franc Čuš, Jože Balič, 2005, original scientific article

Abstract: This paper presents an approach, for the systematic design of condition monitoring system for machine tool and machining operations. The research is based on utilising the genetic optimization method for the on-line optimization of the cutting parameters and to design a program for the signal processing and for the detection of fault conditions for milling processes. Cutting parameters and the measured cutting forces are selected in this work as an application of the proposed approach.
Keywords: ball-end milling, cutting process, data acqusition, simulation, cutting forces
Published: 01.06.2012; Views: 1390; Downloads: 79
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A generalized neural network model of ball-end milling force system
Uroš Župerl, Franc Čuš, Bogomir Muršec, Anton Ploj, 2005, original scientific article

Abstract: The focus of this paper is to develop a reliable method to predict 3D cutting forces during ball-end milling process. This paper uses the artificial neural networks (ANNs) approach to evolve an generalized model for prediction of cutting forces, based on a set of input cutting conditions. A set of ten input milling parameters that have a major impact on the cutting forces was chosen to represent the machining conditions. The training of the networks is performed with experimental machining data. This approach greatly reduces the time-consuming mathematical work normally required for obtaining the cutting force expressions. The estimation performance of the network is evaluated through a detailed simulation study. The accuracy of an analytical model, which is a feasible alternative to the network, is compared to that of the network. With similar system parameter estimates for both methods, the network is found to be considerably more accurate than the analytical model. The results of model validation experiments on machining Ck45 are also reported. Experimental results demonstrate that this method can accurately estimate feed cutting force within an error of 4%. The results also indicate that when the combination of sigmoidal and gaussian transfer function were applied, the prediction accuracy of neural network is as high as 98%.
Keywords: end-milling, cutting forces, cutting parameters, generalized neural networks, modeling
Published: 01.06.2012; Views: 1879; Downloads: 72
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Genetic equation for the cutting force in ball-end milling
Matjaž Milfelner, Janez Kopač, Franc Čuš, Uroš Župerl, 2005, original scientific article

Abstract: The paper presents the development of the genetic equation for the cutting force for ball-end milling process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology and intelligent process technology with the adequate hardware and software support. Ball-end milling is a very common machining process in modern manufacturing processes. The cutting forces play the important role for the selection of the optimal cutting parameters in ball-end milling. In many cases the cutting forces in ball-end milling are calculated by equation from the analytical cutting force model. In the paper the genetic equation for the cutting forces in ball-end milling is developed with the use of the measured cutting forces and genetic programming. The experiments were made with the system for the cutting force monitoring in ball-end milling process. The obtained results show that the developed genetic equation fits very well with the experimental data. The developed genetic equation can be used for the cutting force estimation and optimization of cutting parameters. The integration of the proposed method will lead to the reduction in production costs and production time, flexibility in machining parameter selection, and improvement of product quality.
Keywords: milling, ball-end mill, optimization, cutting forces, cutting parameters, genetic algorithms
Published: 01.06.2012; Views: 1601; Downloads: 93
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An intelligent system for monitoring and optimization of ball-end milling process
Franc Čuš, Matjaž Milfelner, Jože Balič, 2006, original scientific article

Abstract: The paper presents an intelligent system for on-line monitoring and optimization of the cutting process on the model of the ball-end milling. An intelligent system for monitoring and optimization in ball-end milling is developed both in hardware and software. It is based on a PC, which is connected to the CNC main processor module through a serial-port so that control and communication can be realised. The monitoring system is based on LabVIEW software, the data acquisition system and the measuring devices (sensors) for the cutting force measuring. The system collects the variables of the cutting process by means of sensors. The measured values are delivered to the computer program through the data acquisition system for data processing and analysis. The optimization technique is based on genetic algorithms for the determination of the cutting conditions in machining operations. In metal cutting processes, cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product. Experimental results show that the proposed genetic algorithm-based procedure for solving the optimization problem is effective and efficient, and can be integrated into a real-time intelligent manufacturing system for solving complex machining optimization problems.
Keywords: ball-end milling, cutting forces, monitoring, optimization
Published: 30.05.2012; Views: 1458; Downloads: 89
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