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A model of surface roughness constitution in the metal cutting process applying tools with defined stereometry
Stanisĺaw Adamczak, Edward Miko, Franc Čuš, 2009, original scientific article

Abstract: The process of surface roughness formation is complex and dependent on numerous factors. The analysis of the latest reports on the subject shows that mathematical relationships used for determining surface irregularities after turning and milling are not complete or accurate enough and, therefore, need to be corrected. A new generalized mathematical model of roughness formation was developed for surfaces shaped with round-nose tools. The model provides us with a quantitative analysis of the effects of the tool representation, undeformed chip thickness, tool vibrations in relation to the workpiece, tool runout (for multicutter tools) and, indirectly, also tool wear. This model can be used to prepare separate models for most of the typical machining operations. Surface roughness is represented here by two parameters Ra and Rt. Simulations carried out for this model helped to develop nomograms which can be used for predicting and controlling the roughness Ra of surfaces sculptured by face milling.
Keywords: metal cutting, surface roughness, finishing, face milling
Published: 31.05.2012; Views: 1183; Downloads: 13
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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: 1204; Downloads: 64
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Adaptive self-learning controller design for feedrate maximization of machining process
Franc Čuš, Uroš Župerl, 2007, original scientific article

Abstract: An adaptive control system is built which controlling the cutting force and maintaining constant roughness of the surface being milled by digital adaptation of cutting parameters. The paper discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy (Particle Swarm Optimization) in modeling and adaptively controlling the process of end milling. An overall approach of hybrid modeling of cutting process (ANfis-system), used for working out the CNC milling simulator has been prepared. The basic control design is based on the control scheme (UNKS) consisting of two neural identificators of the process dynamics and primary regulator. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear.
Keywords: end milling, adaptive force control, artificial intelligence, optimisation, adaptive control systems
Published: 31.05.2012; Views: 998; Downloads: 24
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Feature extraction from CAD model for milling strategy prediction
Jože Balič, Simon Klančnik, Simon Brezovnik, 2008, original scientific article

Abstract: In this paper we present a procedure of feature determination from a CAD model. From the model we extract information, which has the greatest influence on the technological parameters of treatment and then transform this information into appropriate input data for different intelligent processing strategy prediction systems (for example artificial neural network). With formally complex CAD models, different processing strategies are required on a single workpiece. For this reason we use segmentation as described in this paper, to partition the surface of the CAD model into regions, so that we treat each region as an independent model and determine its features.
Keywords: CAD-CAM systems, milling strategies, feature extraction, CAD models, segmentation
Published: 31.05.2012; Views: 1299; Downloads: 25
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Intelligent cutting tool condition monitoring in milling
Uroš Župerl, Franc Čuš, Jože Balič, 2011, original scientific article

Abstract: Purpose: of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time by using a combination of neural decision system, ANFIS tool wear estimator and machining error compensation module. Design/methodology/approach: The principal presumption was that the force signals contain the most useful information for determining the tool condition. Therefore, ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). Findings: The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. Research limitations/implications: This study also briefly presents a compensation method in milling in order to take into account tool deflection during cutting condition optimization or tool-path generation. The results indicate that surface errors due to tool deflections can be reduced by 65-78%. Practical implications: The fundamental limitation of research was to develop a single-sensor monitoring system, reliable as commercially available system, but much cheaper than multi-sensor approach. Originality/value: A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals.
Keywords: tool condition monitoring, TCM, wear, tool deflection, ANFIS, neural network, end-milling
Published: 01.06.2012; Views: 940; Downloads: 22
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Fuzzy control strategy for an adaptive force control in end-milling
Uroš Župerl, Franc Čuš, Matjaž Milfelner, 2005, original scientific article

Abstract: This paper discusses the application of fuzzy adaptive control strategy to the problem of cutting force control in high speed end-milling operations. The research is concerned with integrating adaptive control with a standard computer numerical controller (CNC) for optimising a metal-cutting process. It is designed to adaptively maximise the feed-rate subject to allowable cutting force on the tool, which is very beneficial for a time consuming complex shape machining. The purpose is to present a reliable, robust neural controller aimed at adaptively adjusting feed-rate to prevent excessive tool wear, tool breakage and maintain a high chip removal rate. Numerous simulations and experiments are conducted to confirm the efficiency of this architecture.
Keywords: automatic control, end milling, adaptive force control, fuzzy adaptive control
Published: 01.06.2012; Views: 1189; Downloads: 59
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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: 1290; Downloads: 77
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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: 1589; Downloads: 62
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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: 1152; Downloads: 67
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Computer-based workpiece detection on CNC milling machine tools using optical camera and neural networks
Simon Klančnik, Jernej Šenveter, 2010, original scientific article

Abstract: In this paper, system for optical determining the workpiece origin on the CNC machine is presented. Similar high sophisticated systems are commercially available but in most cases they are very expensive and so their purchase is economically unjustified. The purpose of our research is to develop an inexpensive system for non-contact determination of the workpiece origin, which is also sufficiently precise for practical use. The system is implemented on a three-axis CNC milling machine Lakos 150 G, which is primarily designed for good machinability materials. Calibration procedure using feed-forward neural networks was developed. With this method the calibration procedure is simplified and the mathematical derivation of camera model is avoided. Learned neural network represents the camera calibration model. After neural network learning is complete, we can begin using the system for determining the workpiece origin. This developed system was through a number of tests proved to be reliable and suitable for use in practice. In the paper, working of system is illustrated with a practical example, which confirms the effectiveness of the implemented system in actual use on machine.
Keywords: neural networks, image processing, milling, workpiece detection
Published: 01.06.2012; Views: 1168; Downloads: 26
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