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1.
Approach to optimization of cutting conditions by using artificial neural networks
Franc Čuš, Uroš Župerl, 2006, izvirni znanstveni članek

Opis: Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network-based approach to complex optimization of cutting parameters is proposed. It describes the multi-objective technique of optimization of cutting conditions by means of the neural networks taking into consideration the technological, economic and organizational limitations. To reach higher precision of the predicted results, a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.
Ključne besede: optimization, cutting parameter optimization, genetic algorithm, cutting parameters, neural network algorithm, machining, metal cutting
Objavljeno: 30.05.2012; Ogledov: 1330; Prenosov: 60
URL Povezava na celotno besedilo

2.
Prediction of surface roughness using a feed-forward neural network
Jernej Šenveter, Simon Klančnik, Jože Balič, Franc Čuš, 2010, izvirni znanstveni članek

Opis: This article presents the development of a system for predicting surface roughness, using a feed-forward neural network. The primary goal was to develop a system in order to predict with complex reliability and defined accuracy. However, this system is designed in such a way that it is also possible to use it for various other workpieces. The described system uses a neural network which receives signals at the input level. The signals then travel through all hidden levels to the output level, where the responses to input signals are received. Data are used which affects the selection of surface roughness regarding the input to the neural network. Three different inputs in total are used for the neural network. Data which represents the inputs to the neural network are encoded, so that they occupy values between 0 and 1. Adequate cutting speed, feed, and depth of cut, are selected in order to achieve an adequate surface roughness of the workpiece, using the trained neural network. This contributes to the optimisation and economy of machining, which is very important during the production of an individual product and also for an individual company or organisation when transferring the final product to the contracting authority or final customer.
Ključne besede: machining, turning, surface roughness, neural network
Objavljeno: 31.05.2012; Ogledov: 1016; Prenosov: 40
URL Povezava na celotno besedilo

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Intelligent cutting tool condition monitoring in milling
Uroš Župerl, Franc Čuš, Jože Balič, 2011, izvirni znanstveni članek

Opis: 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.
Ključne besede: tool condition monitoring, TCM, wear, tool deflection, ANFIS, neural network, end-milling
Objavljeno: 01.06.2012; Ogledov: 845; Prenosov: 19
URL Povezava na celotno besedilo

5.
Intelligent tool path generation for milling of free surfaces using neural networks
Jože Balič, Marjan Korošec, 2002, izvirni znanstveni članek

Opis: The presented paper has an intention to show how with the help of Artificial Neural Network (ANN), the prediction of milling tool-path strategy could be made in order to establish which milling path strategy or their sequence will show the best results (will be the most appropriate) at free surface machining, according to set technological aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. Configuration of used Neural Network (NN) is presented, and the whole procedure is shown on an example of mould, for producing light switches. The verification of machined surface quality, according to average mean roughness, Ra, is also being done, and compared with the NN predicted results
Ključne besede: neural network, CAD/CAM, CAPP, ICAM, milling strategy
Objavljeno: 01.06.2012; Ogledov: 958; Prenosov: 59
URL Povezava na celotno besedilo

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Prediction of the hardness of hardened specimens with a neural network
Matej Babič, Peter Kokol, Igor Belič, Peter Panjan, Miha Kovačič, Jože Balič, Timotej Verbovšek, 2014, izvirni znanstveni članek

Opis: In this article we describe the methods of intelligent systems to predict the hardness of hardened specimens. We use the mathematical method of fractal geometry in laser techniques. To optimize the structure and properties of tool steel, it is necessary to take into account the effect of the self-organization of a dissipative structure with fractal properties at a load. Fractal material science researches the relation between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes an application of the fractal dimension in the robot laser hardening of specimens. By using fractal dimensions, the changes in the structure can be determined because the fractal dimension is an indicator of the complexity of the sample forms. The tool steel was hardened with different speeds and at different temperatures. The effect of the parameters of robot cells on the material was better understood by researching the fractal dimensions of the microstructures of hardened specimens. With an intelligent system the productivity of the process of laser hardening was increased because the time of the process was decreased and the topographical property of the material was increased.
Ključne besede: fractal dimension, fractal geometry, neural network, prediction, hardness, steel, tool steel, laser
Objavljeno: 17.03.2017; Ogledov: 509; Prenosov: 51
.pdf Celotno besedilo (632,41 KB)
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9.
Characterization of Slovenian coal and estimation of coal heating value based on proximate analysis using regression and artificial neural networks
Darja Kavšek, Adriána Bednárová, Miša Biro, Roman Kranvogl, Darinka Brodnjak-Vončina, Ernest Beinrohr, 2013, izvirni znanstveni članek

Opis: Chemical composition of Slovenian coal has been characterised in terms of proximate and ultimate analyses and the relations among the chemical descriptors and the higher heating value (HHV) examined using correlation analysis and multivariate data analysis methods. The proximate analysis descriptors were used to predict HHV using multiple linear regression (MLR) and artificial neural network (ANN) methods. An attempt has been made to select the model with the optimal number of predictor variables. According to the adjusted multiple coefficient of determination in the MLR model, and alternatively, according to sensitivity analysis in ANN developing, two descriptors were evaluated by both methods as optimal predictors: fixed carbonand volatile matter. The performances of MLR and ANN when modelling HHV were comparable; the mean relative difference between the actual and calculated HHV values in the training data was 1.11% for MLR and 0.91% for ANN. The predictive ability of the models was evaluated by an external validation data set; the mean relative difference between the actual and predicted HHV values was 1.39% in MLR and 1.47% in ANN. Thus, the developed models could be appropriately used to calculate HHV.
Ključne besede: Slovenian coal, higher heating value, HHV, regression, artificial neural network
Objavljeno: 03.04.2017; Ogledov: 516; Prenosov: 153
.pdf Celotno besedilo (749,77 KB)
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10.
Web based education tool for neural network robot control
Jure Čas, Darko Hercog, Riko Šafarič, izvirni znanstveni članek

Opis: This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC) and the graphical user interface (GUI). Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI), running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote's user.
Ključne besede: LabVIEW, Matlab/Simulink, neural network control, remote educational tool
Objavljeno: 19.07.2017; Ogledov: 357; Prenosov: 40
.pdf Celotno besedilo (792,88 KB)
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