Hot Selling Machines
Mining Machines Home>Products

Papers On Ball And Milling Operation Nural Network In Versity

2019-01-14T14:01:10+00:00
  • Dynamic neural network approach for tool cutting force ...

    2011-11-03  (2006). Dynamic neural network approach for tool cutting force modelling of end milling operations. International Journal of General Systems: Vol. 35, No. 5, pp. 603-618.

  • Cited by: 11
  • Artificial Neural Network Modeling of Ball Mill Grinding ...

    Artificial neural networks (ANN) are an attractive tool for the modeling of non-linear processes and phenomena. In the cement industry, ANNs are mostly used to describe and control the main ...

  • Author: Veerendra Singh
  • Adaptive Optimization of Face Milling Operations Using ...

    In this paper, the method for adaptive optimization of the cutting conditions in a face milling operation for maximizing the material removal rate is proposed. The optimization procedure described uses an exterior penalty function method in conjunction with a multilayered neural network. Two neural networks are introduced: one for estimating tool wear length, and the other for mapping input ...

  • Avoiding neural network fine tuning by using ensemble ...

    DOI: 10.1007/S00170-011-3300-Z Corpus ID: 110352934. Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations @article{Bustillo2011AvoidingNN, title={Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations}, author={Andr{\'e}s Bustillo and J. D{\'i}ez-Pastor and G. Quintana and C. Garc{\'i}a ...

  • Avoiding neural network fine tuning by using ensemble ...

    A reliable surface roughness prediction application is presented in this paper. It is based on ensemble learning for vertical high-speed milling operations with ball-end mills for finishing operations on quenched steel 1.2344 (AISI H13) that are widely used in the manufacture of moulds and dies. The new approach was validated with an experimental dataset that includes geometrical tool factors ...

  • of roughness in the ball end milling surface methodology, and

    Prediction of surface roughness in the ball‐end milling ... ish milling operations [7]. When referencing to the already published papers, obviously there have been a lot of researches regarding the prediction of surface roughness in face, but also end milling process. For this purpose, a number of statistical methods were used such as RSM and soft computing techniques or bio‐inspired ...

  • Peng Huang's research works Southeast University (China ...

    Aiming at the disadvantage of conventional optimization method for ball mill pulverizing system, a novel approach based on RBF neural network and genetic algorithm was proposed in the present ...

  • Application of ANN in Milling Process: A Review

    AbstractIntroductionSurface RoughnessCutting ForcesTool Life and WearConclusionsIn recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on th
  • A Neural Network Model for SAG Mill Control

    the neural network also uses real-time size data obtained from cameras located over the mill feed conveyor and analysed . by a Split-OnLine vision system. The paper is organized into two parts. Part 1 reviews the concepts and issues for implementing expert systems and Neural Networks. Part 2 then describes the execution of the Neural Network

  • Optimization of CNC ball end milling: a neural network ...

    2005-07-15  In this paper, an integrated product development system for optimized CNC ball end milling is presented. First, the developed model is extended from flat end milling to ball end milling. Second, the optimization is extended from 2D (speed and feed) to 3 (1/2) D (speed, feed, radial and axial depths of cut). Third, the modeling and simulation of the flat end milling is extended to include more ...

  • Modeling of CNC Machining Process - Artificial Neural ...

    Only few recent studies have been published in this area and they are mostly focused on the milling with ball end mill tool. The paper presents modeling and prediction of technological parameters of CNC milling using artificial neural networks (ANN), while multilayered feedforward neural network (MFFNN) was

  • Tool cutting force modeling in ball-end milling ... - CORE

    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 ...

  • Optimization of CNC ball end milling: a neural network ...

    2005-07-15  In this paper, an integrated product development system for optimized CNC ball end milling is presented. First, the developed model is extended from flat end milling to ball end milling. Second, the optimization is extended from 2D (speed and feed) to 3 (1/2) D

  • Avoiding neural network fine tuning by using ensemble ...

    A reliable surface roughness prediction application is presented in this paper. It is based on ensemble learning for vertical high-speed milling operations with ball-end mills for finishing operations on quenched steel 1.2344 (AISI H13) that are widely used in the manufacture of moulds and dies. The new approach was validated with an experimental dataset that includes geometrical tool factors ...

  • Mechanics and Dynamics of Ball End Milling Journal of ...

    The helical ball end mill attached to the spindle is modelled by orthogonal structural modes in the feed and normal directions at the tool tip. For a given cutter geometry, the cutting coefficients are transformed from an orthogonal cutting data base using an oblique cutting model. The three dimensional swept surface by the cutter is digitized using the true trochoidal kinematics of ball end ...

  • of roughness in the ball end milling surface methodology, and

    Prediction of surface roughness in the ball‐end milling ... ish milling operations [7]. When referencing to the already published papers, obviously there have been a lot of researches regarding the prediction of surface roughness in face, but also end milling process. For this purpose, a number of statistical methods were used such as RSM and soft computing techniques or bio‐inspired ...

  • Investigation of Surface Roughness while Ball Milling ...

    Paper Titles Generation of Microelectrodes for Micro EDM ... M. L. Garcia-Romeu, J. Ciurana, Surface roughness monitoring application based on artificial neural networks for ball-end milling operations, J. Intell. Manuf. 22 (2010) 607-617. DOI: 10.1007/s10845-009-0323-5 [9] J. S. Hossain, N. Ahmad, Surface Roughness Prediction Model for Ball End Milling Operation Using Artificial Intelligence ...

  • Cutting Force Prediction of High-Speed Milling Hardened ...

    BP neural networks Micro-ball end mill ... Ruan, X.: Cutting Forces Simulation of Ball-End Milling Based on Solid Modeling. Journal of Shanghai Jiao Tong University 35, 1003–1007 (2001) (in Chinese) Google Scholar. 4. Dimla, E., Dimla, J.R., Paul, M., Nigel, J.: Automatic Tool State Identification in a Metal Turning Operation Using MLP. Neural Networks and Multivariate Process Parameters 38 ...

  • On the use of back propagation and radial basis function ...

    Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network. A DOE based approach for the design of RBF artificial neural networks applied...

  • IACSIT International Journal of Engineering and Technology ...

    life prediction in milling operations by using artificial neural networks and Taguchi design of experiment. Machining experiments were performed under various cutting conditions by using sample specimens. A very good agreement between predicted model and experimental results was obtained. The correlation between the estimated and experimental data was 0.96966 for train and 0.94966 for

  • On the use of back propagation and radial basis function ...

    More from Journal of Industrial Engineering International. Cooperative vehicle routing problem: an opportunity for cost saving Cooperative vehicle routing problem: an opportunity for cost saving. JIT single machine scheduling problem with periodic preventive maintenance JIT single machine scheduling problem with periodic preventive maintenance. A fuzzy MCDM model with objective and subjective ...

  • Non-local Neural Networks

    Non-local Neural Networks ... 1Carnegie Mellon University 2Facebook AI Research Abstract Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range depen-dencies. Inspired by the classical non-local means method [4] in computer ...

  • Journal of Engineering and Technology Research ...

    Artificial neural networks (ANN’s) is an approach to evolve an efficient model for estimation of surface roughness, based on a set of input cutting conditions. Neural network algorithms are developed for use as a direct modeling method, to predict surface roughness for end milling operations. Prediction of surface roughness in end milling is often needed in order to establish automation or ...

  • INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END

    Original scientific paper In this article, an adaptive neural controller for the ball end-milling process is described. Architecture with two different kinds of neural networks is proposed, and is used for the on-line optimal control of the milling process. A BP neural network is used to identify the milling state and to de-termine the optimal cutting inputs. The feedrate is selected as the ...

  • Investigation of Surface Roughness while Ball Milling ...

    Paper Titles Generation of Microelectrodes for Micro EDM ... M. L. Garcia-Romeu, J. Ciurana, Surface roughness monitoring application based on artificial neural networks for ball-end milling operations, J. Intell. Manuf. 22 (2010) 607-617. DOI: 10.1007/s10845-009-0323-5 [9] J. S. Hossain, N. Ahmad, Surface Roughness Prediction Model for Ball End Milling Operation Using Artificial Intelligence ...

  • Modeling and adaptive force control of milling by using ...

    The contribution discusses the use of combining the methods of neural networks, fuzzy logic and PSO evolutionary strategy in modeling and adaptively controlling the process of ball-end milling. On ...

  • Prediction of Cutting Temperatures by Using Back ...

    Paper Titles Electro-Discharge Machining of SUS 304 Stainless Steel with TaC Powder-Mixed Dielectric ... Ploj, A generalized neural network model of ball-end milling force system, Journal of Materials Processing Technology, 175 (2006) 98–108. DOI: 10.1016/j.jmatprotec.2005.04.036 [3] M. Alhazza and E. Y. T. Adesta. Flank Wear Modeling In High Speed Hard Turning By Using Artificial Neural ...

  • Development of family of artificial neural networks for ...

    Therefore, in this paper a family of artificial neural networks (FANN) was developed to predict the axial force and drilling torque as a func- tion of a number of influencing factors. The formation of the FANN took place in three phases, in each phase the neural networks formed were trained by drilling lengths until the drill bit was worn out and by a variable parameter, while the combinations ...

  • ROUGHNESS OF A MACHINED SURFACE IN MILLING

    Minimum experiment trials are designed by Taguchi based L9 (3^3) orthogonal array with the help of Minitab 17.0 software and a fuzzy logic appr oach based model is taken as to predict the value of surface roughness of a machined surface in 6101 aluminum alloy, Copper of Electrolytic grade and Mild Steel 2062 milling operation using HSS end mill cutter of 12 mmdiameter. Three membership ...

  • Optimum damage and surface roughness prediction in end ...

    This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, and depth of cut) and end mill flutes leading to minimum surface roughness and delamination factor in end milling of Limestone fibre reinforced plastics (GFRP) by coupling neural network (NN) and genetic algorithm (GA). In this regard, the advantages of statistical experimental design ...

  • INTELLIGENT ADAPTIVE CUTTING FORCE CONTROL IN END

    Original scientific paper In this article, an adaptive neural controller for the ball end-milling process is described. Architecture with two different kinds of neural networks is proposed, and is used for the on-line optimal control of the milling process. A BP neural network is used to identify the milling state and to de-termine the optimal cutting inputs. The feedrate is selected as the ...

  • Prediction of Cutting Temperatures by Using Back ...

    Paper Titles Electro-Discharge Machining of SUS 304 Stainless Steel with TaC Powder-Mixed Dielectric ... Ploj, A generalized neural network model of ball-end milling force system, Journal of Materials Processing Technology, 175 (2006) 98–108. DOI: 10.1016/j.jmatprotec.2005.04.036 [3] M. Alhazza and E. Y. T. Adesta. Flank Wear Modeling In High Speed Hard Turning By Using Artificial Neural ...

  • Prediction and modeling of roughness in ball end milling ...

    Reset your password. If you have a user account, you will need to reset your password the next time you login. You will only need to do this once.

  • DKUM - Search

    Digital library of University of Maribor: diploma theses, master of science theses and doctoral theses of the University of Maribor

  • Application of Neural Network Methodology ... - papers.nips.cc

    University of Queensland, St Lucia, Queensland 4072, Australia. Abstract In this paper, a tree based neural network viz. MARS (Friedman, 1991) for the modelling of the yield strength of a steel rolling plate mill is described. The inputs to the time series model are temperature, strain, strain rate, and interpass time and the output is the corresponding yield stress. It is found that the MARS ...

  • Curriculum Vitae

    1990.3 - 1994. 2 : Ph. D. in Mechanical Engineering, Pohang University of Science and Technology . Thesis Topic : Monitoring and Optimization of Milling Processes Using Neural Networks. 1983.3 - 1985. 2 : M.S. in Mechanical Engineering, Pusan National University

  • Reliability Analysis of the Chatter Stability during ...

    In this paper, the stability of regenerative chatter that might occur during the milling process was analyzed, and a reliability analysis of the chatter stability was performed utilizing a BP neural network. The effect of random factors on the stability of the milling process was analyzed. This approach is more suitable for practical engineering applications than theoretical calculations based ...

  • A neural network approach for chatter prediction in turning

    A neural network approach for chatter prediction in turning ... processes is chatter, or unstable spindle speed-chip width combinations that exhibit self-excited vibration. In this paper, an artificial neural network (ANN) is applied to model turning stability. The analytical stability limit is used to generate a data set that trains the ANN. It is observed that the number and distribution of ...

  • Optimum damage and surface roughness prediction in end ...

    This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, and depth of cut) and end mill flutes leading to minimum surface roughness and delamination factor in end milling of Limestone fibre reinforced plastics (GFRP) by coupling neural network (NN) and genetic algorithm (GA). In this regard, the advantages of statistical experimental design ...

  • Application of Artificial Neural Network for Flow Stress ...

    2017-03-03  This paper discusses the application of artificial neural network (ANN) for calculation of flow stress of material from experimental data. Experiments were conducted in a dynamic thermo-mechanical simulator to measure flow stress of steel at different strain, strain rate and temperature. The experimental data was used to calculate coefficients of empirical equations using multivariable ...

  • TEL
    0086-21-33901608