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Cites:

Information Journal Paper

Title

TOOL WEAR ESTIMATION IN FACE MILLING USING SPINDLE MOTOR PARAMETERS WITH NEURAL NETWORKS

Pages

  79-88

Abstract

 The monitoring of TOOL WEAR is one of the important methods to improve the dimensional accuracy and economic aspects of machining. To achieve this, instead of measuring the TOOL WEAR, other parameters, which are related to TOOL WEAR, are used. In this paper, an intelligent system, based on NEURAL NETWORKS, is presented by this method; the TOOL WEAR is estimated online with measuring spindle motor parameters, such as current and the speed of motor. Thus, the current and the speed of motor in different condition of machining (feed, depth of cut and rpm of tool) and wear were measured with practical experiments and the effects of TOOL WEAR on current and speed of motor is analyzed. Based on the results, a back propagation (BP) neural network is developed and trained. Using this network, the TOOL WEAR could be estimated while machining in different conditions, measuring current and speed of motor. This system could be used to control and monitor the machining process.

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  • Cite

    APA: Copy

    MOSTAFA POURASL, A., & RAZFAR, M.R.. (2005). TOOL WEAR ESTIMATION IN FACE MILLING USING SPINDLE MOTOR PARAMETERS WITH NEURAL NETWORKS. AEROSPACE MECHANICS JOURNAL, 1(1), 79-88. SID. https://sid.ir/paper/101828/en

    Vancouver: Copy

    MOSTAFA POURASL A., RAZFAR M.R.. TOOL WEAR ESTIMATION IN FACE MILLING USING SPINDLE MOTOR PARAMETERS WITH NEURAL NETWORKS. AEROSPACE MECHANICS JOURNAL[Internet]. 2005;1(1):79-88. Available from: https://sid.ir/paper/101828/en

    IEEE: Copy

    A. MOSTAFA POURASL, and M.R. RAZFAR, “TOOL WEAR ESTIMATION IN FACE MILLING USING SPINDLE MOTOR PARAMETERS WITH NEURAL NETWORKS,” AEROSPACE MECHANICS JOURNAL, vol. 1, no. 1, pp. 79–88, 2005, [Online]. Available: https://sid.ir/paper/101828/en

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