TOOL WEAR MONITORING IN FACE MILLING BY FORCE MEASURING WITH NEURAL NETWORKS

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MOSTAFA POURASL A.; RAZFAR M.R.

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Journal: AMIRKABIR Year:2006 | Volume:16 | Issue:63-B Page(s): 27-35

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Information Journal Paper

Title

TOOL WEAR MONITORING IN FACE MILLING BY FORCE MEASURING WITH NEURAL NETWORKS

Author(s)

MOSTAFA POURASL A. | RAZFAR M.R. | Issue Writer Certificate

Keywords

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Abstract

Tool wear is a purpose in machining process. Dimensional accuracy, surface quality of work, machining forces, vibration, tool life, and machine tools life are influenced by tool wear. In other hand estimating the tool wear without stopping the operation is an essential problem in system automation. In this paper an intelligent approach for tool wear estimation from machining forces is proposed. Many experimental tests have been done in different machining conditions and tool wear amount have been measured directly. With this data, a back propagation (bp) neural network have been designed and trained. This neural network can estimate the tool wear amount with a high accuracy receiving data of depth of cut, feed rate, revolution and machining forces in X, Y and Z coordinates.

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APA: Copy

MOSTAFA POURASL, A., & RAZFAR, M.R.. (2006). TOOL WEAR MONITORING IN FACE MILLING BY FORCE MEASURING WITH NEURAL NETWORKS. AMIRKABIR, 16(63-B), 27-35. SID. https://sid.ir/paper/899/en

Vancouver: Copy

MOSTAFA POURASL A., RAZFAR M.R.. TOOL WEAR MONITORING IN FACE MILLING BY FORCE MEASURING WITH NEURAL NETWORKS. AMIRKABIR[Internet]. 2006;16(63-B):27-35. Available from: https://sid.ir/paper/899/en

IEEE: Copy

A. MOSTAFA POURASL, and M.R. RAZFAR, “TOOL WEAR MONITORING IN FACE MILLING BY FORCE MEASURING WITH NEURAL NETWORKS,” AMIRKABIR, vol. 16, no. 63-B, pp. 27–35, 2006, [Online]. Available: https://sid.ir/paper/899/en

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