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Tool condition monitoring system

发布时间：2017-01-03 18:21

本文关键词：机械设备状态监测无线传感器网络研究进展，由笔耕文化传播整理发布。

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12th

A. Stoi , P. Raos

Mechanical Engineering Faculty ,

Trg I. B. Ma urani 18, HR 35000 Slavonski Brod, Croatia

On line sensing of cutting process has been used to detect tool failure indirectly, measuring acoustic emission, force, sound, vibration, etc. In these indirect methods, the signal magnitude, root mean square value, or magnitudes of the power spectrum, among others, are reported. These values indicate necessity for tool change typically via threshold. To make decision logic works safely and with minimal faults, the control system has to be well trained. The system training results shows influence of cutting speed, feed rate, nose radius of cutting tool on noise and forces during turning. The measurements were performed on 40 CrMnMo 7 steel, when CBN were used as cutting material during dry cutting. The results showed that a high possibility in terms of tool wear monitoring exists, especially for a large-scale production.

1. INTRODUCTION

Considerable research has been done in the area of tool monitoring due to the fact that tool failure represents about 20% of machine tool down–time and that tool wear negatively impacts the work quality in the context of dimensions, finish, and surface integrity. The monitoring tasks are performed through of three main parts: sensing of the process, signal processing and monitoring decision-making. There is no a fault free alarms during the detection of the cutting process abnormalities (wear, breakage, chipping). The monitoring decision-making has to make possible the transformation between the monitoring indices and the process conditions [1]. Research and development in the machining process monitoring and control has not met the requirements necessary for industry adoption. Commercial realization of these technologies is still quite limited after some thirty years of research efforts.

2. OBJECT OF THE MONITORING

Very important decision what has to be done before performing of monitoring tasks is selection of suitable monitoring object (cutting effects – fig.1) and number of influencing parameters. With increasing of the number of monitored parameters decrease the portion of randomly appeared disturbances on chosen cutting effect within sensing and increase the reliability of monitoring model but also indirectly sampling number.

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