权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Development of an intelligent deep-hole boring tool

智能深孔镗刀的研制

基本信息

批准号：
06650304
负责人：
KATSUKI Akio
金额：
$ 1.22万
依托单位：
KYUSHU UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-06650304/
关键词：
Deep hole boring tool Intelligent Guiding Laser Surface roughness Tool wear Sound Artificial neural network

项目摘要

The research was conducted to bore high-quality ultra-deep-hole (L/D*1000) by monitoring the cutting conditions, i.e., by giving functions of such five senses as sight, hearing, touch, taste and smell to a deep-hole boring tool. The following three items were made clear. (1) The old-type laser-guided deep-hole boring tool was improved to be of high-performance. The original tool had a He-Ne laser in the optical system for detecting its attitude and used cams as actuators. The new one is equipped with an argon laser and piezoelectric translaters. Basic experiments using duralumin workpieces (JIS A2017-T4) with a prebored 108 mm diameter hole showed that the new tool has high performance in manipulation and guidance. (2) The surface roughness could be simply characterized on the off-line information processing system by observation through an industrial endoscope, using a CCD camera and image processing. The chips exhausting-and tip-conditions could be observed on-line using image processing. (3) To detect the abnormal cutting, an Artificial Neural Network was constructed using cutting sound as input. Experiments were conducted on a lathe to examine whether the ANN can classify cutting conditions of chattering vibration, formation of multi-cornered hole and tool wear, which are expected to occur in deep hole boring. As a result, the cutting conditions couled be clasified off-line. Evaluation of the total deep-hole boring system using ANN continues hereafter.

通过对切削条件的监控，通过赋予深孔钻具视觉、听觉、触觉、味觉、嗅觉五种感官功能，明确了以下三点。(1)对老式激光导向深孔镗刀进行了高性能化改造。最初的工具在光学系统中有一个He-Ne激光器用于检测其姿态，并使用凸轮作为致动器。新仪器配备了氩激光器和压电晶体。使用带有108 mm直径预钻孔的硬铝工件（JIS A2017-T4）进行的基础实验表明，新工具具有高性能的操作和引导。(2)表面粗糙度可以简单地表征离线信息处理系统通过工业内窥镜观察，使用CCD相机和图像处理。利用图像处理技术可以在线观察切屑的排屑和切尖状态。(3)为了检测切削异常，以切削声为输入，建立了一个人工神经网络.在车床上进行了实验，以检查人工神经网络是否可以分类切削条件的颤振，形成多角孔和刀具磨损，预计将发生在深孔镗削。从而实现了切削条件的离线分类。使用人工神经网络的整个深孔镗削系统的评估将在下文继续进行。