Development of an Intelligent System for Sheet Metal Bending

钣金折弯智能系统的开发

基本信息

批准号：
03555026
负责人：
SHIMA Susumu
金额：
$ 8.26万
依托单位：
KYOTO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Developmental Scientific Research (B)
财政年份：
1991
资助国家：
日本
起止时间：
1991 至 1992
项目状态：
已结题

项目摘要

This project is to develop an intelligent metal forming system, in particular a common sheet metal bending, that performs flexibility in production and high accuracy incorporating feed forward control. By using a newly developed bending tool set which is suitable for displacement control, deformation of sheet metal is measured on-line ; the material properties was estimated by the measured data and combining with high accuracy of the tool displacement was performed. Further, a neural network was incorporated in the control system for further improvement of accuracy.(1) Instead of conventional V-angled bending tool, the newly developed tools of circular or elliptical shape was used to bend sheet metals on a prototype bending machine. It was thus confirmed that flexibility was improved and the accuracy was also improved by one fold. This is due to the learning control system.(2) In the above tool est, the contact point between the workpiece and the tool shifts according to the tool displacement. An intelligent sensor that measures the forces, both normal and tangential directions, was developed. The contact point and thus bend angle can be measured.(3) A neural network, which describes complicated phenomena was incorporated in the system to develop a control model with high accuracy. The material properties estimated by the deformation model was input into the neural network and binding condition was thus determined. It was thus concluded that combination of plasticity model and neuro-model has its potential of an efficient control model with high accuracy.

该项目是开发一个智能的金属形成系统，特别是一种普通的金属弯曲，在生产方面具有灵活性，并具有高精度，并结合了前馈控制。通过使用适合位移控制的新开发的弯曲工具集，可以在线测量钣金的变形。通过测量数据估算材料特性，并与工具位移的高精度结合在一起。此外，将神经网络纳入了控制系统，以进一步提高准确性。（1）而不是传统的V角弯曲工具，使用了新开发的圆形或椭圆形的工具，用于在原型弯曲机上弯曲薄板金属。因此，确认灵活性得到了提高，并且精度也提高了一倍。这是由于学习控制系统所致。（2）在上述工具中，工件和工具之间的接触点根据工具位移而移动。开发了一种智能传感器，以测量正常和切向方向的力。（3）神经网络描述了系统中的复杂现象，以高精度开发控制模型。因此，通过变形模型估计的材料特性输入到神经网络中，并确定了结合条件。因此得出的结论是，可塑性模型和神经模型的组合具有高精度的有效控制模型的潜力。