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

Intelligent data base for grinding processes

磨削过程智能数据库

基本信息

批准号：
05402031
负责人：
INASAKI Ichiro
金额：
$ 9.92万
依托单位：
KEIO University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (A)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1994
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-05402031/
关键词：
Grinding Artificial intelligence Genetic algorithm Fuzzy reasoning Neural network Data base Dressing 人口知能

项目摘要

The purpose of this study is to establish an intelligent learning model which can imitate the decision making process of skilled operators for setting up the grinding parameters. The function of the system is to provide both the dressing condition and the grinding condition which can achieve the required surface roughness and the specific grinding energy. The specific grinding energy has a decisive influence on the occurrence of grinding burn. Therefore, it is an important output to be observed.In order to establish such intelligent learning model, we employs the neural network to imitate the associative memory of operators. The operator must adopt the grinding conditions with which he could achieve successful results in his experiences. Such process is imitated in our system by applying two different types of neural network i.e., a conventional Feed Forward Network and a Brain-State-in-a-Box Network. These two networks were combined into a hybrid network to imitate the associative memory of operators. The system is able to provide a combination of dressing and grinding condition which can meet the required surface roughness. The effectiveness of the proposed system was confirmed through a series of computer simulations.The second system proposed has an ability to imitate the learning function of operators. In order to achieve such function, we applied a genetic algorithm and a fuzzy rule. The system can learn causalities between the input and the output in the grinding process through practical operations. The system can, consequently, establish a grinding data base. The availability of the system was confirmed through both the computer simulations and the practical grinding experiments.

本研究的目的是建立一个智能学习模型，模拟熟练操作人员设置磨削参数的决策过程。该系统的功能是提供修整条件和磨削条件，以达到所需的表面粗糙度和比磨削能量。磨削比能对磨削烧伤的发生有决定性的影响。为了建立这样的智能学习模型，我们采用神经网络来模拟算子的联想记忆。操作员必须采用磨削条件，才能在他的经验中取得成功。在我们的系统中，通过应用两种不同类型的神经网络来模拟这一过程，即传统的前馈网络和大脑状态在盒中的网络。这两个网络被组合成一个混合网络，以模拟操作员的联想记忆。该系统能够提供修整和磨削的组合条件，满足所需的表面粗糙度。通过一系列计算机仿真验证了所提系统的有效性。第二种系统具有模拟操作员学习功能的能力。为了实现这一功能，我们应用了遗传算法和模糊规则。通过实际操作，该系统可以学习磨削过程中输入和输出之间的因果关系。因此，该系统可以建立磨削数据库。通过计算机仿真和实际磨削实验，验证了该系统的有效性。