Research Initiation Award: Automatic Precedence Knowledge Acquisition and Design of a Real-Time Intelligent Assembly Scheduler with Neural Computing

本研究的目的是开发一种实时的 智能装配系统集成了从 将装配设计过程转化为最优装配进度 一代 本研究的目的是：（1）获得 自动装配对象的优先级知识 无论是基于规则的系统，还是神经网络学习， 方案;（2）找到优先知识之间的映射 和研究的神经网络;（3）生成最佳的 装配进度计划的基础上的映射之间的优先级 知识和网络和算法;（4）实现一个 系统化智能装配系统， 设计过程和最优装配体的生成 schedule. 本发明适用于汽车装配， 电子线路板组件，工业柔性 制造业和其他相关领域 优先约束计划和调度问题。

The objective of this research is to develop a real-time intelligent assembly system which integrates the operations from the assembly design process to the optimal assembly schedule generation. The goals of the research are: (1) obtain the precedence knowledge of the assembled object automatically either from a rule-based system, or from neural network learning schemes; (2) find the mapping between the precedence knowledge and investigated neural networks; (3) generate the optimal assembly schedule based on the mapping between the precedence knowledge and the network and algorithms; and (4) implement a systematic intelligent assembly system which integrates the design process and the generation of the optimal assembly schedule. The system is applicable to automobile assembly, electronic circuit board assembly, industrial flexible manufacturing and other fields related to the precedence-constraint planning and scheduling problems.