An Efficient Case-Based Assembly Sequence Generation System

装配序列自动生成是实现高效制造和并行工程的重要手段。开发ASG工具有两个主要的困难：(i)组合学使得盲搜索解决方案难以解决；（ii）最优装配序列的标准难以形式化。PI的早期工作调查了基于案例的推理技术的使用，以同时解决这两个问题，并获得了令人鼓舞的结果，特别是关于覆盖大型问题集的可扩展性。这个项目的实际目标是在一个更大的原型上验证这些结果，从而证明基于案例的装配序列生成对实际应用的有用性。这个项目的理论目标是研究适应性学习和案例匹配的新方法，这将构成新原型的一部分。这项工作的结果是一个完全实现的基于案例的推理系统，用于大型装配设备。构建该系统所涉及的概念结果将被证明对智能设计和制造系统以及基于案例的推理领域的研究有用。

Automatic assembly sequence generation (ASG) is important for efficient manufacturing and concurrent engineering. There are two main difficulties with developing tools for ASG: (i) the combinatorics makes solutions by blind search intractable and (ii) criteria for optimal assembly sequences are difficult to formalize. Earlier work of the PI investigated the use of case-based reasoning techniques to simultaneously address both problems with encouraging results especially concerning the extensibility to covering large sets of problems. The practical objective of this project is to verify these results on a larger prototype, and thus demonstrate the usefulness of case-based assembly sequence generation for practical applications. The theoretical objectives of this project are to investigate new methods for adaptive learning and case matching which will form part of the new prototype. The result of this work is a fully implemented case-based reasoning system for a large class of assembly devices. The conceptual results involved in building such a system should prove useful to research in intelligent design and manufacturing systems and in case-based reasoning field.