An Efficient Case-Based Assembly Sequence Generation System

自动装配序列生成（ASG）对于 高效制造和并行工程。 有 为助理秘书长开发工具的两个主要困难：㈠ 组合学使得通过盲目搜索的解决方案变得棘手， (ii)最佳装配顺序的标准很难 正式化。 PI的早期工作调查了基于病例的 推理技术，同时解决这两个问题， 令人鼓舞的结果，特别是关于可扩展性， 涵盖了大量的问题。 实际目标是， 项目是在一个更大的原型上验证这些结果， 从而证明了基于实例的装配序列的实用性 生成实际应用。 理论 该项目的目标是研究新的方法， 自适应学习和案例匹配，这将构成 新的原型 这项工作的结果是全面实施了基于案例的 推理系统的一大类装配设备。 的 建立这样一个系统所涉及的概念性成果应 对智能设计与制造的研究有一定的参考价值 系统和基于案例的推理领域。

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.