Design Classification and Hybrid Variant/Generative Process Planning

此拨款旨在解决资讯科技应用于工程方面的两个问题。第一个需要解决的领域是开发使用详细的产品设计属性对设计进行自动分类和检索的技术，这些属性比组技术代码更有意义和准确，并且可以从存储在数据库中的设计中自动计算出来。第二个要解决的领域是开发一种混合的变体/生成方法来进行过程规划，这种方法将结合变体和生成过程规划的最佳特征，同时避免每种方法的最坏限制。当新设计需要流程计划时，混合方法将使用上述分类方案检索与设计的相应部分相关的几个计划的部分或“片段”，然后将这些片段组合并修改它们以生成新设计的计划。如果成功，这项工作将有以下好处。首先，它将以一种创新的方式结合变型过程规划和生成过程规划的优势。与传统的变型工艺计划一样，提出的工艺计划方法将构建工艺工程师可能需要改进的工艺计划。然而，通过自动调整检索到的计划以适应新的设计需求，它将最小化对这种改进的需求，从而产生比以前的方法更成功的实现和应用程序。其次，建议的工作将开发技术，帮助设计和制造工程师有效地使用遗留数据，并使其适应新的问题。由于据估计，实践工程师花费超过80%的时间搜索遗留数据、目录和早期的工程项目，这应该对工程实践产生重大的积极影响。

This grant provides funding to address two problem areas in the application of information technology to engineering. The first area to be addressed is the development of techniques for automatic classification and retrieval of designs using detailed product design attributes that are more meaningful and accurate than Group Technology codes and can be computed automatically from the designs stored in the database. The second area to be addressed is the development of a hybrid variant/generative approach to process planning that will combine the best characteristics of both variant and generative process planning, while avoiding the worst limitations of each. When a process plan is needed for a new design, the hybrid approach will use the above classification scheme to retrieve portions or `slices` of several plans that are relevant for corresponding portions of the design, and then will combine these slices and modify them to produce a plan for the new design. If successful, this work will have the following benefits. First, it will combine, in an innovative way, the strengths of both variant and generative process planning. Like traditional variant process planning, the proposed approach to process planning will construct process plans that the process engineer may need to improve. However, by automatically adapting the retrieved plans to the new design requirements, it will minimize the need for such improvements, thus producing more successful implementations and applications than previous methods. Second, the proposed work will develop techniques that help design and manufacturing engineers effectively use legacy data and adapt it to new problems. Since it is estimated that practicing engineers spend more than 80 percent of their time searching through legacy data, catalogs, and earlier engineering projects, this should have significant positive impact on engineering practice.