RUI: Layered Manufacturing Decision Support and Design Feedback

这项研究将开发一种方法，用于自动确定给定零件的实体自由曲面制造(SFF)工艺性能。该项目的主要目标是为设计工程师提供制造和原型决策的知识和方法，从而改善SFF部件的设计和制造之间的接口，并为设计反馈提供重要的评估能力。该项目计划包括以下内容的制定、开发和软件实施：(1)用于从计算机辅助设计模型中提取基于特征的工艺信息的公差表示方案、算法和方法，(2)用于定义优化特定于特征的精度约束的零件方向的算法解决方案，以及(3)用于量化和评估制造特征的质量的计算机化工艺模型。该项目旨在关注小企业制造商的特殊需求，他们(本质上)有时会受到劳动力较少的限制。决策支持系统的演示和验证将利用通过与当地制造商和小型企业发展中心(SBDC)合作提供的行业数据。如果成功，这项研究将提供节省时间的工具，促进快速识别设计可能性和及早整合制造考虑因素。SFF吞吐量的提高将由基于功能的优化例程的自动化来补充。开发的方法将突出可制造性评估中的重要元素，并演示如何在调查重新设计方案时将它们与产品模型联系起来。改进的决策支持和评估能力还将有助于促进分层制造方法的进一步使用和行业认可，而不仅仅是一种快速原型技术。

This research will develop a methodology for the automated determination of Solid Freeform Fabrication (SFF) process performance for a given part. The primary goal of this project is to provide the knowledge and the means through which manufacturing and prototyping decisions can be made by the design engineer, thus improving the interface between the design and the manufacture of SFF parts, and providing important evaluation capabilities for design feedback. The project plan includes the formulation, development, and software implementation of: (1) tolerance representation schemes, algorithms, and methods for extracting feature-based process information from computer-aided design models, (2) algorithmic solutions for defining part orientations that optimize feature-specific precision constraints, and (3) a computerized process model used to quantify and assess the quality of fabricated features with minimum human effort. The project intends to focus on the special needs of small business manufacturers, who (by nature) sometimes suffer from the limitations of a smaller work force. Demonstration and validation of the decision support system will utilize industry data provided through collaboration with local manufacturers and the Small Business Development Center (SBDC). If successful, this research will serve to provide time-saving tools that facilitate the rapid identification of design possibilities and the early integration of manufacturing considerations. SFF throughput improvements will be complemented by the automation of feature-based optimization routines. The developed methodology will highlight the important elements in manufacturability assessment, and demonstrate how they can be linked back to the product model in investigating redesign options. Improved decision support and assessment capabilities will also serve to promote the further use and industry approval of layered manufacturing methods as more than a rapid prototyping technique.