RUI: Layered Manufacturing Decision Support and Design Feedback

本研究将开发一种方法，用于自动确定固体自由成形制造（SFF）工艺性能的给定部分。 该项目的主要目标是提供知识和方法，通过这些知识和方法，设计工程师可以做出制造和原型设计决策，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。 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过程信息的公差表示方案、算法和方法，（2）用于定义优化特征特定精度约束的部件取向的算法解决方案，以及（3）用于以最少的人力来量化和评估制造特征的质量的计算机化工艺模型。 该项目打算侧重于小企业制造商的特殊需要，他们（由于天性）有时会受到劳动力规模较小的限制。 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，这项研究将有助于提供节省时间的工具，促进快速识别设计的可能性和早期集成的制造考虑。 SFF吞吐量的提高将通过基于功能的优化例程的自动化来补充。 所开发的方法将突出可制造性评估中的重要元素，并演示如何将它们与重新设计方案中的产品模型联系起来。 改进的决策支持和评估能力也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.