Shape Deviation Generator and Learner - An Engineering-Informed Convolution Modeling and Learning Framework for Additive Manufacturing Accuracy Control

形状偏差生成器和学习器 - 用于增材制造精度控制的工程知情卷积建模和学习框架

• 批准号: 1901514
• 负责人: Qiang Huang
• 金额: $ 35万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901514&HistoricalAwards=false
• 关键词: Shape; Deviation; Generator; Learner; Engineering

Although additive manufacturing (AM), known as 3D Printing, holds great promise as a direct manufacturing technology, significant deviations from the desired part shape often occur in the printed parts. As a result,shape distortion control is a critical issue for AM built products. With advances in computing and increased accessibility of AM product data, machine learning for AM has become a viable strategy for enhancing 3D printing performance. However, meaningful learning of engineering data requires effective integration of domain knowledge, making general-purpose machine learning methods difficult to apply. As a result, there is a critical need for an engineering-informed, data-analytical, machine learning framework for shape distortion control. Such a tool is essential to improving AM quality and reducing cost and waste. The project will establish an engineering-informed convolution modeling and learning methodological framework for AM distortion control. A Shape Deviation Generator and Learner for shape accuracy control will be researched by: (1) modeling 3D shape deviation generation by establishing a new convolution formulation for layer-by-layer fabrication processes, (2) transferring the 3D shape deviation model from a small set of training shapes to a wider variety of shapes by exploring and learning shape similarity under a cookie-cutter modeling framework, (3) transferring the shape deviation model between AM processes by exploring and learning process similarity through an effect equivalence framework, and (4) validating modeling and transfer learning methodologies in both polymer and metal AM processes. Methodologies and tools will be developed to mitigate both shape and process complexities towards the goal of building AM products with high geometric fidelity: from single shape to multiple shapes, and from single process to multiple AM processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

虽然增材制造（AM），即3D打印，作为一种直接制造技术，具有很大的前景，但在打印零件中经常会出现与所需零件形状的显著偏差。 因此，形状变形控制是AM制造产品的关键问题。随着计算技术的进步和AM产品数据可访问性的提高，AM的机器学习已成为提高3D打印性能的可行策略。然而，对工程数据进行有意义的学习需要有效地整合领域知识，这使得通用机器学习方法难以应用。因此，迫切需要一个工程信息，数据分析，机器学习框架的形状变形控制。 这种工具对于提高AM质量、降低成本和浪费至关重要。该项目将建立一个工程知情的卷积建模和学习方法框架的AM失真控制。用于形状精度控制的形状偏差生成器和学习器将通过以下方式进行研究：（1）通过建立用于逐层制造工艺的新卷积公式来对3D形状偏差生成进行建模，（2）通过在饼干模型框架下探索和学习形状相似性来将3D形状偏差模型从一小组训练形状转移到更广泛的各种形状，（3）通过效应等效框架探索和学习过程相似性，在AM过程之间转移形状偏差模型，以及（4）验证聚合物和金属AM过程中的建模和转移学习方法。将开发方法和工具，以减轻形状和工艺的复杂性，实现构建具有高几何保真度的增材制造产品的目标：从单一形状到多种形状，从单一工艺到多个增材制造工艺。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling

• DOI: 10.1016/j.jmsy.2020.04.001
• 发表时间: 2020-07-01
• 期刊: JOURNAL OF MANUFACTURING SYSTEMS
• 影响因子: 12.1
• 作者: Decker, Nathan;Wang, Yuanxiang;Huang, Qiang
• 通讯作者: Huang, Qiang

Automatic Feature Selection for Shape Registration in Additive Manufacturing

增材制造中形状配准的自动特征选择

• 发表时间: 2020
• 期刊: IISE ANNUAL CONFERENCE & EXPO
• 作者: Lin, Weizhi;Dai, Peng;Huang, Qiang
• 通讯作者: Huang, Qiang

Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing

用于预测增材制造中 3D 形状变形的扩展制造感知卷积学习框架

• 发表时间: 2021
• 期刊: IEEE International Conference on Automation Science and Engineering CASE
• 作者: Wang, Yuanxiang;Ruiz, Cesar;Huang, Qiang
• 通讯作者: Huang, Qiang

Optimizing the Expected Utility of Shape Distortion Compensation Strategies for Additive Manufacturing

优化增材制造形状畸变补偿策略的预期效用

• DOI: 10.1016/j.promfg.2021.06.038
• 发表时间: 2021
• 期刊: Procedia Manufacturing
• 作者: Decker, Nathan;Huang, Qiang
• 通讯作者: Huang, Qiang

Learning and Predicting Shape Deviations of Smooth and Non-Smooth 3D Geometries Through Mathematical Decomposition of Additive Manufacturing

• DOI: 10.1109/tase.2022.3174228
• 发表时间: 2023-07
• 期刊: IEEE Transactions on Automation Science and Engineering
• 影响因子: 5.6
• 作者: Yuanxiang Wang;C. Ruiz;Qiang Huang