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

尽管被称为3D打印的附加制造(AM)作为一种直接制造技术具有很大的前景，但在打印的零件中经常会出现与所需零件形状的重大偏差。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，工程数据的有意义的学习需要有效地集成领域知识，这使得通用的机器学习方法很难应用。因此，迫切需要一种基于工程知识、数据分析和机器学习的形状变形控制框架。这样的工具对于提高AM质量、降低成本和浪费是必不可少的。该项目将为AM失真控制建立一个基于工程知识的卷积建模和学习方法框架。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几个方面进行研究：(1)通过为逐层制造过程建立新的卷积公式来建模3D形状偏差生成；(2)通过在饼干切割器建模框架下通过探索和学习形状相似性来将3D形状偏差模型从较小的训练形状集转移到更广泛的形状；(3)通过探索和通过效果等价框架学习过程相似性来在AM过程之间传递形状偏差模型；(4)验证聚合物和金属AM过程中的建模和迁移学习方法。将开发方法和工具，以降低形状和过程的复杂性，以实现构建具有高几何保真度的AM产品的目标：从单一形状到多个形状，以及从单一过程到多个AM过程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.