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

尽管被称为3D打印的增材制造（AM）作为一种直接制造技术具有很大的前景，但打印出来的零件经常会出现与所需零件形状的显著偏差。因此，形状变形控制是增材制造产品的关键问题。随着计算技术的进步和增材制造产品数据的可访问性的提高，增材制造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对领域知识进行有效的整合，这使得通用的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，机器学习框架来控制形状变形。这样的工具是必不可少的，以提高增材制造质量，减少成本和浪费。该项目将为AM失真控制建立一个工程信息卷积建模和学习方法框架。本文将研究一种用于形状精度控制的形状偏差发生器和学习器：(1)通过建立新的逐层加工工艺卷积公式，对三维形状偏差生成进行建模；(2)在千篇式建模框架下，通过探索和学习形状相似度，将三维形状偏差模型从一小组训练形状转移到更广泛的形状；(3)通过效果等效框架，通过探索和学习过程相似度，在增材制造过程之间转移形状偏差模型。(4)验证聚合物和金属增材制造过程中的建模和迁移学习方法。将开发方法和工具，以减轻形状和工艺的复杂性，以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。该奖项反映了美国国家科学基金会的法定使命，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。

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.