Monitoring 3D Lattice Structures in Additive Manufacturing Using Topological Data Analysis

2025-10-10

AI Summary

Real-time monitoring of topological defects in hollow lattice structures—widely used in aerospace and biomedical implants fabricated via additive manufacturing—remains a critical challenge. Method: This paper introduces persistent homology, for the first time, into lattice quality control, proposing a nonparametric statistical process control (SPC) framework based on zero-dimensional (connected components) and one-dimensional (circular voids) homological features. We construct topological feature control charts and design hypothesis-testing strategies to enable intrinsic geometric-aware, real-time monitoring of point cloud or mesh data. Contribution/Results: Evaluated on diverse simulated lattice datasets incorporating realistic process-induced defects, our method demonstrates superior sensitivity and robustness in detecting subtle topological anomalies compared to conventional SPC approaches. It achieves high detection accuracy while preserving geometric fidelity, establishing a novel paradigm for quality assurance of complex 3D periodic architectures.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

We present a new method for the statistical process control of lattice structures using tools from Topological Data Analysis. Motivated by applications in additive manufacturing, such as aerospace components and biomedical implants, where hollow lattice geometries are critical, the proposed framework is based on monitoring the persistent homology properties of parts. Specifically, we focus on homological features of dimensions zero and one, corresponding to connected components and one-dimensional loops, to characterize and detect changes in the topology of lattice structures. A nonparametric hypothesis testing procedure and a control charting scheme are introduced to monitor these features during production. Furthermore, we conduct extensive run-length analysis via various simulated but real-life lattice-structured parts. Our results demonstrate that persistent homology is well-suited for detecting topological anomalies in complex geometries and offers a robust, intrinsically geometrical alternative to other SPC methods for mesh and point data.

Problem

• Monitoring topological changes in 3D lattice structures during manufacturing
• Detecting anomalies in connected components and loops using persistent homology
• Providing statistical process control for additive manufacturing of complex geometries

Innovation

• Monitoring lattice structures via topological data analysis
• Using persistent homology for detecting topological anomalies
• Implementing nonparametric testing and control charting schemes