Bonatti Amedeo Franco, Meringolo Francesco Domenico, Tubertini Ilaria, Lavecchia Carolina Eleonora, Favaro Alberto, Vozzi Giovanni, De Maria Carmelo
Research Center "E. Piaggio" and Department of Information Engineering, University of Pisa, Pisa, Italy.
Rejoint Srl, Castel Maggiore, Italy.
3D Print Addit Manuf. 2025 Feb 13;12(1):36-47. doi: 10.1089/3dp.2023.0222. eCollection 2025 Feb.
The implementation of online, nondestructive quality control (QC) solutions is a key aspect to consider especially in medical device manufacturing, where the production process should comply to relevant quality-related standards to guarantee a safe product for the patient. For metallic implants fabricated using electron beam melting (EBM), the presence of defects (e.g., porosities, cracks, delamination, balling effect) could affect the final part quality in terms of structural integrity and mechanical properties. In this context, we propose an expert system algorithm capable of automated detection of porosities that could occur in prosthetic components, such as tibial trays, printed using a cobalt-chromium alloy. The choice of focusing only on the porosities was done as these defects are particularly critical for orthopedic prosthesis, where they can negatively impact the fatigue behavior of the printed component. The algorithm was developed to analyze images of the manufacturing process taken from a camera embedded in the printer (Arcam Q10plus). Images can be used to identify porosities, performing a nondestructive evaluation that supports the process of part qualification. The developed algorithm automates and improves the visual inspection task conducted by human experts, including quantitative assessment on the size and location of the porosities, and reporting the presence of high porosity density areas. The defect detection performance was evaluated through the design of two tasks: The layer-wise defect detection and the large pore identification. The defect detection was performed with a sensitivity of 91% and a precision of 76%. Furthermore, a comparison with the gold-standard nondestructive evaluation technique, that is, computed tomography evaluation, allowed to validate the algorithm (percent agreement of 98%). The developed expert system allows to quickly evaluate an entire printing volume with several components representing a reliable and fast tool for defect detection and QC of EBM-printed prosthetic components.
实施在线无损质量控制（QC）解决方案是一个需要特别考虑的关键方面，尤其是在医疗器械制造中，生产过程应符合相关的质量标准，以确保为患者提供安全的产品。对于使用电子束熔炼（EBM）制造的金属植入物，缺陷（如孔隙、裂纹、分层、球化效应）的存在可能会在结构完整性和机械性能方面影响最终部件的质量。在此背景下，我们提出了一种专家系统算法，能够自动检测使用钴铬合金打印的假体部件（如胫骨托盘）中可能出现的孔隙。之所以选择只关注孔隙，是因为这些缺陷对骨科假体尤为关键，它们会对打印部件的疲劳行为产生负面影响。该算法旨在分析从打印机（Arcam Q10plus）中嵌入的摄像头获取的制造过程图像。这些图像可用于识别孔隙，进行无损评估，以支持部件鉴定过程。所开发的算法实现了人工专家进行的视觉检查任务的自动化并加以改进，包括对孔隙大小和位置的定量评估，以及报告高孔隙密度区域的存在情况。通过设计两项任务对缺陷检测性能进行了评估：逐层缺陷检测和大孔隙识别。缺陷检测的灵敏度为91%，精度为76%。此外，与金标准无损评估技术（即计算机断层扫描评估）进行比较，验证了该算法（一致性百分比为98%）。所开发的专家系统能够快速评估包含多个部件的整个打印体积，是一种用于EBM打印假体部件缺陷检测和质量控制的可靠且快速的工具。
