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Expert System for Online Defect Detection in Medical Devices Produced by Electron Beam Melting Using Layer-by-Layer

Amedeo Franco Bonatti¹, Francesco Domenico Meringolo¹, Ilaria Tubertini²

¹Research Center "E. Piaggio" and Department of Information Engineering, University of Pisa, Pisa, Italy.

3D Printing and Additive Manufacturing

|March 28, 2025

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An expert system algorithm automates porosity detection in 3D-printed medical implants using electron beam melting (EBM). This nondestructive quality control (QC) ensures patient safety by identifying critical defects in cobalt-chromium alloy prosthetics.

Area of Science:

Additive Manufacturing
Materials Science
Medical Device Manufacturing

Background:

Online, nondestructive quality control (QC) is crucial for medical device manufacturing, ensuring patient safety and compliance with standards.
Electron Beam Melting (EBM) of metallic implants can introduce defects like porosities, impacting structural integrity and mechanical properties.
Porosities are critical defects in orthopedic prosthetics, potentially compromising fatigue behavior.

Purpose of the Study:

To develop an expert system algorithm for automated detection of porosities in 3D-printed prosthetic components.
To enhance the quality control process for cobalt-chromium alloy tibial trays manufactured via EBM.
To provide a nondestructive evaluation method supporting part qualification and improving upon manual inspection.

Keywords:

artificial intelligence defect detection electron beam melting expert system quality control

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Main Methods:

An expert system algorithm was designed to analyze images from an embedded camera in an Arcam Q10plus printer.
The algorithm performs automated, quantitative assessment of porosity size and location, identifying high-density defect areas.
Defect detection performance was evaluated via layer-wise detection and large pore identification tasks, with validation against computed tomography (CT).

Main Results:

The algorithm achieved a sensitivity of 91% and a precision of 76% in defect detection.
Validation against the gold-standard computed tomography (CT) showed a 98% agreement, confirming the algorithm's reliability.
The system enables rapid evaluation of entire print volumes, offering a fast and reliable QC tool.

Conclusions:

The developed expert system provides an effective, automated solution for detecting critical porosities in EBM-printed prosthetic components.
This nondestructive evaluation method significantly improves upon traditional visual inspection, offering quantitative data and enhanced speed.
The algorithm represents a reliable tool for quality control in medical device manufacturing, ensuring the safety and integrity of patient implants.