Overview
SIST-TP CEN ISO/ASTM TR 52958:2026 specifies a workflow for the detection of lack of fusion flaws during the additive manufacturing of metals using powder bed fusion-laser based (PBF-LB) processes. This standard focuses on in-situ monitoring using coaxial photodiode sensors and the employment of statistical and machine learning algorithms for real-time flaw detection. The methodology includes experimental procedures for creating intentionally seeded flaws, setting and optimizing detection thresholds, and validating detection algorithms against computed tomography (CT) data. It is developed to help manufacturers improve process quality, ensure structural integrity, and advance in-process inspection capabilities.
Key Topics
- In-situ Coaxial Photodiode Monitoring: Implementation and calibration of a coaxial photodiode sensor aligned with the laser beam path for real-time capture of melt pool light intensity.
- Detection of Lack of Fusion Flaws: Emphasis on identifying lack of fusion-induced flaws, a key defect affecting the mechanical properties of metal additively manufactured components.
- Experimental Workflow: Preparation of test coupons featuring intentionally seeded flaws and process-induced stochastic flaws for algorithm validation and calibration.
- Algorithm Application:
- Statistical Algorithms: Use of thresholding, moving averages, and signal perturbation analysis for flaw detection.
- Clustering Machine Learning Algorithms: Application of unsupervised clustering methods, such as self-organizing maps (SOM) and K-means, for data-driven identification of process anomalies without pre-labelled data.
- Validation using Computed Tomography (CT): Use of high-resolution CT scanning for ex-situ validation of in-situ detected flaws, ensuring accuracy and reliability of the proposed process.
- Multi-laser Considerations: Coverage of hardware limitations and the potential challenges inherent in multi-laser powder bed fusion systems.
Applications
The standard provides practical value for industries and organizations involved in metal additive manufacturing using PBF-LB. Key applications include:
- Quality Assurance in Additive Manufacturing: Enables real-time monitoring and detection of process-induced defects, reducing the risk of undetected flaws and the likelihood of expensive post-production inspection failures.
- Process Optimization: Facilitates tuning of PBF-LB process parameters and machine learning models to maximize component integrity by understanding and mitigating defect formation.
- Research and Development: Offers a robust, standardized methodology for researchers developing novel monitoring solutions or studying defect formation mechanisms in laser-based powder bed fusion systems.
- Scaling for Industrial Production: Provides guidance for scaling up PBF-LB processes, including multi-laser installations, with standardized monitoring workflows for reliable production output.
Related Standards
For comprehensive implementation and interoperability, the following standards are relevant:
- ISO/ASTM 52900: Additive manufacturing - General principles, terminology, and vocabulary.
- ISO/ASTM 52906: Additive manufacturing - Methodologies for determining and quantifying metal powder bed fusion defects.
- ISO/ASTM 52915: Additive manufacturing - File format support (AMF) for modelling.
- ISO/ASTM 52910: Design - Requirements, guidelines, and recommendations.
Conclusion
SIST-TP CEN ISO/ASTM TR 52958:2026 delivers essential guidance for the detection and analysis of lack of fusion flaws in PBF-LB metal additive manufacturing using coaxial photodiode monitoring and advanced analytical algorithms. By standardizing monitoring workflows and validation methods, it supports improved product quality, process reliability, and industry best practices in additive manufacturing. Manufacturers, quality engineers, and researchers can leverage this standard to implement effective in-situ monitoring systems, enhance defect detection, and contribute to the safe adoption of metal AM in critical applications.
