Additive Assurance's AMiRIS software is being evaluated by an unnamed aerospace and defense company to standardize process monitoring data across multiple metal 3D printing platforms. The software is designed to consolidate measurement data from different laser powder bed fusion (LPBF) systems into a common analysis layer, addressing a key challenge for manufacturers operating across diverse machine platforms and supply chains.
During the LPBF process, thermal signals generated throughout the build are used to identify process anomalies that may indicate irregularities within individual layers. However, machine manufacturers typically rely on proprietary sensors and evaluation algorithms, making it difficult to establish a consistent quality assurance approach across different systems. AMiRIS aims to provide a manufacturer-independent analysis framework for process monitoring.
"As an aerospace company, we qualify components not only on a single machine family," says a senior engineer from the OEM. "If we truly want to scale additive manufacturing (AM), we need an approach that we can deploy throughout our entire supply chain without having to redefine procedures with every platform change. The appeal of AMiRIS is that it provides us with a common analysis level instead of binding us to the proprietary algorithms of individual manufacturers."
The company emphasized that process monitoring should not be viewed as direct defect detection. Instead, in-situ monitoring identifies process anomalies that may be associated with pores, lack of fusion, or other material deviations, requiring validation through established inspection techniques.
"We choose our terms carefully," adds the engineer. "In most cases, a process anomaly is detected during the process, rather than a 'defect detected,' which may cause a defect. The key is the correlation: What does the thermal signal indicate, and what do computed tomography, destructive component analysis, or other non-destructive testing methods (NDT) reveal? If these results cannot be correlated, transitioning to serial production is not possible."
According to the company, reliable quality assurance depends on correlating thermal process data with results obtained from computed tomography (CT), destructive section analysis, and other non-destructive testing (NDT) methods. Only this validation confirms whether detected anomalies correspond to actual component defects.
The challenge becomes more significant for large and complex metal components, where conventional post-process inspection methods can be costly and technically constrained.
"For large and complex components, the question of testing becomes existential," says the engineer. "Computed tomography can be very expensive, and some geometries simply cannot be tested as we would like. In-situ monitoring is the only approach that appears scalable – provided the results are processed automatically, cleaned of noise signals, and practically usable."
Additive Assurance stated that AMiRIS was developed to bridge the gap between research-focused process monitoring and practical industrial quality assurance by providing automated analysis and reporting capabilities that integrate into existing manufacturing workflows.
"Teams in aerospace do not need more charts. They need reliable evidence that can be verified and stored long-term – without turning every manufacturing order into a comprehensive investigation. AMiRIS was developed to support this path: with automated analyses, reporting that integrates into real workflows, and deployment options that work even in controlled environments."
Large-format metal components present additional inspection challenges, as computed tomography can become expensive, time-consuming, and technically limited for large dimensions or complex internal geometries. Automated process monitoring data could help identify areas requiring further inspection, reducing the scope of post-process evaluation.
The aerospace company is continuing its assessment of AMiRIS. A broader deployment will depend on whether process anomalies identified during manufacturing can be consistently correlated
with subsequent inspection results, enabling process monitoring to serve as a reliable quality assurance tool for serial production.
