The Latest Advancements in AI for Additive Manufacturing Monitoring & Quality Assurance with Isabelle Hachette, CEO of Interspectral
March 10, 2025 | Reading time: 5 min
The cross-pollination of industries often sparks groundbreaking innovation, and an expert transitioning from medical imaging to additive manufacturing (AM) is no exception.
In a recent episode of the Additive Snack Podcast, host Fabian Alefeld welcomed Isabelle Hachette, CEO of Interspectral, to discuss how insights from medical imaging are transforming AM process monitoring and quality assurance.
From Medical Imaging to Additive Manufacturing: A Data-Driven Evolution
With over two decades in medical imaging, Isabelle Hachette brings a wealth of experience in digitalization, data connectivity, and AI-driven diagnostics. In the healthcare sector, AI has revolutionized diagnostic accuracy, enabling radiologists to detect anomalies with unprecedented precision. This expertise is now being leveraged to refine AM processes, particularly in quality assurance and process monitoring.
Hachette shared how Interspectral’s journey into AM began through a research collaboration with Siemens Energy. Recognizing the vast amounts of data generated during the AM process, her team developed a solution to aggregate and analyze this data — creating a digital twin of each build job. This technology empowers engineers with deeper insights into their manufacturing processes, driving efficiency and precision.
Providing Actionable Insights in AM Production
At its core, Interspectral provides software solutions for quality assurance and process monitoring in AM. Its flagship platform, AM Explorer, serves as a powerful tool for engineers, offering automatic error detection and data visualization. The software integrates seamlessly with various data sources — point clouds, sensor data, optical tomography (OT), melt pool monitoring, and even CT scans — allowing manufacturers to gain a comprehensive understanding of their build jobs.
For companies like Volum-e, which operates multiple EOS machines, AM Explorer has dramatically reduced the time spent analyzing process data. Before implementing Interspectral’s solution, engineers manually sifted through large volumes of images to identify defects. Now, with automated error detection, they’ve cut down image analysis time by 90%, accelerating production and reducing costly trial-and-error cycles.
In addition, early detection of job crashes and faulty parts ensures that manufacturers identify errors and pinpoint their exact location. This level of insight has led to significant cost savings, with some companies reporting savings of up to € 50,000 per machine per year using Interspectral’s solution.
AI-Driven Error Detection and Process Optimization
In the medical imaging world, AI serves as a second reader for radiologists, assisting in the detection of tumors and other anomalies. Similarly, Interspectral’s AI-powered tools enhance AM process monitoring by identifying errors in real time and pinpointing root causes. Whether the issue stems from inadequate laser power, gas flow disruptions, or material inconsistencies, AM Explorer provides actionable insights to mitigate defects before they escalate.
“The ability to fuse multiple data sources and apply AI-driven analytics allows manufacturers to take proactive steps in optimizing their processes,” Hachette explained. “Rather than reacting to quality issues after production, we empower engineers to make informed decisions during the build.”
The Future of AM: AI, Digital Twins, and Standardization
As AI advancements accelerate across industries, AM stands to benefit from these innovations in unprecedented ways. Recent technological leaps, such as Nvidia’s latest AI chip for robotics and autonomous systems, indicate that AI advancements in just a single quarter are now outpacing those of an entire previous year. This rapid development underscores the transformative potential of AI-driven process monitoring in AM.
Hachette envisions a future where AI-driven process monitoring works hand in hand with simulation tools, reducing development cycles for new materials, processes, and applications. Additionally, by integrating digital twin technology, manufacturers can continuously refine their products even after they’ve entered real-world use.
However, one of the biggest hurdles remains standardization. Unlike the medical imaging industry, which operates on widely adopted standards such as DICOM, AM still lacks a universal framework for data connectivity and archiving. Interspectral is actively working with industry partners like EOS to establish new quality assurance benchmarks, ensuring that AM can meet the stringent demands of production environments.
Paving the Way for Scalable AM Adoption
Beyond prototyping, scalable AM adoption requires robust process monitoring to ensure repeatability and reliability. By reducing reliance on manual inspection and enabling early detection of defects, solutions like AM Explorer lower the barriers to entry for manufacturers looking to integrate AM into their production workflows.
“Interspectral is dedicated to pushing AM forward,” Hachette concluded. “By leveraging AI, data fusion, and automation, we aim to make additive manufacturing not just a viable alternative to traditional methods, but a preferred solution for high-performance production.”
Listen and Learn More
To explore how medical imaging principles are shaping the future of AM, tune in to the full episode of the Additive Snack Podcast. Gain insights from industry leaders like Isabelle Hachette and discover how data-driven decision-making is unlocking new frontiers in additive manufacturing.
For more information about Interspectral’s innovative solutions, visit their website and connect with Isabelle Hachette on LinkedIn.
