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Leading up to the annual RAPID + TCT conference and exhibit, it’s a great time to reflect on how far the additive industry has come, where additive manufacturing (AM) is going at the intersection of digital + additive, and how CCAT and other partners and collaborators are helping reduce barriers to AM adoption—especially for small businesses and manufacturers.
The Journey
Historically, AM was mainly used for prototyping. Since AM was invented in the 1980s by Chuck Hull, who is credited with inventing SLA, the journey has included expansion through industrial exploration and consumer hype in the 2000s, followed by industrialization through integration with digital systems—with an emphasis on production and supply chain impact in the 2020s.
AM is no longer just a machine technology; it is part of a digitally connected manufacturing system and additive workflows. Digital transformation is what enables industrial-scale use. Integrated additive digital workflows—including end-to-end production planning, real-time monitoring and control, and supply chain integration—are leading to AM being used to create end-use parts.
The Role of Model-Based Definition (MBD) and Digital Thread in AM
Significant business impact occurs when technical data is connected across engineering, production, and quality systems. One of the biggest advantages is data reuse, which eliminates repetitive data entry and reduces human error. Smart software verifies data, corrects errors, and creates a single source of truth across the lifecycle.
Technologies such as digital twins, model-based engineering, and advanced analytics deliver value when operating within a connected data ecosystem. AM strategies thrive in this type of digital platform. MBD + AM together enable faster certification due to reduced manual documentation, improved repeatability through clear digital definition, and a scalable digital thread.
Digital design and simulation (CAD, generative design, digital twins) enable complex geometries that only AM can produce. MBD reduces trial-and-error and lowers adoption risk. Implementing a digital thread platform—including IoT, edge devices, smart manufacturing, and a manufacturing execution system (MES)—connects design, machines, quality processes, and suppliers across the supply chain. AI and data analytics optimize print parameters, predict defects, and improve yield.
For small manufacturers, MBD is the foundation that makes AM scalable and certifiable. CCAT recommends advancing both together in parallel—not separately. Without digital workflows, AM remains isolated and underutilized.
Digital thread and MBD initiatives are more likely to be sustained when tied to business outcomes: streamlined communication (SSOT), faster time-to-market, cost reduction and efficiency, improved quality and reduced scrap, enhanced collaboration, digital thread integration, and supply chain resiliency.
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Seek Out Strong Support and Assistance in Digital Transformation + Additive Adoption
It is not a question of business impact vs. technological trends—effective technologies rise and are adopted when service providers can clearly demonstrate the business case through hands-on application, as CCAT does.
Traditional AM barriers include high capital cost, quality variability, limited scalability, and integration challenges. Digital transformation (DT) mitigates these by enabling predictive quality and in-process monitoring, resulting in fewer defects and higher repeatability. DT provides traceability to support qualification and certification and enables simulation before production, reducing failed builds and material waste. Automation and verification improve throughput and consistency.
These improvements lead to stronger, risk-adjusted ROI. CCAT and other service providers can demonstrate this before a company invests—validating AM on real parts and de-risking adoption.
Selecting the right applications—whether finished parts or in-process tooling—is critical to achieving ROI. Manufacturers should seek guidance to avoid underutilized equipment.
CCAT, a nonprofit serving the Northeast for over 20 years, offers an integrated AM adoption model, including:
- AM maturity assessments
- Application identification (parts, tooling, fixtures)
- Demonstration and validation
- Roadmaps and recommendations
- Onboarding support
- Supplier funding guidance
- Hands-on technical training and talent development
Training is offered in East Hartford, Connecticut, at the Additive Workflows Lab and Digital Thread Lab.
National organizations supporting AM qualification and advancement include the U.S. Naval Sea Systems Command, SME, America Makes, and the National Institute of Standards and Technology (NIST).
The Role of Artificial Intelligence (AI) in AM
AI + AM is advancing defect detection in industries producing safety-critical systems. Recent research shows that machine learning (ML) and deep learning (DL) techniques—such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs)—are widely used to identify defects like porosity, delamination, and dimensional inaccuracies.
Hybrid AI models that integrate ML and DL demonstrate improved performance in detecting complex defects. Studies show that combining multimodal data—such as thermal imaging, acoustic signals, and optical measurements—can improve defect detection rates by an average of 22%, enhancing model accuracy and robustness [Khan et al. 1].
Additional research shows similar improvements, increasing the likelihood of producing certified AM parts. [2, 3, 4, 5]
See you at RAPID +TCT in Boston
RAPID + TCT, North America’s largest additive manufacturing and industrial 3D printing event, will take place in Boston from April 13–16, 2026. The expansive show floor features hands-on exhibits from over 400 leading providers.
Each morning, the Executive Perspectives Keynote Series brings industry leaders together to examine the present and chart the future of additive manufacturing.
Visit CCAT at Booth #2435 to hear from clients and partners about progress in digital + additive and how collaboration is advancing adoption, training, and the next generation of talent. Featured partners include The Barnes Global Advisors (TBGA), Novo Precision, Guill Tool & Engineering, University of Maine, Quinnipiac University, and CBIZ.
To learn more about RAPID + TCT 2026, visit rapid3devent.com
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About the Author
Dr. Amy Thompson currently serves as the Chief Technology Officer (CTO) for the Connecticut Center for Advanced Technology (CCAT). In this role, she leads the technology, talent development, and training teams as a unified powerhouse that delivers end-to-end services to manufacturers, supporting their technology adoption journey. Dr. Thompson helms the development and implementation of CCAT’s technical training programs and courses, supporting small and medium-sized manufacturers (SMMs) throughout Connecticut and the northeast region. Prior to joining CCAT in 2023, Dr. Thompson was an Associate Professor-In-Residence of Systems Engineering at the University of Connecticut and the Associate Director for the Pratt & Whitney Institute for Advanced Systems Engineering. She earned a B.S. in Industrial Engineering, an M.S. in Manufacturing Engineering, and a Ph.D. in Industrial and Systems Engineering from the University of Rhode Island. Her technical expertise includes model-based systems engineering (MBSE), the design and operation of smart, connected, and energy-efficient manufacturing and building systems, supply chain design, and engineering education. Her research has been funded by the U.S. Department of Energy, NIUVT, AFRL, Pratt & Whitney, Eversource, and United Illuminating.
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References
1. Khan, Md Rabbi, Jareer Murtaza Amin, Zubair Hossain Mahamud, and Mohammad Samiul Islam. "AI For Defect Detection in Additive Manufacturing: Applications In Renewable Energy And Biomedical Engineering." Available at SSRN 5106851 (2024).
2. Johnson, Marshall V., Kevin Garanger, James O. Hardin, J. Daniel Berrigan, Eric Feron, and Surya R. Kalidindi. "A generalizable artificial intelligence tool for identification and correction of self-supporting structures in additive manufacturing processes." Additive Manufacturing 46 (2021): 102191.
3. Gu, Sungmo, Minhyeok Choi, Hwijae Park, Sangjun Jeong, Jaehyeok Doh, and Sang-in Park. "Application of artificial intelligence in additive manufacturing." JMST Advances 5, no. 4 (2023): 93-104.
4. Kumar, Sharan, A. Mahamani, Yeshwant M. Sonkhaskar, Pandian Mani, and M. Harish. "AI-Enhanced Advanced Materials for Lightweight Additive Manufacturing in Aerospace & Automotive." In Advanced Materials for Biomedical Devices, pp. 8-19. CRC Press, 2026.
5. Kumar, Santosh, and Aayushi Gautam. "A comprehensive review on multifaceted role of AI in additive manufacturing: Applications, optimization, challenges, and future directions." Journal of Advanced Manufacturing Systems 25, no. 02 (2026): 365-404.
