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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 for intersections of Digital + Additive, and how CCAT and other partners and collaborators in our industry are bringing down barriers to AM adoption for especially small businesses and manufacturers.
The Journey
Historically, AM was mainly used for prototyping. Since AM was invented in the 1980’s by Chuck Hull, credited with inventing SLA, the journey included expansion through industrial exploration and consumer hype through the 2000’s, to industrialization by integration with digital systems with an emphasis on production and supply chain impact in the 2020’s. 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 support, including end-to-end production planning, real-time monitoring and control, and supply chain integration is leading to AM being used to create end-use parts.
The Role of Model-Based Definition (MBD) and Digital Thread in AM
Big business impacts occur when technical data is connected across engineering, production, and quality systems. The biggest time saver is data reuse, which eliminates repetitive data entry and human data errors, because data is checked and verified by smart software, finding and correcting human errors, and creating a single source of truth across the lifecycle. Technologies such as digital twins, model-based engineering, and advanced analytics deliver value when they operate within a connected data ecosystem, and AM strategies thrive in this type of digital platform and system. MBD + AM together enables faster certification due to less manual documentation, better repeatability due to clear digital definition, and a resulting scalable digital thread.
Digital design & simulation (CAD, generative design, digital twins) enables complex geometries that only AM can produce. MBD reduces trial-and-error and lowers adoption risk. Implementing a digital thread platform that includes IoT, edge devices, smart manufacturing, and a manufacturing executions system (MES) connect design to machines to quality processes to suppliers and the supply chain. AI and data analytics optimize print parameters, predict defects, and improve yield.
For a small manufacturer, MBD is the foundation that makes AM scalable and certifiable. The best recommendation that CCAT makes is to do them together and make progress in parallel, not separately. Without digital workflows, AM remains isolated and underutilized. Digital thread and MBD initiatives will be more likely to sustain when the MBD implementations are tied to business impacts and outcomes: Streamlined Communication (SSOT), Faster Time-to-Market, Cost Reduction & Efficiency, Improved Quality Rates and Reduced Scrap, Enhanced Collaboration, Digital Thread Integration, 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, reputable and good technologies will rise, flourish, and become adopted when service providers can demonstrate and show the business case hands-on on actual customer products, like we do at the Connecticut Center for Advanced Technology (CCAT). Traditional AM barriers include high capital cost, quality variability, limited scalability, and integration with existing systems. Digital transformation (DT) mitigates them by adding powerful predictive quality & in-process monitoring leading to fewer defects and higher repeatability. DT adds clear traceability supporting qualification and certification of components. DT adds the ability to move manufacturing to the digital space and to simulate before producing, reducing failed builds and material waste. Work is automated and verified and improves throughput and consistency.
These improvements result in risk-adjusted return on investment (ROI). CCAT and other service providers can demonstrate this for clients before a company buys, demonstrating AM for their own parts and products, derisking acquisition. Selecting the correct finished parts or in-process tools and fixtures leading to real ROI is key, so manufacturers should seek help to find the right applications, so machines don’t end up in the corner unused. CCAT, a non-profit serving the northeast region for over 20 years, offers an integrated AM adoption model for manufacturers including AM maturity assessments, selecting best parts and in-process tools and fixtures for AM, demonstration, validation, roadmaps and recommendations, onboarding support, supplier funds, hands-on technical training, talent development, and technical support and expertise. CCAT offers technical training in East Hartford, Connecticut in its Additive Workflows Lab and Digital Thread Lab.
National organizations supporting AM qualification, certification, and AM advancement in industry include the U.S. Naval Sea Systems Command, SME, America Makes Manufacturing Institute, and the U.S. National Institute of Standards and Technology.
The Role of Artificial Intelligence (AI) in AM
AI + AM can transform AM by advancing defect detection in manufacturing industries that produce safety critical systems. Recent research demonstrates 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 common defects like porosity, delamination, and dimensional inaccuracies. Hybrid AI models, integrating ML and DL, demonstrated superior performance in detecting complex, multi-dimensional defects across various AM applications and integrating multimodal data like thermal imaging, acoustic signals, and optical measurements, were reported in the literature to improve defect detection rates by an average of 22%, enhancing the robustness and accuracy of AI models. [Khan et al., 1] Other recent AI research shows similar big improvements leading to more likelihood of producing certified AM parts. [2, 3, 4, 5]
See you at RAPID +TCT in Boston
RAPID is North America's largest additive manufacturing and industrial 3D printing event, RAPID + TCT, and runs in Boston from April 13-16, 2026. The event’s expansive show floor features hands-on 3D printing technology exhibits from over 400 of the industry’s leading product and service providers. Each morning, at the Executive Perspectives Keynote Series, industry leaders candidly examine the present while charting a course for the future of additive manufacturing. You can find the assistance and information you need to start or continue your AM journey.
Come see CCAT at our booth #2435, and hear from our clients and partners on the great strides being made in Digital + Additive, and how partners are supporting not only adoption and technical training, but also preparing the next generation of AM technicians, engineers, and businesses: The Barnes Global Advisors (TBGA), NOVO Precision, Guill Tool & Engineering, University of Maine, Quinnipiac University, and CBIZ.
RAPID + TCT 2026: 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.
