Researchers at NOVA University of Lisbon have developed a new approach to evaluating additive manufacturing (AM) maturity, offering a more precise way for companies to assess their progress.
Having applied fuzzy logic, the researchers created a model that captures the nuances of AM adoption, addressing the gaps in traditional maturity models that often fail to account for real-world complexities.
While AM has become a key part of Industry 4.0, seamlessly integrating it into manufacturing workflows is far from straightforward. Many organizations find themselves in a transitional phase, where AM is being used to some extent but hasn’t yet become a core part of production.
Traditional maturity models often categorize companies too rigidly, making it difficult to identify specific areas for improvement. To address this limitation, the fuzzy AM maturity model (Fuzzy AMMM) offers a more flexible assessment that accounts for human judgment and the uncertainties inherent in technological adoption.
Evaluating AM maturity across strategy, workforce, and technology
Published in Procedia Computer Science journal, the model evaluates AM maturity across three key areas: organizational, cultural, and technological. Organizational maturity looks at whether AM is integrated into a company’s strategy, how committed leadership is, and the level of investment in AM projects.
Cultural maturity assesses workforce readiness, employee skills, and openness to AM adoption, while technological maturity focuses on how well AM is implemented in production processes, including CAD software use and part manufacturing.
To put the model to the test, the researchers conducted a case study at an undisclosed automotive supplier that has been using AM for five to ten years.
The assessment involved a combination of surveys and interviews with company representatives, including an operations manager with expertise in mechanical engineering. Results indicated an overall AM maturity level of 3, placing the company at an intermediate stage where AM is in use but has yet to significantly impact production.
A closer look at the scores revealed a strong performance in organizational and technological maturity, both ranking at 4.68, suggesting that the company has integrated AM into its strategy and production workflow. However, cultural maturity lagged at 3.00, pointing to a gap in workforce training.
Employees showed proficiency in CAD software, design for additive manufacturing (DFAM), and slicing software, but there was a noticeable lack of personnel with leadership skills to oversee AM operations. Additionally, while some external training initiatives exist, there is no structured internal competency development program, limiting long-term growth.
One of the study’s key findings was a disconnect between leadership’s commitment to AM and employee readiness. The company had made substantial financial investments and adapted its processes, but without dedicated AM specialists and structured training programs, it faced challenges in maximizing AM’s potential.
This underscores a broader issue seen across industries, having the right technology in place is only part of the equation. Without the necessary skills and expertise among employees, full integration remains out of reach.
By applying fuzzy logic, the researchers were able to capture a more accurate and realistic picture of AM maturity compared to conventional models, which tend to oversimplify the assessment process. According to the researchers, the Fuzzy AMMM not only helps organizations determine where they stand but also highlights the specific areas that need improvement.
Future research aims to expand the model’s application across multiple industries, refining its framework to support organizations as they navigate their AM journey. With clearer insights, businesses can make more informed decisions, ensuring that AM integration becomes not just a technical upgrade, but a strategic advantage.
Novel approaches to assessing AM maturity
Adding to this year’s expert predictions, Paul Gradl, Principal Engineer at NASA Marshall Space Flight Center, stated that developing an AM maturity model could help standardize processes and refine material properties, contributing to the future of 3D printing.
At Formnext 2023, industry players including Siemens, DyeMansion, Forward AM, EOS, and HP introduced the Additive Manufacturing Industrialization Navigator (AM I Navigator) initiative. Designed to address industrial 3D printing challenges, the initiative outlines a structured maturity model to help companies integrate AM into traditional workflows.
Through this tool, companies can evaluate their AM maturity through a Maturity Check, based on Siemens’ Digital Manufacturing Excellence framework, to identify gaps and receive recommendations for improvement. The goal is to support a transition from manual to fully autonomous AM production while optimizing process coordination.
While structured maturity models provide a roadmap for AM adoption, some companies take a more direct approach by focusing on quality assurance. Parts sourcing AM platform MakerVerse and ZEISS showcased a range of dimensional, surface, and material property assessment solutions, enabling companies to systematically evaluate the reliability of their AM parts.
Through Optical 3D Scanning, Tactile CMM, Surface Roughness measurements, and Industrial CT/X-ray inspections, this platform offers a data-driven approach to AM maturity, ensuring that printed parts meet certification standards and are suitable for full-scale production.
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Featured image shows the Fuzzy AMMM development method. Image via NOVA University of Lisbon.
