Artificial intelligence (AI) has surged forward in recent years, transforming industries across the board, and manufacturing is no exception. In particular, the fusion of AI and additive manufacturing (AM) is unlocking robust new design and manufacturing possibilities. By combining AI’s ability to process vast datasets and uncover hidden insights with the flexibility of AM, manufacturers are shifting from rigid, linear workflows to agile, intelligent production systems. The result is a greater efficiency, pinpoint precision, and real-time decision-making that adapts to changing conditions on the fly.

Industry platforms like Formnext 1, the international trade fair and convention for Additive Manufacturing and modern production held annually in Frankfurt, Germany, showcase the latest developments of AI in additive manufacturing. Formnext brings together leading companies, startups, researchers, and professionals across the entire AM value chain, creating a space for collaboration, knowledge exchange, and innovation.

This article explores how AI is driving optimization in 3D printing workflows. It also discusses how Formnext 2025 provides a platform for leaders in AI and AM to converge and share their latest innovations.

Optimizing Print Paths and Materials with AI

Improving Efficiency and Sustainability

AI enhances the efficiency and sustainability of 3D printing by optimizing toolpaths and printing strategies. Traditional 3D printing often involves trial-and-error in path planning, which can lead to excessive material use, longer print times, and higher energy consumption. AI algorithms analyze the part's geometry and dynamically generate optimized toolpaths that minimize unnecessary movements of the print head. This reduces print time and energy usage and ensures precise material deposition.

Moreover, AI-driven optimization helps reduce material waste by intelligently controlling the amount and placement of printing material. Instead of uniformly layering material, AI can adapt the printing strategy to use less material in non-critical areas without compromising structural integrity. For example, by adjusting layer heights based on localized geometry, AI enables denser infill where needed and sparser where it’s structurally permissible. This approach is very useful for lightweight applications in industries like aerospace and automotive. 

Predictive Maintenance and Defect Detection

AI-powered predictive maintenance utilizes machine learning models trained on sensor data from 3D printers to monitor equipment health continuously. AI can predict when components are likely to fail or degrade by analyzing patterns such as temperature fluctuations, vibration, and print quality metrics. Maintenance can be scheduled proactively to prevent unexpected breakdowns that cause costly downtime and material waste.

Events like Formnext play an important role in accelerating these innovations by bringing together the global AM and AI communities. Formnext 2025, in particular, is set to spotlight the latest breakthroughs in AI-driven predictive maintenance and defect detection, offering a platform where industry leaders, researchers, and startups can exchange transformative ideas and technologies.

For instance, at Formnext 2024, Advanced Additive presented its new production software featuring AI-driven path planning for fused layer modeling (FLM). This system intelligently analyzes part geometries and dynamically generates optimized toolpaths, which accelerate print times and reduce material consumption and energy use. The software enables manufacturers to achieve faster, more reliable additive manufacturing processes while minimizing waste and ensuring precise material deposition by adapting filling strategies in real time. 2

At Formnext 2023, Fraunhofer ILT demonstrated sensors printed directly onto components during the Laser Powder Bed Fusion process. These sensors enable real-time data collection for predictive maintenance, allowing early detection of equipment issues and defects before they impact print quality or cause downtime. 3

Similarly, AI-based defect detection uses real-time monitoring and image analysis to identify print anomalies such as layer misalignment, warping, or surface defects as they occur. This enables immediate corrective actions, either by alerting operators or autonomously adjusting printing parameters. 

Stay up to date with the latest AI innovations in AM, and be the first to hear about Formnext 2025 speakers, exhibitors, and exclusive content by subscribing to the Formnext newsletter.  

Industrial Adoption Examples

The industrial use of AI for print path optimization and predictive maintenance has been prominently featured at Formnext events. Case studies presented at Formnext show how companies have integrated AI to enhance performance and reliability.

For instance, at Formnext 2023, an AI-powered software named AMAIZE was introduced. It autonomously analyzes build files and corrects thermo-mechanical issues before printing. In one standout case, a California rocket launch provider used AMAIZE to overcome major AM challenges and saved 30% overall cost by achieving an 80% reduction in support structures. 4, 5

AI-Powered Design Innovation: Generative and Topology Optimization

Generative Design for Complex Geometries
Traditional CAD relies on manual modeling. AI-driven generative designs apply algorithms to autonomously create a wide array of design alternatives based on user-defined goals and constraints. Generative tools use computational power to iterate through thousands of potential solutions and often provide unconventional, organic shapes optimized for specific performance metrics. These tools help engineers to explore novel structural forms. For instance, AI can suggest intricate lattice structures, internal channels, and biomimetic geometries that would be nearly impossible to conceive or efficiently model by hand.

AI considers factors such as load paths, vibration, and manufacturability to ensure that the resulting designs are lightweight, robust and functional. 

Incorporating Topology Optimization
Topology optimization is a more targeted form of generative design, which systematically removes non-essential material while maintaining structural reliability. The algorithm identifies and eliminates regions that contribute little to structural integrity. Topology optimization ensures that load-bearing paths and connection points are intact to maintain mechanical performance.

This technique is very useful for industries where weight reduction directly impacts efficiency and cost. For example, in aerospace, reduced weight results in fuel savings; in automotive, it improves handling; and in robotics, it enhances agility.

Customization and Personalization at Scale
AI can handle vast datasets and learn from user input, which makes it a tool for mass customization. AI can rapidly tailor designs to specific use cases through machine learning algorithms trained on user data or functional input parameters. For instance, it can help create patient-specific medical implants, personalized footwear, or ergonomic tools shaped for individual users.

AM, combined with AI-driven design, makes it economically viable to produce one-off or highly customized parts without costly tooling changes.

The Future of AI in Additive Manufacturing

Human-AI Collaboration in Design and Manufacturing
A common misconception is that AI will replace human creativity in design and engineering. In reality, the future lies in collaborative intelligence, where AI augments human expertise rather than replacing it.

AI-powered design tools can generate many novel design alternatives based on user-defined goals and constraints. This frees human designers to focus on high-level conceptual thinking and aesthetic judgment. AI can rapidly analyze vast datasets from previous projects, materials libraries, and performance simulations, providing actionable solutions to guide human decision-making. 

Data-driven Decision-making and AI Ecosystems
Data-centric manufacturing ecosystems are a defining feature of next-generation AM. Modern AM machines are equipped with a suite of sensors capturing temperature, vibration, layer quality, and many other variables. AI algorithms process this data in real-time, detecting anomalies and suggesting immediate corrective actions. This continuous feedback from the manufacturing process allows for adaptive control.

Digital twins that are virtual replicas of physical assets (machines, parts, or entire production lines) are becoming an important part of this AI-integrated AM ecosystem. AI-driven digital twins facilitate closed-loop optimization, where data from the virtual environment directly inform real-world operations. 

Outlook and emerging trends
Several key trends and challenges can shape the trajectory of AI in AM. As AI tool usage grows, ensuring compatibility between different software, hardware, and data standards will be crucial for building flexible, scalable production environments. Ethical concerns around IP ownership, algorithmic transparency, and AI decision accountability will need to be addressed as well. Moreover, engineers and designers will need to develop new competencies, such as data literacy, AI model interpretation, and cross-disciplinary collaboration to fully utilize AI-driven workflows.

Conclusion

As AI continues to mature, its mark on AM is growing stronger and more strategic. AI is becoming a necessary tool across the entire 3D printing value chain. Whether in accelerating design cycles and fine-tuning material usage or enhancing final part inspection and quality assurance, AI-driven optimization positively impacts 3D printing workflows at every stage.

Looking ahead, staying competitive means staying informed. Formnext 2025 promises to be a landmark event for anyone seeking to understand the full potential of AI in AM. The event will bring together industry leaders, technology pioneers, and researchers with a dedicated focus on digital transformation, cutting-edge software, and intelligence in AM.

Register for the FormNext newsletter now to receive updates and ensure you don't miss a moment of this industry-defining event.

References

1. Formnext – the industry hub for Additive Manufacturing [Online] Formnext. Available at: https://formnext.mesago.com/frankfurt/en.html (Accessed on May 8, 2025)

2. Software innovations facilitate automation, more efficient AM production, [Online] Formnext. Available at: https://formnext.mesago.com/frankfurt/en/industry-insights/fonmag/fonmag_articles/articles/software-innovations-facilitate-automation-more-efficient-am-production.html (Accessed on June 20, 2025)

3. Integrated Sensor Technology: The Next Step in Additive Manufacturing (2023) Fraunhofer Institute for Laser Technology ILT. [Online] Available at:  https://www.ilt.fraunhofer.de/en/press/press-releases/2023/9-20-integrated-sensor-technology-formnext.html (Accessed on May 8, 2025)

4. Additive Manufacturing and Artificial Intelligence. The next hype cycle? [Online] Formnext Magazine. Available at: https://formnext.mesago.com/frankfurt/en/industry-insights/fonmag/fonmag_articles/articles/the-next-hype-cycle.html (Accessed on May 8, 2025)

5. Why Combine Artificial Intelligence with Additive Manufacturing? (2023) AFMG. [Online] Available at: https://amfg.ai/2023/11/24/why-combine-artificial-intelligence-with-additive-manufacturing/ (Accessed on May 8, 2025)