Where does the application of AI to 3D printing make sense?
As previous editions of the 3D Printing Industry Executive Survey identified, the emergence of accessible AI tools and increased investment is set to accelerate broader industrial trends. Optimistically, the confluence of discrete components of the manufacturing and business ecosystem unified by AI tools ushers in a new era of productivity. Taking a more jaded perspective, AI is either the most recent hype train or liable to unleash a digital Sauron.
We asked additive manufacturing experts where do they see AI applications having an impact in AM?
The range of answers spans the manufacturing ecosystem and touches on many other aspects of business productivity where AI tools may be useful. For example, ChatGPT has enabled what some see as enhanced communication, allowing our global industry to produce written content in a standardized manner or to generate ideas.
A counterpoint, one supported by those who decry the encroachment of machines into the realm of human creativity, is that the influx of generic yet verbose, emoji-laden social media posts is of little benefit. Perhaps unsurprisingly, given the contrarian cycle, a backlash against AI-created content, with its highly visible telltale traits, is already fomenting.
The written word is not the only domain under threat, one expert warns of “copycat and copyright” problems for physical parts.
We should remember that AI is not monolithic. While certain expressions of AI may be prone to hallucination, lack of social nuance, missing implicit knowledge, or an absence of contextual memory, the resolution of such flaws is not critical for particular strands to be useful.
Viewing AI as an enabling technology on the path to “industrial-scale production” with simulation tools and in situ monitoring ensuring “first-time-right builds.” One expert summarises the benefit as more time 3D printing, less time “tinkering” – with manufacturers’ eyes on throughput, indeed this is a goal worthy of pursuit. For “fleet-level” operations, AI may become vital in aiding predictive maintenance and automated production.
Material characterization and material development – “Next-Generation Chemistry” – are expected to benefit from AI. The latter may be an idea resisted by some, and whether a computational alloy progresses beyond lab scale production is a non-trivial task to unravel.
Design tools whether in the form of text-to-CAD generation, appraisal of existing design repositories to determine fit of AM, build preparation, or optimized complex geometries are all subjects raised by the experts.
Once again, this is a long read. We’ve included the responses from those who generously gave their time to provide insights; compiling this series is always a pleasure. Perhaps you may wish to bookmark this page and revisit it at leisure.
We hope the answers here provide a starting point for a bigger conversation. If you’d like to join that conversation, get in touch.
Overall, the tone is positive, and to generalize the overall perspective, “AI” will increase the adoption of AM.
All aboard! To the “industrial metaverse” and beyond!
More from the 2025 3D Printing Industry Executive Survey:
3D Printing Industry Economic Outlook for 2025
Sona Dadhania, Principal Technology Analyst, IDTechEx
3D printing exploded in the early 2010s thanks to its community of tinkerers; however, new users, especially in the industrial segment, want to spend less time tinkering and more time printing parts that are ready-to-go. Where AI could have the most impact is in pre-production and real-time monitoring applications. If AI could be used to identify defects before or during printing and then automatically fix the 3D model or adjust printing parameters without human intervention, then a lot of the labor involved in 3D printing (figuring out how to print well) can be eliminated.
Sascha Rudolph, Chief Operating Officer, Equispheres
AI applications in Additive Manufacturing will be another enabling technology as the industry accelerates the shift to industrial-scale production, most significantly in AM design and process optimization, workflow automation and supply chain management. Advancements in generative AI tools will fuel the design of highly tailored geometries for AM while training models will help to fine-tune run parameters during the printing process, such as laser power, scanning speed and layer thickness, significantly improving build rates and consistency at higher volumes. We also see AI being used in the development of new and specialized alloys to further enhance the mechanical properties of finished parts. Increasing automation in predictive maintenance and supply chain management will help to ensure maximum capacity for production scale.
Dr. Max Siebert, CEO and Co-Founder, Replique GmbH
Applications of AI will significantly affect a number of fields. In design, AI is already enhancing how parts are optimized for 3D printing, using design for additive manufacturing (DFAM) tools, and this will only grow.
AI will also transform AM process management. It will make quoting more effective and enhance the entire process of choosing materials and technologies. By finding the right print parameters for every structure of the part and the simulation of the print process it will further increase the amount of first-time-right builds. Additionally, it will identify mistakes made during the printing process, guaranteeing improved quality control and fewer defects. This will finally reduce costs of printing. Furthermore, AI will be essential in forecasting demand, assisting businesses in streamlining production plans and inventory control, and reducing waste throughout the process.
Nanne Veldman, Vice President, EMEA, UltiMaker
Automatic detection of print failures and identification of their root causes is an exciting advancement in FDM 3D printing. Essentially, this concept mirrors UltiMaker’s visual troubleshooting guide, where the system analyzes defects and provides clear insights into the likely cause of the issue. By integrating this know-how with AI, you create an automated troubleshooting tool that can identify and resolve problems in real-time. In fact, similar technology has already been implemented, with systems being capable of detecting “spaghetti” failures and halting the print—this innovation has been around for years.
Martin Jewell, CTO, Rapid Fusion
AI applications are poised to revolutionize Additive Manufacturing (AM) by enabling smarter, more efficient, and highly adaptive processes across several key areas:
In-Process Monitoring and Optimization
AI-driven in-process monitoring systems analyze real-time sensor data during printing, enabling closed-loop feedback for dynamic adjustments. This ensures optimal parameters such as extrusion speed, layer height, and thermal settings are continuously maintained. By detecting anomalies and making corrections in real time, AI significantly reduces defects and material waste, while enhancing overall part quality.
Advanced G-Code Manipulation
AI enables real-time generation and optimization of G-code, tailoring toolpaths for specific material properties, geometries, and process conditions. This ensures precision in material deposition, better thermal control, and improved strength and surface finish, especially for complex designs and high-performance materials.
Predictive Maintenance and Smart Machine Management
AI plays a pivotal role in enabling predictive maintenance by analyzing real-time data from machines to identify wear patterns and potential failures before they occur. This proactive approach minimizes unplanned downtime and extends machine lifespan. Additionally, AI systems can autonomously schedule maintenance, track performance metrics, and optimize machine operations for maximum productivity.
Quality Assurance and Process Stability
AI-driven analysis of in-process and post-process inspection data, including thermal imaging and dimensional measurements, ensures every part meets stringent quality standards. Automated defect detection and trend analysis reduce reliance on manual inspections and provide insights into long-term process stability.
Smart Factory Integration
AI enables the seamless integration of AM systems into smart factory environments. By connecting machines, sensors, and production workflows, AI enhances overall manufacturing efficiency. This includes optimizing resource allocation, managing production schedules, and creating adaptive workflows to respond to real-time demand changes. AI also facilitates better collaboration between AM and traditional manufacturing methods, fostering hybrid production approaches.
Thermal Management in High-Temperature Applications
For applications involving advanced materials like PEEK, AI dynamically controls thermal inputs, ensuring consistent material properties and minimizing defects. By monitoring and adjusting heat flow, AI improves process reliability and supports the adoption of high-temperature printing in aerospace and other demanding industries.
By integrating these AI capabilities, AM is moving toward a future of smarter, more reliable production systems, reducing costs, enhancing part quality, and driving innovation across industries such as aerospace, automotive, and beyond.
Mahdi Jamshid, PhD, Director – Market Intelligence, Wohlers Associates, powered by ASTM International
AI is poised to significantly impact numerous aspects of Additive Manufacturing (AM). Key areas of influence include:
– Machine health and in-situ process monitoring for real-time feedback and predictive maintenance;
– Advanced material characterization and inspection, encompassing imaging, analysis, interpretation, and seamless data sharing;
– Data analysis and management for process optimization and quality control;
– Material development focused on achieving novel properties, increased processability, and lower production costs;
– Design optimization through AI-powered tools;
– Supply chain management optimization; and
– Streamlined process qualification and part certification.
Finally, the development of robust AI models will be significantly accelerated by increased collaboration among organizations. The formation of new coalitions or consortia focused on the generation and sharing of high-quality data for model training will be vital for driving further innovation and accelerating industry growth.
Matteo Vezzali, Head of Partnerships, MyMiniFactory
If we consider AI a statistical model designed to give a result closer to the expectation of the human operator, it can have multiple applications within the realm of AM workflows. From improving features of parametric models to fit into larger assemblies to optimizing supports through machine learning, AI could be a game changer for many workflows.
If we consider AI as a creative tool or something to replace creatives or creativity per se, I don’t see much use for it as the current tools for 2D images produce interesting results when they fail their mission rather than when they succeed.
If we view AI as a statistical model designed to produce results that align closely with the expectations of a human operator, it can offer numerous applications within AM workflows. These applications range from enhancing the features of parametric models to ensure seamless integration into larger assemblies to optimizing support structures through machine learning. AI has the potential to significantly improve efficiency and effectiveness across various AM workflows.
Justin Michaud, CEO, REM Surface Engineering
AI would seem to have many potential benefits relative to part design and build optimization as well as for process monitoring.
Irma Gilbert, CEO, Autentica – Car parts
AI will play a transformative role in additive manufacturing (AM) by enabling the creation of a decentralized AI digital manufacturing infrastructure combined with Web3-powered e-commerce marketplaces for 3D printing allocation and decision-making.
This integration empowers a decentralized, intelligent supply chain where AI algorithms optimize the production, allocation, and distribution of 3D-printed parts in real-time. By analyzing demand patterns, material availability, and manufacturing capacity, AI can dynamically allocate production tasks to qualified manufacturers while ensuring cost efficiency and reduced lead times.
Moreover, integrating Web3 technologies with AI ensures secure, transparent transactions and protects intellectual property during part design and distribution. Together, these innovations create a more resilient, scalable, and sustainable framework for additive manufacturing—one that brings greater trust, efficiency, and flexibility to the industry.
Stefan Ritt, Owner and founder, AM/3D printing concepts & market integration
AI will very fast become a double-edged sword, so to speak, for AM. On one hand it will be very helpful to erase common errors in building processes and operations through analysis of big data volume (if made available!) and help to design new polymer mixtures and metal alloys or composites. On the other hand, it will require less experienced engineers or designers to create working products. Copycat and copyright problems for parts and processes will become a more prominent problem. AI will then enable or at least simplify the design and production of restricted items in various fields. This has to be seen as a threat to communities and needs to be addressed smartly.
Vincenzo Belletti, Director of EU Public Affairs, CECIMO – European Association of Manufacturing Technologies
AI applications can bring added value to additive manufacturing (AM) by significantly improving quality, speed, and efficiency across the production lifecycle. AI-driven solutions can accelerate AM’s integration into broader manufacturing workflows, enabling seamless and efficient operations. Among its applications, advanced AI inspection systems can provide real-time quality monitoring, ensuring that AM components consistently meet rigorous industry standards.
This level of control will have a major impact on reducing defective output, increasing first-time-right production rates, and enhancing the overall reliability of AM processes. As AM technology matures, the integration of AI will enhance the reliability and consistency of AM processes, making it a more dependable technology and accelerating its adoption across diverse industries.
Franco Cevolini, CEO and CTO, CRP Technology
Artificial intelligence is a transformative force in additive manufacturing. One of its most impactful applications is in design optimization, where AI-driven generative design is creating highly efficient and lightweight structures. These designs are particularly beneficial in industries like aerospace and transportation, where performance and weight reduction are critical.
AI is also streamlining production processes by enabling real-time monitoring and optimization of machine parameters. Predictive maintenance, powered by AI, is reducing downtime and ensuring consistent quality in manufacturing operations. Furthermore, AI is accelerating material innovation by analyzing vast datasets to identify new formulations, enabling faster development cycles and more precise material properties.
The ability of AI to drive collaboration across industries is equally significant. Partnerships between 3D printing providers and sectors such as space exploration, automotive, or renewable energy are fostering groundbreaking developments. By leveraging AI, these collaborations are pushing the boundaries of what’s achievable and unlocking new opportunities for innovation.
Finally, AI is shaping the future workforce by enhancing the tools and technologies available to engineers and designers. By providing insights into design, materials, and processes, AI is empowering professionals to deliver personalized solutions at industrial scales, ultimately ensuring that additive manufacturing remains at the cutting edge of technological progress.
Alex Hussain, CEO, 3DChimera
AI has tremendous potential to revolutionize additive manufacturing, and its impact will be felt across multiple levels, from machine operations to design optimization.
At the machine level, AI will play a critical role in detecting failures and making real-time adjustments to production parameters. Initially, this will be driven by simple sensor data, such as environmental temperature or humidity, but it will quickly evolve to include camera-based monitoring and, eventually, 3D scanning capabilities. These advancements will enable printers to self-correct, improving reliability and reducing waste.
In the print setup process, AI advisors will become integral to slicer software, guiding users through complex decisions such as print parameters, part orientation, and layout. These tools will optimize for print speed, quality, strength, and part finish, making additive manufacturing more efficient and accessible to a broader audience.
Looking further ahead, AI will enable design optimization for additive manufacturing (DfAM). Imagine taking a part designed for CNC machining and running it through an AI algorithm that reimagines it for FFF or SLS technologies. The result would be different but functionally equivalent parts, each tailored to the strengths and constraints of their respective processes. This capability will unlock new opportunities for innovation and help industries maximize the potential of additive manufacturing.
AI will fundamentally change how we approach additive manufacturing, driving efficiency, reliability, and creativity across the board.
Alexandre Donnadieu, Chief Commercial Officer, Chief Commercial Officer, 3YOURMIND
AI is emerging as a game-changer for additive manufacturing, addressing some of the most persistent challenges while unlocking new opportunities. Its applications span the entire lifecycle, from design to production and materials development.
Augmented Designers
One of the most immediate and impactful applications of AI is in augmenting the design process. Generative AI will play a critical role in enabling faster and more efficient creation of 3D designs, as well as optimizing those designs for additive manufacturing. Design complexity remains a bottleneck for widespread AM adoption, but AI-powered tools can lower this barrier by making design more accessible and accelerating the path from concept to production.
Zero-Defect Processes
AI will revolutionize production processes by providing real-time control and monitoring, as well as predictive capabilities to anticipate and mitigate defects. This will not only improve part quality but also streamline the qualification process, which is often lengthy and data-intensive. By leveraging data-driven pre-qualification, manufacturers can reduce lead times and costs, enabling faster and more reliable production.
Next-Generation Chemistry
AI will also have a profound impact on research and development, particularly in the realm of new material innovation. Through advanced simulations of material properties, AI can accelerate the customization of materials for specific applications in industries such as medical, aerospace, and electronics. This capability will open the door to entirely new use cases, allowing additive manufacturing to solve challenges that were previously out of reach.
As AI continues to evolve, it will drive greater efficiency, precision, and innovation in additive manufacturing. The integration of these technologies will redefine what’s possible and open new frontiers for industries leveraging 3D printing.
Rob Higby, Chief Executive Officer, Continuum Powders
AI will play a transformative role in additive manufacturing. It is already having an impact in areas such as design optimization, predictive maintenance, and quality assurance. In the future, AI will enable smarter material selection, real-time process monitoring, and enhanced efficiency, allowing manufacturers to minimize waste and maximize performance. This shift will drive innovation and sustainability across advanced manufacturing.
Dr. Wilderich Heising, Partner & Director, Boston Consulting Group (BCG)
I see two main areas, where AI will make a difference in the AM industry. First, generative AI capabilities will boost the design processes by leveraging generative design and topology optimization. We will be able to design faster, more efficient, and with better outcomes. Second, we will see more and more equipment providers using machine learning and in-line quality control and detection powered by AI to increase reliability and reproducibility of print jobs.
Henrik Lund-Nielsen, Founder & General Manager, COBOD
3D Printing enables cost-efficient construction of AI-generated design, which would be cost-prohibitive with conventional construction methods.
Dr. Jeffrey Graves, President & CEO, 3D Systems, President & CEO, 3D Systems
I believe AI will directly impact the efficiency of 3D printing through incorporation of sensing and automated in-process data collection which will feed rapid, large-scale data analysis and real-time optimization of the process. At a fleet level, AI can make large impacts in machine up-time and automated operation, as well as optimization of the full-work flow. I anticipate this will lead to step-function changes in part quality and throughput, decreasing component costs and reducing risk of adoption in high-reliability applications.
Dayton Horvath, Director, Emerging Technology and Investments, AMT-The Association For Manufacturing Technology
AI applications in AM fall into two representative categories today: the first is interface augmentation and the second is a complement or alternative to modeling and simulation tools. The AM technology stack requires human interaction at every major step; AI can serve as an accelerant by changing the medium of interaction, length of the interaction, or impact of the interaction. When using AI as a tool to improve the technology directly, opportunities exist where physics-based modeling or simulation falls short in efficacy, efficiency or cost. In the case of efficacy, certain data problems do not have easily correlated physical models and gives AI tools a chance to shine.
Rob Lent, Chief Operating Officer, Vision Miner
AI has shown it can handle complex tasks in no time, and we’re starting to see that impact manufacturing too. Manufacturing is still both an art and a science—where the craftsman is the artist, and experience makes all the difference. Right now, that experience is critical. But in the coming years, I expect it to matter less for making parts. You might not need the expert who knows every detail about a tricky thermoplastic—AI will step in to fill that gap.
Giles Gaskell, Additive Industry Specialist, Pinnacle X-Ray Solutions
AI has the potential to combine data from in-line process monitoring and continuous real-time independent inspection methods to close the quality feedback loop, bringing us closer to making perfect parts, every time.
AI is instrumental for the value creation of 3D printing. It starts with automation, optimization of file, uptime of machine, and lowering cost. A trend we see in traditional manufacturing for years now.
Ric Fulop, Founder and CEO, Desktop Metal
AI is already heavily used in shape compensation in sinter based processes and thermal stress simulation in melt based processes. I expect we will see it heavily used in CAD and other content creation tools. This should increase demand for AM products.
Harshil Goel, Founder and CEO, Dyndrite
I believe the applications of “AI” are over blown at the moment. There will be applications of image recognition, linear algebra and statistics to make better parts faster through thermal management. The folks using “AI” right now are in my opinion engaged in marketing.
Dyndrite has an AI strategy to be made public at a later date. In the meantime, quite a bit needs to happen before AI can do anything interesting.
Paul Bullock, Director / Owner, 3D 360
Improvements to quality and in-process monitoring. AI will also lead to more flexibility and what is able to be 3D printed.
Fabio Sant’Ana, Director, Farcco Tecnologia
Design, Faster Simulations, Better Processes
Jeremy Haight, Chief Principal Engineer, Vestas Wind Systems A/S
In the near-term, I see AI having the biggest application in in situ process monitoring and error mitigation. In the long term, I see AI having a wide range of applications such as: LLM (large language model/conversational AI)-to-solid model, automated process and MES optimization, automated applications selection (PLM-to-DfAM), embedded CAD/CAMAM recommendations with automatic topology and process optimization. This is not even to speak directly to the many industry specific applications such as those in healthcare, aerospace, energy, etc…
Jonathan Beck, Founder/Manager, Scan the World / MyMiniFactory
AI will develop further into generative design, from design to production and post-processing. Currently a lot of wasted materials and time, risking becoming less cost effective than other manufacturing industries. AI’s ability to optimise design, monitor production processes and automate post-processing will hopefully make AM more agile, cost effective and scalable, allowing for innovation and users of all abilities to contribute
Thomas Batigne, Co-founder & CEO, Lynxter
AI will serve as a valuable assistant for designers and manufacturers, reducing the labor intensity of tedious tasks, streamlining operations, and enhancing creativity. It can be particularly useful in additive manufacturing (AM) for nesting and printing profile optimization, as well as for analyzing print data and reports.
Aurélien FUSSEL, Innovation Program Manager, ALSTOM
While the immediate impact of AI applications in additive manufacturing (AM) may seem limited, prioritizing intelligence can unlock new possibilities. Geeblee’s innovative French solution stands out by aggregating constraints to design unique part shapes, thereby pushing performance boundaries to achieve unprecedented outcomes in AM.
Dr. Vincent Morrison, CEO, NEW AIM3D GmbH
We will see AI technologies in data preparation and preprocessing in the next few years, which will then make the use of the machines faster and easier for the end users.
There will also be a great opportunity in the fact that AI technologies will accelerate the development of AM equipment, as it will massively speed up the programming of equipment and processes, creating new space for machine and process innovation. This is already happening today.
The last step, which in our view is still a long way off, is the extensive integration of AI into the machine control of extrusion systems. This is where high demands in terms of data volume and processing speed currently come up against insufficient computing power in the field of industrial control systems.
Sascha Schwarz, CTO, TUM Venture Labs
Physics-informed AI algorithms, not just rule-based software, will finally enable the AM user to master the complexity of the multi-modal parameter space on the process level and also unlock the relevant generative designs capable of taking over the intended function in the real physical environment, such as thermal management in complex technical systems.
Adam Penna, Founder, All Digital Additive Manufacturing
AI applications are set to revolutionize Additive Manufacturing (AM) in numerous ways. Firstly, AI can optimize the design process by incorporating AM-specific parameters, enabling generative design, and optimizing topology for efficient production. In material development, AI helps create customized materials that enhance performance and broaden the range of AM applications. Process optimization is another area where AI shines, providing real-time control and adjustments to ensure optimal printing conditions. Quality assurance benefits from AI through advanced vision systems that detect defects in real-time, resulting in higher accuracy and reduced waste. Predictive maintenance, powered by AI, identifies early signs of equipment issues, thereby reducing downtime and extending the lifespan of AM machinery. Additionally, AI streamlines supply chain management, optimizing logistics, and reducing inventory costs.
These AI applications significantly enhance the efficiency, quality, and innovation in Additive Manufacturing.
Dr. Özlem Weiss, General Manager, Expertants GmbH
Additive manufacturing is most profitable when a single print job can repeatedly produce many customized parts. AI will play a key role in achieving this in a stable and most efficient way! New models will be created based on the data set of today and will help streamlining and standardizing design of parts. Data mining and monitoring process parameters will enable robust manufacturing and post-processing.
I dare to go as far as to say that it will be AI that will close many gaps and finally enable additive manufacturing to take its place alongside all other manufacturing methods.
Slobodan Ilic, Sales & Marketing Director, BLT Europe, Bright Laser Technologies
I believe AI can make a significant impact on additive manufacturing across three major areas.
The first is design. Here, AI can be used to support innovation and enable full customization by optimizing designs specifically for AM. This opens the door to creating parts that were previously impossible to manufacture.
Next is quality control, where AI offers powerful tools to monitor, interpret and adjust process parameters in real-time. By integrating data from various sensors and quality assurance systems, AI can identify and correct issues as they arise. A major hurdle for this is the lack of structured and classified data, which is essential for AI to reach its full potential in this area.
Lastly, AI can work in parallel with AM processes to streamline manufacturing operations, procurement and strategic management. This can lead to more efficient production workflows, better resource allocation and improved decision-making at every level of the manufacturing process.
Ian Falconer, Founder & CEO, Fishy Filaments
Automated NDT, which is directly analogous to medical imaging, so could be co-developed. Design optimisation is already there in FEA, but it could be radically democratised by AI.
Rudolf Franz, CEO, voxeljet
AI will transform AM through process optimization, generative design, and predictive maintenance. AI-driven analytics will improve part quality, reduce defects, and boost efficiency. Generative design tools will unlock optimized geometries for 3D printing, enhancing performance. Predictive maintenance will reduce downtime and improve reliability.
Maxence Bourjol & Kareen Malsallez, Head of Sales & Marketing Manager, 3DCeram Sinto
AI is already making significant inroads in ceramic 3D printing. In our case, we’ve been developing AI solutions for three years now. We’ve structured our AI assistance in two key phases: pre-process with CERIA Set, which provides design guidance and custom parameter generation, and on-process with CERIA Live for real-time control and optimization. This dual strategy directly addresses what matters most to manufacturers – achieving cost-effective production and optimal productivity.
We’re continuously expanding our AI databases to optimize the printing process, ensuring our partners can maintain competitive production costs in their markets. This isn’t just about having AI capabilities – it’s about developing tools that guarantee profitability for our industrial partners.
The impact of AI on productivity is transformative, and process providers who haven’t started integrating AI solutions risk falling behind. As we are now in 2025, we’re focused not just on producing quality parts, but on ensuring our partners achieve meaningful returns on their AM investments through AI-driven optimization. This is how we’re shaping the future of ceramic 3D printing: by making industrial-scale AM both possible and profitable.
Gil Lavi, Founder & CEO, 3D Alliances
Integrating AI into additive manufacturing offers numerous benefits, including enhanced efficiency, innovation, and scalability. One of the primary areas of focus is leveraging AI to optimize the design of components for various AM technologies. Over time, AI has the potential to achieve maximum optimization of designs before printing, ensuring superior performance and resource efficiency. This capability will be particularly critical as AM becomes more integrated into manufacturing processes.
Ma Jingsong, GM, Uniontech
3D printing is like a glimmer of light, bursting into a new life for the industry. Under the demonstration effect of metal printing application breakthroughs and market coercion, relying on the rise of AI, as well as the penetration of the digital wave in the consumer end, more and more manufacturing enterprises will actively embrace 3D printing technology to help the renewal of the consumer market and transaction efficiency. UnionTech will continue to take 3D printing as the main technical carrier, through continuous technological innovation and application innovation, to build a high delivery capability of digital manufacturing in small-batch scenarios, and achieve the replicability, transferability, and connectivity of this capability.
Sherri Monroe, Executive Director, AMGTA – Additive Manufacturer Green Trade Assn.
Some specific areas within the use of AM where I expect to see AI impacts are:
• Highly optimized designs, material and energy usage
• Matching of opportunities to transition waste and by-products into assets and resources – connecting who has it to who wants it
• Streamlined and more efficient processes for production and distribution
• More intelligent business models that leverage AM-enabled production distributed across geographies, time, and designs for economic and environmental outcomes
Andre Wegner, CEO, Authentise
In the near term, AI will help in the following ways:
1) Documentation and Certification: We’re going to make the tedium of AM, like getting certification approved. ThreadsDoc is already doing this for Boeing.
2) Identifying AM Applications: We’re still struggling to find parts or assemblies where additive manufacturing can be useful. AI can change that by quickly and effectively reviewing massive amounts of data—design intent, material properties, and performance needs—and it’s a powerful tool for helping us decide where AM adds the most value.
3) Empowering Operators: AI can be like a co-pilot for AM operators. It provides real-time guidance, suggesting machine settings, adjusting process parameters, and even flagging potential defects before they happen. This is not replacing operators—standards and common sense require them to always be in the loop—but whether visual or text-based AI, it will play a role.
Brad Rothenberg, CEO, nTop
Accelerating simulation and calculation of manufacturing / build parameters.
Joseph Crabtree, Founder and CEO, Additive Manufacturing Technologies (AMT)
AI applications are set to revolutionize additive manufacturing by streamlining processes and enhancing efficiency. Collaborations like AMT’s work with NVIDIA and HP highlight the transformative potential of AI in areas such as automatic file generation, optimized printing strategies, and seamless post-processing of 3D parts. These advancements not only reduce human intervention but also improve part quality and production speed. By leveraging AI-powered tools, additive manufacturing is moving closer to achieving fully integrated and automated workflows, which will be critical for scaling production and meeting the growing demand for precision and customization in industries such as aerospace, healthcare, and automotive.
Marleen Vogelaar, CEO, Shapeways
AI is a catch all term for a number of different processes and technologies, some of which are already well known to AM (and the wider manufacturing industry) and some of which are emerging as potentially useful. Machine learning, computer vision, large language models, neural networks… these and more are all part of the ‘AI’ conversation.
All in all, AI is poised to have profound impacts across the entire AM process chain. From ideation, design, model preparation, print setup, monitoring, correcting, etc etc. Of these (without any timeline!) we can expect to see AI:
Accelerating ideation and design
Generative design tools powered by AI will start to create complex geometries optimized for 3D printing, significantly reducing design timelines. Integrated into major CAD platforms or through standalone tools, these systems enable designers to explore multiple solutions rapidly. By leveraging deep learning models and large datasets, AI will identify the most efficient designs, offering engineers the freedom to innovate without being constrained by traditional design limitations.
Moreover, AI-powered predictive analytics tools analyze designs for potential print issues before production begins. Physics-informed AI can already simulate builds, predict failures, and recommend adjustments, reducing costly trial-and-error cycles.
Optimizing material and build parameters
AI can play a critical role in material selection and process definition. Machine learning models analyzing vast material datasets and past build outcomes will more widely be used to predict how specific materials will behave under different conditions. This will allow manufacturers to make data-driven decisions, ensuring material compatibility with the desired application while reducing waste.
Additionally, AI helps define optimal build strategies, including part orientation, support structure design, and printing parameters. To an extent this already happens (and we don’t call it AI, maybe just ‘an algorithm’). Predictive scheduling tools further optimize workflows by determining the best sequence for print jobs based on material availability, machine readiness, and deadlines.
QA and process monitoring
Real-time monitoring technologies, use computer vision and deep learning to inspect every layer of a print for defects. These systems flag issues like warping, layer shifts, or irregularities as they occur, enabling corrective action during the build process. This prevents material waste and ensures higher consistency in final parts.
Post-build, AI-driven inspection tools automate quality checks using computer vision to identify defects faster and more reliably than manual inspections. These systems reduce human error, ensure compliance with specifications, and streamline workflows.
Post-processing and workflow automation
Robotic systems equipped with AI will increasingly automate tasks like support removal and surface finishing, ensuring consistent results. By standardizing these labor-intensive processes, manufacturers will achieve higher throughput while reducing variability and labor costs.
Predictive maintenance
By using IoT-enabled sensors and machine learning algorithms, AI will monitor machine health, predicting when maintenance is needed before a breakdown occurs. This proactive approach will reduce unplanned downtime, lower repair costs, and extend the lifespan of expensive AM equipment.
Search & Discovery
An additional application is search technology for end users; Thangs 3D for example, leverages cutting-edge AI to revolutionize how users find and work with models. With advanced text, 2D image, and patented 3D search capabilities. This makes it effortless to locate exact or similar models, even identifying parts within parts. AI 3D search capabilities make it possible to analyze 3D mesh and geometry in real time to deliver search results of geometric matches or visually similar models. This innovation streamlines discovery and opens up new possibilities for design efficiency.
Shon Anderson, CEO, B9Creations
Artificial intelligence is poised to play a transformative role in additive manufacturing, fundamentally reshaping how we design, produce, and scale parts. From machine learning to expert systems, AI will not only enhance technical capabilities but also make additive manufacturing more accessible and intuitive for a broader range of industries and workforce levels.
One of the most immediate impacts of AI lies in optimizing the layout, orientation, and printing of parts. Machine learning algorithms can analyze a vast array of geometries, materials, and production requirements to determine the most efficient configurations automatically. This means faster print times, reduced material waste, and improved part performance—all of which directly benefit the bottom line.
AI also enables predictive analytics and real-time monitoring, ensuring process reliability and consistency. By analyzing data from sensors during the printing process, AI can identify and correct deviations before they result in defects. This level of automation not only enhances quality assurance but also builds trust in additive manufacturing as a reliable production method. At B9Creations, we’ve already implemented real-time 3D printing adjustments into our technology that account for machine tolerances, material chemistry, light output, and part geometry to ensure high CAD fidelity and high consistency part-to-part and printer-to-printer. As a real-world example, for one of our aerospace partners, we are holding +/- 12 micron tolerances on a foot tall part across 50 machines, with interchangeable resin vats and build tables, all managed by our QA/QC toolset.
Another critical area where AI will have a profound impact is workforce training and adoption. Expert systems can make complex parts of the additive manufacturing process “invisible” by automating tasks that currently require significant expertise. For example, AI can handle intricate design adjustments, slicing optimizations, or post-processing recommendations, allowing users to focus on broader goals rather than technical minutiae. This democratization of additive manufacturing will help companies scale their operations by reducing the learning curve for new team members and enabling wider adoption across industries. In dental, aerospace, and jewelry, B9Creations has already leveraged this capability to go directly from a scan or design file to an STL loaded on a printer, making the CAM portion invisible to the user.
AI will also play a key role in expanding the applications of additive manufacturing. Generative design, powered by AI, is already enabling engineers to create innovative geometries that were previously unthinkable. As these tools become more sophisticated, we’ll see entirely new product categories emerge, pushing the boundaries of what AM can achieve.
Finally, AI will be integral to integrating additive manufacturing into larger manufacturing ecosystems. By connecting additive manufacturing workflows with supply chain management, ERP systems, and predictive maintenance platforms, AI will enable seamless end-to-end operations. This integration will allow companies to better align production with demand, reduce lead times, and respond more flexibly to market changes.
In summary, AI is not just a tool to enhance additive manufacturing—it’s a catalyst for its evolution. By making processes smarter, more reliable, and more accessible, AI will drive the industry toward greater efficiency, scalability, and innovation, ensuring additive manufacturing’s continued growth as a cornerstone of advanced manufacturing.
Mike Seal, General Manager, Megnajet
As with some current 3D printing software systems “telling” the system which forces are involved and leaving the system to extrapolate the best output has resulted in some very organic-looking and perfectly functional prints. AI and or machine learning will enable even less expertise in final design allowing focus on concept rather than complexity. Hopefully, this will lead to some truly inspired and original solutions not limited by what has gone before.
Andy Davis: Director of Government Solutions, The Barnes Global Advisors
AI’s strongest advantage is its ability to extract insights from large, complex data pools, enabling human-in-the-loop decision-making. AI processing of datasets will lead to more robust and scalable AM processes, while human oversight ensures validated insights and trustworthy results. Applications for this combined teaming model – often called intelligence amplification – will include rapid assessments of existing bills of material for AM part selection, engineering and design optimization, faster development of build parameters and processes leading to lower cost and shorter qualification cycles, the correlation of repair and overhaul data with non-destructive testing (NDT) and in-situ monitoring data. In addition, as the data pools for in-service parts grow, AI will be key to the identification of optimal design features by analyzing field service data logs for “super-performers,” linking them back to design and material decisions.
Julien Lederman, Interim CEO, Nano Dimension
I think we can expect more precise simulations when it comes to parts, thanks to specific AI algorithms. Also, for general production workflow and continued throughput, AI is proving its worth in ensuring production monitoring solutions are delivering manufacturers critical insight from the factory floor – including AM platforms. This is changing things insofar as preventative maintenance and enabling manufacturers to better avoid downtime and its associated costs.
Additionally, just as it is in other industries, I think we can expect to see AI help manufacturers automate difficult jobs, make intelligent decisions, and drive efficiency. It could also help drive 3D printing forward by enhancing designs, checking the quality of parts, and enabling more scalability across production.
Max Funkner, Founder, 3DWithUs
In the consumer market, with more powerful processors and AI-enabled computers, companies are likely to accelerate the development and training of their own AI systems, integrating them to optimize generators for 3D printing design. Following the emergence of multiple 3D model generators in 2024, we can expect more improvements and advancements in this area in 2025.
Kevin Wang, Co-founder and VP, Elegoo
AI is set to revolutionize the 3D printing experience by making it more precise, efficient, and accessible than ever before. Our Saturn 4 Ultra is equipped with an intelligent detection system that automatically identifies and resolves common printing issues in real time. This feature enhances the reliability of the printing process and empowers users regardless of their technical skill level to achieve professional-grade results.
We are actively exploring partnerships with AI companies to further integrate AI into 3D printing. Imagine the ability to generate 3D models using AI, making the technology even more accessible and allowing a broader audience to engage with 3D printing.
Dr. Johannes Homa, CEO, Lithoz
AI is sure to play a transformative role in additive manufacturing (AM). By making design processes more accessible, AI is enabling a wider range of users to create optimized models, lowering the barrier to entry for innovation. In addition, machine learning leverages big data to detect patterns and identify errors in prototypes, which will significantly reduce the time and cost associated with trial-and-error iterations.
AI is also accelerating the path to serial production by streamlining workflows and optimizing manufacturing processes, ultimately making production faster and more efficient. As AI continues to advance, its integration into AM will unlock new possibilities for scalability and precision.
Graham Tweedale, Chief Operating Officer, Xaar
Historically, one of the primary challenges in additive manufacturing has been the complexity involved in creating printable models, which often require specialised devices and technical expertise. However, the advent of AI tools is significantly lowering these barriers. AI streamlines the design and preparation of models, making it accessible to a broader audience. This shift is likely to facilitate greater adoption of additive manufacturing technologies across various industries, enabling more users to leverage these innovative solutions.
Louise Callanan, Director of Additive Manufacturing, Renishaw
Artificial intelligence (AI) has the potential to revolutionise AM, particularly in process optimisation and defect prevention. As the technology matures, we expect to see AI become more embedded within AM workflows, leading to improvements in efficiency, quality assurance, and predictive maintenance.
Chevy Kok, Vice President, APAC, UltiMaker
AI has the most potential in the pre-3D printing stages of (1) material selection, and (2) print preparation. Being able to identify and fix problems at a much earlier stage of the workflow will bring about the greatest savings in the 3D printing workflow.
AI in material selection could be a GPT-style coach to help engineers choose the right materials for their applications. Material selection is one of the biggest challenges faced by today’s users, as the knowledge of FFF materials is still limited.
AI in print preparation stage could be a guide to help users achieve the right 3D print properties by suggesting the right slicer settings to achieve prints based on the outcome that they want, for example, profiles for engineering parts, profiles for visual models, etc.
James Franz, President, AMER, UltiMaker
The immediate use of AI in AM will be primarily supporting functions such as software development and customer service. AI can streamline issue resolution, automate code development, and improve efficiency.
Another area where AI can make an impact is optimizing the printing process by leveraging data from printers and large-scale data sets. For example, AI can help enhance the way we use printer puts and sensor data to fine-tune the output, whether by adjusting settings in the slicing engine or optimizing machine parameters. Currently, most of the review happens at the end of the print, which allows for analysis and improvements for future prints. But what if the printer can adjust settings in real-time or before a print even starts? As you prepare your model, AI can analyze factors like support structures, orientation, and settings based on specific print goals. This can be done by processing large data sets of past prints. Then during the print itself, AI can leverage sensors on the machine to dynamically adjust factors such as nozzle temperature, flow speed, and other variables to ensure optimal print and reduce errors. In both cases, AI can help to anticipate and correct potential issues, ensuring the highest quality result.
Simon Duchaine, Chief Commercial Officer, Dyze Design
AI will play a transformative role in advancing additive manufacturing (AM), addressing some of its most persistent challenges. One of the primary hurdles blocking broader adoption—particularly in Material Extrusion (MEX)—is the lack of process reliability. Unlike traditional subtractive manufacturing, which has matured into a highly predictable and repeatable process, AM often struggles with inconsistencies that hinder its use in production at scale.
AI has the potential to revolutionize this by enhancing reliability and repeatability across the AM workflow. Through real-time monitoring, advanced data analysis, and predictive modeling, AI can help identify and correct issues during the printing process, significantly reducing defects and ensuring greater consistency. This progress could also pave the way for additive manufacturing to meet stringent certification standards that currently remain out of reach due to repeatability concerns.
In the long term, AI-driven insights will enable smarter, more automated decision-making in design, material selection, and process optimization. By integrating AI, manufacturers can reduce the trial-and-error aspect of AM, streamline production, and unlock new opportunities for AM to serve as a reliable tool in large-scale manufacturing.
Glynn Fletcher, President, EOS North America
AM is digital-first process, and offers fertile ground for AI applications. Generative design, process monitoring, and predictive maintenance are example processes where AI can optimize efficiency, reduce waste, and enhance product performance. AI’s potential to collect and manipulate information to mitigate specific AM challenges shows real promise.
Gleb Gusev, Chief Technology Officer & Co-founder, Artec 3D
With AI, photogrammetry can reconstruct objects, areas, and people with unprecedented quality. Excitingly, AI photogrammetry has the potential to open 3D scanning to an entirely new user base, as it’s compatible with any smartphone or DSLR camera.
There are many scenarios where utilising this technology alongside traditional 3D scanning would help you get the best out of both. For example, you can capture an object with a 3D scanner, then reconstruct the entire surrounding scene with AI photogrammetry. In future, I believe these algorithms will get better and faster. Already, these algorithms can handle shiny, semi-transparent, and featureless surfaces – areas where traditional photogrammetry struggles. AI-powered reconstructions will only get more accurate. I anticipate that they are going to liberalize 3D scanning and open the technology to new markets.
John Kawola, CEO, Boston Micro Fabrication
As AI-driven capabilities continue to impact almost all industries, techniques and workflows, demand for smart solutions will rise. In the 3D printing industry, AI will be able to help users optimize processes, make data-informed decisions, improve design and speed-up development timelines. Additionally, next-generation technologies embedded in consumer goods, like smart glasses, will require both high precision and micro-manufacturing solutions to carry out production of the tiny technology that forms these goods.
Nick Allen, Founder & CEO, 3DPRINTUK
[AI will be used] everywhere. Manufacturing is a slow-to-adopt industry, you can see this with the actual take-up of AM as a manufacturing process in the entire ecosystem. This will likely be the same in AM for AI, but those who do adopt will be the ones who reap the greatest rewards. Efficiency is the key driver to reducing costs, reducing costs is the key driver to product viability, product viability is the key driver to grown. AI may be the driver to efficiency, meaning AI will likely be the root cause of growth for AM.
Julien BARTHES, CEO, 3Deus Dynamics
I see AI being used for design optimization, better definition of process rules and cost optimization.
Ralf Anderhofstadt, Head of Additive Manufacturing, Daimler Truck AG | Daimler Buses GmbH
AI offers great potential for additive manufacturing from different directions. However, it remains to be seen what AI will look like in the coming years, as the “valley of tears” has not yet been reached. If this is not achieved or quickly exceeded, AI will revolutionise additive manufacturing not only in terms of design. This also offers great potential for digital AM business models in particular, as we already see in the successful integration into our digital warehouse and our software-as-a-service solution.
Craig Monk, Founder, 3D Print Monkey/Liquid Models 3D
I see artificial intelligence significantly impacting additive manufacturing by improving efficiency, precision, and scalability. AI-driven design tools, such as generative design and topology optimization, are already enabling the creation of lightweight, complex structures that reduce material use while maximizing performance, and this will only get better over time. Machine learning and predictive analytics enhance process reliability by reducing downtime and ensuring consistent quality through predictive maintenance. In production, AI is and will continue to optimise 3D printing parameters in real time, improving both speed and accuracy while minimizing waste. Post-processing and quality assurance are also becoming more streamlined with AI automating workflows and inspections. These applications are steadily transforming additive manufacturing into a more efficient and intelligent manufacturing process.
Daosheng Cai, Chairman, EASYMFG
Artificial intelligence will be integrated into the entire process of AM technology, including data acquisition, process control, simulation optimization, and more. Naturally, the first area to benefit will likely be data acquisition.
Dr. Karsten Heuser, VP Additive Manufacturing & Head of Company Core Technology Advanced Manufacturing & Circularity, Siemens AG
Artificial Intelligence (AI) is set to revolutionize every aspect of additive manufacturing (AM). One of the most significant impacts will be in the design of better machines using industrial AI. This technology will streamline and accelerate the design and engineering workflow, making these processes much faster and more automated.
Industrial AI will act as a major co-pilot for designers, simplifying complex tasks such as topology optimization, fluidic optimization, and other forms of functional integration. This will make it easier to realize advanced designs and improve overall efficiency.
The industrial metaverse will also become increasingly relevant for AM, leading to immersive and adaptive 3D printing workflows. This virtual environment will allow for more intuitive and flexible manufacturing processes.
Furthermore, industrial AI will enhance the robustness of AM processes. By utilizing in situ data and sensor analytics at the industrial edge, close to the machines, AI will enable real-time monitoring and optimization, ensuring higher quality and consistency in production.
Kris Binon, Managing Director, AMIS
AI is and will be confronted with the same hurdles as AM itself: can you trust the outcome? Can you prove that you can trust the outcome? And AI, too, will be driven by the viability of its business cases. This being said: AI could have significant added value in each step of the process: from design, over (print and part) simulation, to nesting, positioning, etc… At first (again, as with AM itself), this will be in high-end applications where the extra investment (training the AI, scrutinizing/validating the outcomes,… ) outweighs the cost.
Sarah Jordan, CEO, Skuld
Many areas, especially in eliminating non-value added. Lean definition- what customers wouldn’t want to pay you for if the task was separately itemized.
Angel Llavero López de Villalta, CEO, Meltio
Artificial intelligence will allow us to eliminate one of the main barriers that was the process, control, accessibility and the use of qualified personnel. In recent years additive manufacturing of metal has been dominated by highly skilled jobs and now we will be able to minimize and we will be giving more robust and simpler processes that can be solved more effectively and on the fly.
Bart Van der Schueren, CTO, Materialise
AI will play a pivotal role in addressing complexity and generating valuable insights in additive manufacturing. By nature, 3D printing is a highly complex technology, with numerous factors influencing the process and final outcomes. AI, including generative AI, has the potential to unravel this complexity by identifying key variables and offering actionable insights to optimize the process, enhancing efficiency, quality, and repeatability.
Another significant impact of AI will be in mitigating the industry’s skills gap. The current shortage of expertise and skilled professionals is a major challenge for scaling 3D printing operations. By harnessing AI, we can enable qualified engineers—beyond just domain experts—to contribute effectively to AM workflows. For instance, at Formnext, Materialise announced the opening of Magics’ algorithms via an SDK to facilitate custom workflow development. However, even with these advanced tools, creating the most optimal scripts can still be daunting. This is where AI can step in, simplifying complex scripting tasks and making advanced capabilities more accessible to a broader range of professionals.
Xuanmiao Lyu, Marketing Influencer Manager, 3DMakerpro
AI applications in Additive Manufacturing (AM) impact design optimization, process monitoring, materials development, predictive maintenance, supply chain optimization, quality assurance, post-processing automation, and decision support. AI optimizes part designs for 3D printing, monitors processes in real-time, accelerates material discovery, predicts maintenance needs, enhances supply chain efficiency, automates quality control, streamlines post-processing tasks, and provides data-driven insights for better decision-making. By integrating AI across the AM workflow, industries can improve efficiency, quality, and innovation in additive manufacturing processes.
Matthias Schmidt-Lehr, Managing Partner, AMPOWER
AI will have an impact on material parameter development, design and simulation. Indirectly, it will certainly have an effect on every organization in areas such as marketing or software development and coding.
Dr. Stefan Schulze, Director 3D Printing Materials, Lehmann & Voss & Co. KG
AI will run and monitor print farms of hundreds or thousands of printers with a minimum of staff. AI will continuously monitor the printing process and the part quality in each individual printer and will give an alert or even trigger autonomously the removal of poorly printed parts to ensure a high quality level.
Furthermore, AI will help operators of 3D-printers to accelerate their development along the learning curve in achieving a level of professionalism and mastering the technology seen today in injection molding. AI will do so by not only providing suggestion on how to design and print parts but by providing the reasoning behind, contributing to the continuous learning of the operator.
William Alderman, Founding Partner, Alderman & Company
We believe one of the greatest uses of AI in AM will be in the development of exotic materials.
Nick Pearce, Founder & Managing Director, Alexander Daniels Global
AI will mainly impact AM in areas like part design, simulation and process optimisation. It could serve to optimise parameters more quickly, improve quality and repeatability of production with AM, and contribute to energy reduction and sustainability.
Robert Higham, CEO & Co-Founder, Additive Manufacturing Solutions Ltd.
AI as a tool to inspect and predict part performance will increase in the coming years’ but perhaps more importantly for me is that AI will be a method in which we can asses and quantify the relationship of geometry, materials and process for our data generated already. AI as a tool to make our data more powerful is one of the most exciting opportunities in building confidence and increasing AM industrialisation.
Kristin Mulherin, Director, Additive Manufacturing Technology, Hubbell
As an end-user of AM technology, my biggest concern is repeatability and reliability. AI can have a huge impact here. In 2024 we saw the emergence of some great solutions for predicting possible print failures or defects and real-time monitoring, but I think we’re just beginning to scratch the surface. AI will have an increasingly significant impact on AM in the coming years for this reason alone.
Irma Gilbert, CEO, Autentica Industrial Platforms Ltd
Artificial Intelligence (AI) is poised to revolutionize Additive Manufacturing (AM) by enhancing efficiency, security, and user experiences across the following key areas:
Design Automation for Machine Selection and Marketplaces
AI can optimize design processes by automating the selection of the most suitable AM machines and materials for specific projects. This not only improves production efficiency but also ensures higher-quality outcomes.
In digital marketplaces for 3D-printed parts, AI can match customer demands with OEM offerings, personalize recommendations, and facilitate seamless licensing transactions. For example, AI can streamline “right-to-print” licenses by analyzing customer preferences and manufacturing requirements, enabling more precise and tailored solutions.
Quality Control
AI-powered algorithms monitor and predict defects during the printing process in real-time, significantly reducing waste and ensuring consistent quality. Machine learning models analyze sensor data to detect anomalies, enabling preemptive corrections.
Post-production, AI can compare printed parts against digital twins or original models to verify adherence to specifications, thus enhancing reliability and trust in 3D-printed products.
Cybersecurity
Protecting intellectual property (IP) is critical in AM. AI bolsters cybersecurity by detecting threats, unauthorized access, and attempts to counterfeit digital assets. AI-driven solutions, such as blockchain integration and anomaly detection algorithms, ensure secure file transfers and prevent IP theft in distributed manufacturing ecosystems.
Customer Experience
AI transforms the customer journey by leveraging predictive analytics for demand forecasting and personalized recommendations.
Chatbots and virtual assistants powered by generative AI provide real-time support for inquiries, troubleshooting, and order tracking, delivering a seamless user experience. For OEMs and suppliers, AI analyzes customer feedback to refine product offerings and drive customer-centric innovation.
Gene Eidelman, Cofounder, Azure Printed Homes
Design Optimization: AI can analyze data to generate highly efficient, lightweight, and structurally sound designs that would be impossible or too complex for human engineers to create alone.
Production Process Automation: Machine learning algorithms can optimize printing parameters in real-time, improving print quality, reducing material usage, and minimizing errors.
Predictive Maintenance: AI-driven analytics can predict when 3D printers or their components need maintenance, reducing downtime and operational costs.
Material Development and Testing: AI can speed up the discovery of new materials by simulating how different formulations will behave during the printing process, significantly shortening R&D timelines.
Supply Chain Optimization: AI can enhance inventory management and production planning for on-demand manufacturing, making AM an integral part of the evolving supply chain landscape.
Professor Joshua Pearce, Thompson Chair in Innovation Ivey Business School and Department of Electrical & Computer Engineering, Western University
AI is going to be a huge help in the development of smart 3D printers that can cut down on errors while improving print speed. We will also start to see more generative AI develop models. I am most excited to see AI applied to parametric design to make developing new designs easier for everyone.
Roger Uceda, CEO, aridditive
Artificial intelligence is poised to transform the 3D printing industry by democratizing design. In 2025, AI-driven tools are set to make 3D modeling accessible to non-experts, enabling anyone to create complex designs through natural language commands or intuitive sketch-based interfaces. This shift is expected to empower a broader audience, from small businesses to individual creators, to harness 3D printing without needing advanced technical skills.
This democratization is likely to trigger a resurgence of desktop 3D printers, making them a central tool for creativity and innovation. Companies like Prusa and BambuLab are well-positioned to capitalize on this trend, as their user-friendly and high-performance systems align perfectly with the needs of this expanding market. This new wave of accessibility and innovation will redefine the role of 3D printing in everyday life and professional environments.
Davide Ardizzoia, COO, 3ntr
AI will be pervasive into design, bringing topological mapping into initial phases. Will surely then find places into part placement optimization, parameter tweaking and polymer research.
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