3D printing gives designers more freedom to create new and innovative products, but it’s important to know how to design for additive manufacturing (AM). When using AM, manufacturers aren’t just replicating traditionally manufactured parts in a 3D printing environment. Rather, they are creating newly designed parts with improved functionality, which is made possible by the capabilities of 3D printers.
Designing for additive manufacturing is a standard part of the production process for 3D printed parts. The industry has been using AM to produce end-use parts for years, and now manufacturers are focused on optimizing the design process. Technological advancements have allowed for multiple design iterations of a single part and quicker turnaround time for design changes.
When manufacturers consider all the capabilities of AM during the design phase, they can often create a part that is lighter and stronger than the traditionally manufactured counterpart. As technology continues to advance and 3D printing becomes more prevalent, knowing how to design for AM to maximize a part’s capabilities will become the industry standard.
Driving a New 3D Printing Dynamic in 2026
As 3D printing becomes a more practical manufacturing solution for companies, this has led to a new dynamic in industry. Manufacturers are now increasingly partnering with AM experts, recognizing the need to lean on experience when incorporating 3D printing into production operations.
With so much uncertainty in global supply chains, manufacturers are looking to 3D printing as a way to move production and design closer to operations. AM experts help identify what parts may be best suited to design adjustments. This not only leads to closer production of these parts but also allows manufacturers to leverage AM for more sustainable product designs. Both nearshoring and 3D printing enable companies to take advantage of new part designs while strengthening their supply chains and reducing energy consumption.
New technologies are also impacting this shifting product design dynamic. Artificial intelligence (AI) and other smart tools can be paired with AM tech to automate design, so manufacturers can not only have better part designs, but optimal ones.
From Physical Reality to Digital Advantage
AM part designs can be continuously changed and improved within a digital CAD file, enabling the creation of complex parts with essentially the press of a button. But not all AM parts start with digital vision. Sometimes 3D printed parts are used to replace critical components that are still in use — and often these can’t be removed and have unreliable, if any, design documentation. So, a professional who knows how to design for additive manufacturing then has to create something that can take over for these components without much knowledge of the initial designs.
This article from SME Media highlights two situations where engineers turned to AM to transform an existing working component and create a better part design. The first is from the Ontario-based Bruce Power company, where engineers utilized AM to recreate complex parts used for radiation-shielding. As these components evolved over time, their documentation became incomplete and outdated. This resulted in having to measure the parts in place, which is a shift from how additive designs are normally started, but did allow the engineers to collect data on the wear and tear of the current part and incorporate that into their new design.
Once measurement and data collection was complete, the Bruce Power team was able to reverse engineer a design and convert scan data into a digital CAD model. The engineers now had a basis to optimize the design before physically manufacturing a replacement part. They used these digital files to improve part geometry and material placement while maintaining performance.
The other project involved Pienergies, a French energy business. Its team needed to redesign a Pelton wheel in a remote micro-hydropower station, and the team couldn’t remove the current part from service until it had the replacement created. The Pienergies team took steps similar to what Bruce Power had taken to solve the issue. Using digital CAD files, the engineers modernized the part without having to replace the entire system.
These projects offer a nuanced example of designing for additive manufacturing. In both cases, the design started with data that became a digitized design, enabling faster iteration and optimization. The projects are examples of how AM is increasingly used for current infrastructure, rather than just new products. These examples highlight AM production as a series of processes, from data collection to scan to production. To successfully create 3D printed parts, engineers and designers must be familiar with the whole process instead of viewing AM as an isolated production machine. This will result in scalability and increased reliability for AM in the manufacturing industry.
Democratizing Design Through the Power of Agentic AI
Agentic AI is helping to advance AM, reshaping how engineering teams approach design, simulation, and iteration. Whie AM has long enabled the creation of complex products, optimizing those designs has traditionally required specialized simulation expertise concentrated among a small group of engineers. This bottleneck limits a team’s ability to explore new ideas and delays innovation.
The rise of large language models and AI agents is changing this dynamic. Agentic systems can create and manage intricate simulation workflows, handle tasks like generating design variations, and surface actionable design insights — making advanced tools accessible beyond the specialist community. This broadens participation in the design process and accelerates iteration — leading to optimized designs for a wide range of 3D printed products.
The results of integrating these technologies are already measurable. Siemens Energy was able to reduce design iteration turnaround time for 3D printed heat exchangers by up to 30% using cloud-native simulation tools, while Swiss engineering firm Cross-ING cut simulation time by 40% through streamlined geometry handling and preprocessing improvements.
The use of agentic AI shifts simulation from a final validation step into an active, collaborative partner throughout the design process. This enables more engineers to contribute, explore a wider range of designs, and drive better outcomes at scale.
The Future of Design
Designing for additive manufacturing is a necessity for creating 3D printed parts. These parts aren’t exact reproductions of traditionally manufactured parts, and their product design should reflect that. With AM, there are many possibilities for part design, from unique geometries to different materials, and these options can often improve an end-use part.
Creating these new part designs has helped shape the modern AM landscape, from promoting AM expertise in industry to reshaping how legacy parts are redesigned to adopting advanced technologies alongside 3D printers. AM adoption is steadily increasing in manufacturing, and as it becomes more prevalent these companies are looking to continually optimize product designs and leverage AM’s full capabilities.
The implementation of other advanced technologies alongside AM is helping more engineers learn how to utilize and design 3D printed parts. But there’s still a need for more AM specialists in the manufacturing industry. If you’re a manufacturer or engineer wanting to learn more about AM and 3D printing, look into attending RAPID + TCT. The 2027 event takes place April 12-15 at Huntington Place in Detroit. Don’t miss the chance to attend North America’s largest additive manufacturing and industrial 3D printing event.