The Future of 3D CAD Design with Natural Language Processing

In recent years, 3D CAD (Computer-Aided Design) has transformed how we approach product design and development. 3D CAD data is now the global standard for digital manufacturing, such as computer numerical control machining, computer-aided engineering (physical simulation), and 3D/4D printing. The digitalization of manufacturing enhances our work and makes it more efficient and collaborative in a globalized market.

Although 3D CAD software must be a game-changer in manufacturing industries, engineers must spend a lot of time gaining their skills and knowledge about 3D CAD software such as Autodesk Inventor, Solidworks, and CATIA, which have different features in their operations and functions. Engineers need to learn this software separately. Sometimes, it isn’t easy to understand, even if someone has knowledge and experience with a specific software. Moreover, if someone is not in the engineering department, it tends to be challenging to operate/learn 3D CAD software from scratch.

3D model design with Autodesk Inventor, image source: Autodesk

Phisical Simulation (CAE) with Solidworks, image source: Solidworks

Given the current trend of ”Technological Democratization,” 3D CAD software, which has not had an unchanged user interface for 50 years, needs to redefine its user interface as everyone’s tool for manufacturing. This will activate cross-disciplinary collaboration involving more and more.

HOW DOES THE DEMOCRATIZATION OF 3DCAD SOFTWARE HAPPEN??

A new frontier is emerging—the integration of Natural Language Processing (NLP) into 3D CAD systems, thanks to the transformer architecture developed by Google researchers (Ashish Vaswani et al., 2017). Leading large language models (LLMs) such as BIRT (Google), GPT-4(OpenAI) and DeepSeek(DeepSeek) have the potential to revolutionize the design process, making it more intuitive, efficient, and accessible for everyone.

Paradimshift of User Experience with NLP

As I mentioned, the design process in traditional 3D CAD systems can be complex and often requires high technical skill. However, by integrating NLP and machine learning(ML), we are moving towards a future where designers can interact with CAD systems in a more natural, conversational way. Imagine being able to describe your design concept using everyday language (any language we use) — the CAD system would then translate this into a 3D model and optimize the shape. For example, a user might say,” Create a gear with a 50mm diameter and 10 teeth,” and the system would automatically generate the corresponding 3D model.

This shift would democratize design, allowing individuals without deep CAD expertise to bring their ideas to life. It would also increase efficiency by reducing the time spent learning and navigating complex software interfaces. Moreover, it would break the barrier between languages such as English, Chinese, and Japanese.

Shape Generation Profess with large language model (LLM). Image source: Akshay Badagabettu et.al.

Streamlining the Design Process

Another benefit of incorporating NLP into 3D CAD is the potential to streamline the design process. NLP could be used to automate repetitive tasks, such as adjusting dimensions or converting between different units of measurement. Additionally, NLP-powered systems could detect errors by recognizing design discrepancies or inconsistencies based on the designer’s verbal input.

Furthermore, integrating NLP with AI-driven optimized design suggestions could help designers explore new ideas more quickly. For instance, after inputting a design specification, the system could propose optimizations or variations based on best practices or historical data, providing designers with options they might not have considered.

NLP for Collaboration

NLP could be pivotal in communication and idea sharing in collaborative design environments. Designers, engineers, and other stakeholders could communicate more effectively by speaking or typing in natural language. This would remove barriers related to technical jargon, allowing team members with different backgrounds to contribute seamlessly.

This could benefit multidisciplinary teams, where participants may not have expertise in each other’s fields. NLP could act as a bridge/hab, enabling cross-functional teams to communicate their ideas without needing deep technical knowledge of each other’s disciplines.

3D model generation example with Autodesk Inventor, image source: Kivanc Kizildemir@Codeo Solutions Limited

Future Prospects and Challenges

While integrating NLP into 3D CAD systems holds exciting potential, there are still challenges to overcome. One of the biggest hurdles is ensuring the accuracy and contextual understanding of natural language inputs. CAD systems must correctly generate the intended design and interpret vague or ambiguous descriptions to generate the intended fine-tuned documents in desired fields; context recognition in expert conviction is still challenging for NLP.

Moreover, data privacy and security concerns must be addressed, especially if sensitive design information is communicated through NLP-powered interfaces. Technology regulations due to security concerns and economic wars often disrupt technology development for businesses, especially startups. Thus we need to consider for our businesses the following:

Could open-source NLP like GPT-J be the best option?
Is DeepSeek a promising way (Should be affected by regulations in specific countries)?
Should we switch NLP systems depending on the country? For China, use DeepSeek; for the U.S., use ChatGPT. (Development costs could skyrocket)

To return to the topic… As NLP technologies evolve, we can expect even more powerful and user-friendly systems that will redefine how we approach design and product development.

By making design tools more accessible and efficient, NLP could unlock new possibilities in industries ranging from manufacturing to healthcare, where precise and innovative design is critical.

Conclusion

Natural Language Processing is poised to be a game-changer for digitalized intelligent manufacturing with 3D CAD software. The ability to interact with design systems using natural language will enhance user experience, increase productivity, and foster more inclusive collaboration. As this technology advances, it has the potential to shape the future of design and innovation in ways we are only beginning to imagine.

NLP-based interactive assistance for 3D shape generation will provide a more efficient and collaborative working environment. What kinds of features in NLP-based design software would enable both technical and non-technical users to experience the democratization of design?

References

Badagabettu, Akshay, Sai Sravan Yarlagadda, and Amir Barati Farimani “Query2CAD: Generating CAD Models Using Natural Language Queries” arXiv , 2024, https://arxiv.org/abs/2406.00144
Kung, Chuei-Huei, and Kuei-Chia Liang.”Exploring the Usability and Future Development of AI-Generated 3D Models in CAD Workflows and the Metaverse Based on 3D Model Standards” Computer-Aided Design and Applications, 782–804, 2025, http://dx.doi.org/10.14733/cadaps.2025.782-804
David J Kasik, William Buxton, and David R Ferguson. Ten cad challenges. IEEE Computer Graphics and Applications, 25(2):81–92, 2005, https://ieeexplore.ieee.org/document/1405956/