What are some benefits of artificial intelligence in moldmaking?

Published 08/13/2025

Level Up Your Moldmaking With AI

AI-powered technologies, including feature detection, improve moldmaking efficiency and accuracy, enabling experts to focus on innovation.

David Lindemann, Senior Application Engineer, Cimatron

The Intelligence Advantage

While a valuable stepping stone, traditional analytical feature recognition relies on predefined geometric and topological rules to identify features like holes, pockets and slots. This method struggles with complex parts where features intersect or overlap and lacks the adaptability needed for diverse modeling styles.

AI-based feature detection transcends these limitations by harnessing the power of machine learning (ML). Instead of rigid rules, AI algorithms are trained on vast datasets — in this case, over 100,000 artificially and hand-created parts representing a spectrum of shapes, sizes and orientations.

This extensive training enables the AI to ‘learn” to recognize specific part features, categorize them (e.g., “pocket’) and even sub-categorize them based on nuanced characteristics (e.g., ‘open floor pocket,’ “closed contour pocket”). This intelligent approach enables the system to “understand” the design intent in a way that purely analytical methods cannot.

The synergy of artificial and hand-created training data is key. Artificial parts provide the necessary volume for robust learning, while hand-created parts inject realism and accuracy demanded by real-world moldmaking scenarios. This comprehensive training ensures the AI can detect and classify the critical features for mold, tool and die manufacturers.

AI-based feature detection can be used to automatically color-code features on 3D solid models, assigning colors unique to each feature type. This visualization aid streamlines the transition from design to manufacturing, particularly within integrated CAD/CAM systems.

Seamless Automation From Design to Manufacturing

The benefits of AI-based feature detection extend far beyond mere identification. Once features are recognized, the system can automatically color-code them on the 3D solid model, assigning a unique shade to each feature type. This seemingly simple visualization aid dramatically streamlines the transition from design to manufacturing, particularly within integrated CAD/CAM systems.

CAM programmers can then easily identify all “green” pockets earmarked for a specific cutting process. This visual clarity and the ability to link AI-detected features to existing, saved machining processes unlocks a new level of machining automation. Integrated CAD/CAM software can automatically apply the appropriate process parameters to the AI-identified features, significantly reducing manual programming time and potential errors.

Looking ahead, the potential for customization is even more exciting. Future iterations of AI-based feature detection will likely empower users to leverage their historical data to “teach” the system to recognize company-specific features and best practices. This ability to tailor the AI's knowledge will further enhance automation and ensure the application of proven methodologies.

The synergy of artificial and hand-created training data is key.

Designing With Confidence: Trust but Verify

In AI, the concept of a “confidence level” is crucial for practical application. This metric reflects the AI's assessment of the likelihood that a detected feature has been correctly identified.

Recognizing that even sophisticated ML algorithms can make mistakes, the software provides a probability score, allowing users to set confidence thresholds. For example, setting a 75% confidence level ensures that only features meeting this minimum probability are flagged.

Factors such as model complexity, data quality and variations in file formats and modeling styles can influence AI’s confidence. So, while AI offers immense power, the importance of human validation cannot be overstated. Experienced moldmakers can review the AI's output and perform physical measurements to ensure accuracy, creating a powerful human-AI partnership. Also, user feedback through AI feedback tools plays a vital role in these systems' continuous learning and improvement.

AI-based feature detection helps automate the design of plastic injection molds and generate feature geometry for automated CAM programming.

A Glimpse Into the Future of AI in Moldmaking

While the current benefits of AI-based feature detection are already significant, the future holds even more transformative possibilities for moldmakers. We can anticipate AI capabilities expanding to recognize entire assemblies and associated components like ejector pins and nozzles within solid models. This could lead to an automated bill of materials generation, simplifying the ordering process.

For companies dealing with similar or identical jobs, AI could even automate entire mold designs, adapting existing designs based on variations in size. Laborious tasks like electrode design and programming are also ripe for AI-driven automation, with the potential for automatic toolpath generation from electrode geometry.

One of the most exciting prospects lies in generative design. AI algorithms could be employed to suggest optimal placements for critical mold elements like water and vent lines and components like ejector pins and baffles based on user-defined criteria.

Embrace the Intelligent Revolution

The hype surrounding AI can sometimes obscure its real-world value. However, in the world of moldmaking, AI-based technologies are delivering tangible results. By embracing AI-based feature detection, moldmakers can unlock significant gains in efficiency, accuracy and overall productivity, freeing up their expertise for the innovative challenges driving the industry forward. The future of moldmaking is intelligent, and those who embrace this revolution will be well-positioned for continued success.