Tech Soft 3D releases HOOPS AI for CAD workflows

Key Takeaways

• Tech Soft 3D has released HOOPS AI, a framework for AI and machine learning workflows in CAD data
• HOOPS AI supports multiple stages of the machine learning workflow, including CAD access, dataset preparation, and visualization
• The framework is designed for applications such as part classification, manufacturing feature detection, and similarity search
• HOOPS AI is now generally available for developers and data scientists
• The technology is intended to support the use of AI and CAD data by software vendors and engineering teams

Introduction to HOOPS AI

Tech Soft 3D has announced the full release of HOOPS AI, a framework designed to support AI and machine learning workflows for CAD data. This release follows a beta program that involved over 30 companies and provides a solution for developers and data scientists who want to use 3D engineering data in machine learning pipelines.

Challenges in Using CAD Data in Machine Learning

Using CAD data in machine learning workflows has been challenging due to the complexity of the data and the need for custom scripts and workflows. HOOPS AI is intended to provide a framework for building machine learning models with CAD data, reducing the cost and development time associated with custom approaches.

Features and Capabilities of HOOPS AI

HOOPS AI supports multiple stages of the machine learning workflow, including:

• CAD access: providing access to CAD data for use in machine learning models
• Dataset preparation: preparing CAD data for use in machine learning models
• Encoding: encoding CAD data for use in machine learning models
• Visualization: visualizing CAD data and machine learning results
• Scaling tools: providing tools for scaling machine learning models to large datasets

The following comparison table highlights the key features of HOOPS AI:

Applications of HOOPS AI

HOOPS AI is designed for applications such as:

• Part classification: classifying parts based on their CAD data
• Manufacturing feature detection: detecting manufacturing features in CAD data
• Similarity search: searching for similar parts or designs in large design libraries

Future Development

The development team plans to expand Python access to additional HOOPS Exchange-backed data, including PMI. The long-term goal is to make engineering information in CAD models more accessible across teams.

Bottom Line

HOOPS AI provides a framework for building machine learning models with CAD data, reducing the cost and development time associated with custom approaches. With its support for multiple stages of the machine learning workflow and its applications in part classification, manufacturing feature detection, and similarity search, HOOPS AI is a valuable tool for software vendors and engineering teams working with complex CAD datasets.