Linnea applies AI and machine learning innovation to additive manufacturing engineering workflows to speed the identification of parts suitable for 3D printing
Key Benefits of Linnea
- Improve efficiency and accuracy of AM part selection process
- Save time and costs by automating analysis for suitable parts identification
- Easily process and classify part features from 2D engineering drawings and 3D engineering models
- Empower confident decisions with AI-enabled AM part candidacy scoring and recommendations
AM engineers spend innumerable hours manually reviewing and analyzing various forms of engineering data from disparate sources to identify good part candidates for 3D printing
Linnea enhances AM workflows with AI automation by taking on the heavy analysis burden and calculating part candidacy scores with recommendations for engineer decision making
Key sustainment and logistics organizations are rapidly adopting additive manufacturing as a strategic lever to:
- Gain decision advantage
- Remediate supply chain challenges
- Manage increased product complexity
- Eliminate lag times and costs in delivering parts for repair
Linnea Part Printability Recommendation System
Feature Extraction
- Customized algorithms extract and process multi-format engineering inputs with machine learning, NLP, and computer vision
- 2D drawing processing parses text, segments geometric objects, extracts geometric contours, and computes features
- 3D model processing extracts text and derives geometric and complexity features
Classification Engine
- Expert decision rules are applied to rapidly identify parts not suitable for printing due to incompatibilities with size, tolerance, or material
- AI/ML-classifiers accurately predict AM part suitability based on complexity and geometry features
- 3D print recommendations and scoring for parts are served to the interactive user interface
Interactive User Interface
- Human-in-the loop workflow enables AM engineer to interact with part printability recommendations for further analyses and reporting
- Final 3D print decisions are made by the AM engineer and captured via the user interface
- Parts identified for 3D printing are cataloged and made available for future analyses and decision making
Ready to elevate you AM processes?
Schedule your personalized demo to experience Linnea in action!
Jan Turkelson, Senior Vice President
Janette Steets, PhD, Associate Vice President, Defense Division
John Tribble, Director of Data Science
Customer Journey Case Studies
Our experts leverage relevant accelerators for specific business goals providing quick wins and efficient return on investment
