Advanced Manufacturing Lab
The Advanced Manufacturing Lab in partnership with Penn-State and University of Kentucky includes an AI Sandbox, a dynamic environment where cutting-edge AI solutions are tested and refined to address the complex challenges of modern manufacturing. The sandbox provides a controlled, collaborative space for innovation, enabling the seamless integration of AI into manufacturing workflows.
The AI Sandbox: A Hub for Innovation
The sandbox showcases advanced systems designed to simulate, test, and enhance manufacturing processes in real-world conditions. Key features include:
- Collaborative Robotics: Intelligent robots designed for assembly, material handling, and quality inspection, adaptable to a range of manufacturing scenarios.
- Immersive Quality Inspection Systems: AI-powered multi-camera setups capture and analyze detailed 3D visual data, enabling more precise defect detection and product validation.
- Real-Time Process Control: AI-driven logic systems optimize workflows, ensuring faster and more accurate adjustments to dynamic production environments.
- Enhanced Decision-Making Tools: Integrated AI systems offer predictive insights for maintenance, safety, and resource efficiency.
Applications of the Sandbox
- Dynamic Testing: Real-time simulations of manufacturing processes to evaluate AI solutions under controlled conditions.
- Workflow Optimization: Iterative testing of new production line configurations and process improvements before full-scale deployment.
- AI Integration Trials: Development and refinement of intelligent systems for robotics, defect detection, and predictive maintenance.
Broader Impact
The AI Sandbox fosters an iterative innovation process allowing the lab to develop resilient, impactful solutions. This hands-on approach ensures the lab remains at the forefront of intelligent manufacturing, delivering scalable and sustainable outcomes.
Learn more about our Manufacturing Advisory Council
Example Engagement Process in the AI Sandbox
The engagement process showcased in this infographic highlights the iterative approach to developing, testing, and deploying AI-driven computer vision models within the AI Sandbox. By breaking down the process into distinct stages, it ensures structured innovation, scalability, and alignment with production requirements.
The AI Sandbox enables a variety of engagement types beyond defect detection, tailored to specific challenges in manufacturing:
- Predictive Maintenance:
- Collect data from IoT sensors on machinery, analyze vibration patterns, and build AI models to predict failures before they occur.
- Pilot predictive maintenance models using a staged testing process similar to defect detection workflows.
- Production Line Optimization:
- Use AI to simulate different workflow configurations, leveraging generative models to identify bottlenecks and propose layout optimizations.
- Test multiple scenarios in the sandbox before implementing changes on the factory floor.
- Quality Assurance Beyond Defects:
- Analyze product assembly or packaging for compliance with specifications, ensuring consistency and reducing waste.
- Create custom vision models for specific product lines, improving quality control.
- Sustainability Monitoring:
- Integrate AI to measure energy consumption and resource usage, suggesting improvements for greener manufacturing.
- Pilot energy-efficient workflows using real-time data from the sandbox.