Welcome to MATRIX Labs: Makerspace Advancements Through Research, Innovation, and Cross-Collaboration! We aim to develop intelligent systems to manage and maintain makerspaces.
At Georgia Tech’s MATRIX Lab, we are at the forefront of integrating IoT technologies and intelligent systems into makerspaces, aligning with the visions of Industry 4.0 and Industry 5.0.
Our mission is to transform traditional makerspaces into innovative, interconnected environments that prioritize safety, efficiency, and inclusivity. By embedding sensors, computer vision, and data analytics into tools and workflows, we enable real-time monitoring, predictive maintenance, and enhanced user experiences.
Key initiatives include:
- IoT-Enabled Tool Monitoring: Deploying low-cost, non-invasive sensors on equipment like bandsaws to collect data on parameters such as vibration and temperature, facilitating predictive maintenance and operational insights.
- Vision-Based Safety Systems: Implementing computer vision models to monitor tool usage, ensuring safe operation by detecting anomalies and providing immediate feedback to users.
- Makerspace Dashboard: Developing an interactive platform that aggregates real-time and historical data on attendance, tool utilization, and machine availability, aiding in resource optimization and decision-making.
- Mobile Makerspaces: Designing adaptable makerspace experiences to engage nontraditional users and promote inclusivity across diverse academic disciplines.
Through these initiatives, MATRIX Lab exemplifies the transition from Industry 4.0’s automation and data exchange to Industry 5.0’s emphasis on human-centric solutions, sustainability, and resilience. Our work not only enhances the functionality of makerspaces but also fosters a collaborative and innovative culture that empowers the next generation of creators and engineers.
Here are examples of problems we work on:
1. How to train users and trainers for safe operations within a makerspace? Example: How to design cloud-based and hands-on learning experiences, along with the validation of the training?
2. How to collect and analyze data from makerspaces for efficient operations of the space and equipment within a makerspace? (example: How to develop a queuing system for tools within a makerspace).
3. How can we support and manage the culture of student-run organizations staffing the makerspace with a focus on volunteers who run the makerspaces?
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