The aim of this project was to design and prototype a smart recycling bin that automatically sorts recyclables using a machine learning system. The bin leverages object recognition, metal detectors, and a precise linear movement system to accurately categorize and dispose of waste. This user-friendly system simplifies recycling and reduces contamination, contributing to more sustainable waste management practices.
The Objective set were:
1 // Analyze Object Recognition Accuracy
2 // Select Appropriate Sensors and Detectors
3 // Design Linear Movement System
4 // Optimize Power Supply and Wiring
5 // Design Frame Structure Using SolidWorks
6 // Select Suitable Materials
7 // Simulate and Test Movement and Sorting Accuracy
8 // Calculate the Cost and Resources Required
9 // Document and Report Progress
As Chief Engineer
Fostered collaboration by mentoring newer members and aligning sub-teams through regular check-ins and task coordination.
Incorporated stakeholder feedback to enhance detection logic and sorting speed, aligning the project with external expectations.
Conducted controlled trials and real-world tests to ensure sorting accuracy across various conditions. Performed stress testing to validate reliability, fine-tuning motor alignment, optimizing the V-Slot rail system, and calibrating sensors to minimize jams and ensure smooth operation over extended use.
Designed the majority of CAD components and created assemblies, ensuring precise fitment and seamless integration of mechanical, electrical, and control systems.
Created physical prototypes using 3D-printed parts to test mechanical designs early, enabling rapid identification of flaws and exploring new design possibilities. This iterative approach minimized development time while ensuring high strength and durability in the final components.
Collaborated with the electrical team to integrate sensors, motors, and controllers, assist with wiring harness design, and troubleshoot circuits to ensure reliable performance.
