Note: Because the project is still in development, the repository is not available to the public per my employer’s instruction.

Summary

At Purdue’s Digital Enterprise Center, we are developing a manufacturing workflow which integrates with IoT tools, computer vision, and Solumina MES to streamline the assembly process. To showcase this workflow, we are assembling a commercial oil pump over three workstations that will each be responsible for a subset of the assembly steps. As a part of this project, I am building software to perform computer vision aided foreign object debris detection, assembly verification and comprehensive data collection which promotes efficiency, consistency, and traceability of the assembly process.

Notable Work

• For a faster training pipeline, I leveraged image augmentation to increase our dataset by up to 64 times, allowing us to efficiently add new objects of interest to our set of foreign objects. I also streamlined the development and new-hire integration process by creating a CI/CD pipeline using the pdoc library alongside GitHub Workflow, enabling automated building and deployment of a documentation page for the data wrangling and model training scripts.
• Using Rust, Tauri, and Svelte, I developed a desktop application prototype for multi-camera selection and real-time object detection. Starting with example object detection code from the official YOLOv8 repository, I improved performance by 20 times by parallelizing pre and post-processing using Rayon.
• Re-wrote legacy Python code, correcting major issues like unblocked concurrent file access. Replaced inefficient system calls that launched new command-line processes with lightweight threading for improved performance and resource management.
• Currently working on integrating the computer vision pipeline with Atlas Copco’s PF6000 controller for real-time feedback on the assembly process. This will allow us to detect and correct errors in real-time, reducing the need for manual inspection and rework. Assembly data is stored in Solumina MES via its native REST API for enhanced traceability and analysis.