Project Overview
This project focused on automating manual validation workflows for robotic systems, where testing procedures were time-intensive, inconsistent, and dependent on technician interaction. Existing processes required operators to manually run tests, record results, and monitor performance, limiting throughput and introducing variability in data collection.
The objective was to develop a fully automated testing and monitoring system capable of executing validation procedures, logging results, and providing real-time quality insights with minimal user intervention.
System Design & Implementation
I developed a Python-based automation framework that combined image recognition with automated mouse and keyboard inputs to interact directly with existing test software.
This enabled automation without modifying underlying systems, allowing rapid deployment across different test environments.
The system supported two primary validation workflows:
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Bot-level assembly testing
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Motor and driver subsystem testing
Once initialized, the system could run independently, enabling technicians to operate multiple test stations in parallel and significantly increasing throughput.
All test results were automatically logged into a structured database, including associated lot numbers and serial numbers, ensuring full traceability across manufacturing batches.
Data Analysis, Monitoring & Deployment
Beyond execution, the system incorporated real-time quality monitoring using statistical process control methods. I implemented automated QC control charts that updated after each test cycle and applied Nelson’s Rules to detect anomalies and non-random patterns, alerting operators to potential issues.
All data and analytics outputs were integrated into a shared engineering cloud environment, enabling visibility across engineering, quality, and manufacturing teams.
To support scalability, I worked with the software engineering team to package the tool and integrate it into the company’s version control pipeline, ensuring maintainability, version tracking, and controlled deployment.
The system was ultimately adopted company-wide and extended to external clients, allowing field teams to run the same validation workflows on deployed robotic systems.
Results & Impact
The system significantly improved test efficiency, consistency, and data quality, eliminating manual steps and reducing operator dependency. By enabling parallel test execution and automated data logging, it increased throughput while minimizing human error.
The integration of real-time analytics transformed testing into a proactive quality monitoring system, enabling early detection of issues both in production and in the field. Its adoption across internal teams and external clients demonstrated its robustness and scalability as a validation platform.
Key Takeaways
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Automation can transform validation from a manual process into a scalable system
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Image-based automation enables rapid deployment without modifying existing software
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Real-time analytics adds significant value beyond simple data collection
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Integration with version control and deployment pipelines is critical for scalable engineering tools
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Designing for both internal and external users ensures broader organizational impact