Robot Component Evaluation Using AI Smart Rooms and PCB Checks
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Solution Overview
Problem
Existing systems lack an effective method for automatic self-evaluation and testing of robots, particularly in factory environments, which can lead to undetected sensor damage affecting equipment output, as they do not comprehensively assess the health of sensors and components.
Innovation Solution
An automatic evaluation system utilizing a processor and memory to evaluate robot components and printed circuit boards (PCBs) through various units such as sensor evaluation, PCB fabrication, and AI-powered quality checks, identifying passed and failed components, and initiating maintenance requests for sub-optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are placed near equipment to detect health metrics, then detection capability is improved, but the sensors may get damaged affecting output
Solution Approach 1:
The system divides the detection function into two separate modules: evaluation sensors placed near equipment for detailed health monitoring, and output sensors positioned at the output for verifying actual equipment output. This segmentation allows the evaluation sensors to perform close monitoring while the output sensors verify results, preventing single-point failure from compromising the entire system.
Solution Approach 2:
The system introduces an intermediary evaluation system that indirectly assesses equipment health by monitoring sensors near the equipment without requiring direct contact with the equipment itself. This intermediary approach allows health detection while reducing the risk of sensor damage from direct exposure to equipment conditions.
2Device complexity
If existing systems detect equipment output to assess health, then system complexity is reduced, but detection accuracy deteriorates as sensor damage goes undetected
Solution Approach 1:
The system implements a feedback mechanism where output sensors continuously monitor the actual output of equipment and compare it against expected performance. When discrepancies are detected, the system triggers alerts and automatically adjusts operations or schedules maintenance, creating a closed-loop system that improves detection accuracy without requiring overly complex manual inspection procedures.
Solution Approach 2:
The system performs preliminary evaluation of sensor health and equipment status before actual operation or failure occurs. By continuously monitoring sensor metrics and predicting potential failures, the system can take preventive actions such as scheduling maintenance or replacing components before they cause equipment failure, thereby improving detection accuracy while maintaining manageable system complexity.
3Reliability
If comprehensive component evaluation is performed, then reliability is improved, but productivity decreases due to extended testing time
Solution Approach 1:
Components undergo comprehensive evaluation and testing before being assembled into the final product. By performing all necessary reliability checks, functional tests, and quality validations in advance, the system ensures that only fully tested and approved components enter assembly. This preliminary action prevents rework and returns during production, thereby maintaining high assembly throughput while ensuring component reliability.
Solution Approach 2:
The evaluation process is segmented into independent parallel testing streams for different component types (sensors, actuators, processors, etc.). Multiple components can be evaluated simultaneously in different testing stations, and passing components are immediately approved for assembly. This parallel processing approach maintains comprehensive evaluation standards while preventing bottlenecks in the assembly pipeline, thus preserving productivity.
Data Source
AI summary
A system for automatic self-evaluation and testing one or more sensors and one or more peripherals in the robot 100. The AI system controls an end-to-end factory environment without human intervention. The AI system includes one or more smart rooms to test the one or more sensors and one or more peripherals in the robot. The one or more peripherals damaged in the robot 100 is removed and the new peripheral is placed and the new peripheral is tested by the AI system. The one or more smart rooms in the robot 100 evaluate the one or more peripherals individually to identify the fault in the individual peripherals.