Feasibility Setup Tool for Machine Vision Optimization
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Solution Overview
Problem
Machine vision systems in industrial settings face challenges in achieving high fidelity image analysis efficiently, often requiring extensive tuning of imaging settings, leading to costly production line downtime and potential defects in product quality due to inadequate system adjustments.
Innovation Solution
A method and system for optimizing machine vision system performance through a feasibility setup analysis using processors to compare image results with pass and fail indications, generating suggestions for adjustments in imaging settings and tool configurations, and automatically applying these adjustments to enhance image capture and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a technician manually tunes imaging settings for machine vision tools, then the system may achieve adequate configuration, but the process requires hours of time and may never reach optimal performance
Solution Approach 1:
The machine vision system automatically performs feasibility setup analysis and generates optimization suggestions without requiring manual technician intervention. The system self-diagnoses image quality issues and proposes setting adjustments, eliminating the need for hours of manual tuning while achieving optimal performance.
Solution Approach 2:
The system analyzes actual image capture results and provides feedback about image quality metrics, then generates recommendations for adjusting imaging settings. This closed-loop feedback mechanism enables the system to automatically improve image analysis fidelity based on real performance data.
2Reliability
If the production line is stopped to diagnose machine vision problems, then the system can be troubleshooted, but massive economic losses occur and restart takes hours or days
Solution Approach 1:
The feasibility setup analysis is performed in advance during setup or maintenance periods, so that when the system operates, optimization suggestions are already available. This preliminary analysis enables quick troubleshooting without stopping the production line, as technicians can immediately apply pre-generated recommendations.
Solution Approach 2:
The system introduces an intermediary optimization suggestion mechanism that bridges the gap between image capture and production decisions. Instead of stopping the line for diagnosis, the intermediary analysis tool provides actionable recommendations that can be implemented quickly, maintaining production continuity while improving system reliability.
3Productivity
If the machine vision system is placed in bypass mode to continue production, then the production line remains operational, but parts pass without inspection allowing defective products to leave the facility
Solution Approach 1:
The system performs feasibility setup analysis in advance to establish optimized imaging configurations before production begins. This preliminary optimization ensures the machine vision system is properly configured and ready to inspect parts, eliminating the need to place it in bypass mode during normal operation and maintaining both productivity and quality control.
4Measurement precision
If multiple machine vision tools are included in each machine vision job, then comprehensive image analysis is achieved, but the operator cannot determine specific imaging setting adjustments for each tool
Solution Approach 1:
The system provides individualized optimization suggestions for each machine vision tool within a multi-tool job. By segmenting the analysis and recommendations by tool, the system maintains comprehensive image analysis capability while making settings adjustment manageable, as each tool's specific needs are addressed separately rather than as a monolithic system.
Data Source
AI summary
Methods and systems for optimizing performance of a machine vision system are disclosed herein. An example method includes obtaining one or more first and second images of a target object, where each of the one or more first and second images include a pass indication and a fail indication, respectively. The example method further includes conducting, by a feasibility setup tool, a feasibility setup analysis by (i) performing machine vision techniques on each of the one or more first and second images and (ii) generating a respective updated result indication for each of the one or more first and second images. The example method further includes comparing the respective updated result indication to the respective pass indications and fail indications for the one or more first and second images, respectively; and based on the comparing, generating one or more suggestions to optimize the performance of the machine vision system.