Vehicle Repair Material Prediction System
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Solution Overview
Problem
Vehicle repair facilities face challenges in accurately tracking and predicting the materials needed for repairs, leading to potential safety issues and cost estimation inaccuracies due to the use of incorrect or unknown materials.
Innovation Solution
A vehicle repair management system that utilizes current and historical repair data, along with existing specifications, to predict the materials and quantities required for a repair, incorporating machine learning models to customize predictions based on facility location, vehicle age, and other factors, and verifies if repairs were performed according to the predicted plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual tracking methods are used for repair materials, then implementation simplicity is maintained, but measurement precision and reliability of material tracking deteriorate
Solution Approach 1:
The patent replaces manual mechanical tracking methods with an automated computer vision system using cameras and machine learning algorithms to detect and track materials, achieving precise automatic identification and classification of repair materials without manual intervention
Solution Approach 2:
The system enables self-service material tracking where the computer vision system automatically detects, identifies, and records materials without requiring human operators to manually input data, allowing the system to serve itself in monitoring material usage
2Loss of information
If material usage is not predicted before repair, then repair process flexibility is maintained, but loss of information regarding material costs and quantities increases
Solution Approach 1:
The system performs preliminary action by predicting the types and quantities of materials needed before the repair process begins, using computer vision to scan the repair area and machine learning models to forecast material requirements, enabling advance cost estimation and inventory preparation
Solution Approach 2:
The system implements continuous feedback by comparing predicted material usage with actual material consumption detected by the computer vision system during repair, allowing real-time adjustments and accurate tracking of material usage deviations
3Reliability
If correct materials are not verified during repair, then repair process speed is maintained, but reliability of repair quality and safety deteriorates
Solution Approach 1:
The system continuously monitors material usage during repair and provides real-time feedback by comparing actual materials used against the predicted material list, automatically alerting workers when deviations occur to ensure correct materials are used without slowing down the repair process
Solution Approach 2:
The patent replaces manual verification processes with automated computer vision monitoring that continuously tracks material usage and compares it with predicted requirements, providing automatic verification of material correctness without requiring additional manual inspection steps
Data Source
AI summary
A method includes determining, based on current repair data and at least one of historical repair data or existing repair specifications, a predicted material to be used during a vehicle repair, the vehicle repair including replacing or repairing a part of a vehicle. The material includes at least one of an adhesive, an abrasive, a tape, a paint, a coating, or a tool. The method also includes outputting data indicating the predicted material in a predicted material repair plan.