Coordinate Measuring Fleet Assignment Using Digital Twins
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Solution Overview
Problem
Existing systems fail to optimize the simultaneous use of a multitude of coordinate measuring devices across multiple locations, leading to inefficiencies and idle devices, without considering individual device properties and characteristics.
Innovation Solution
A computer-implemented method and system that utilizes digital twins, artificial intelligence, and optimization algorithms to assign coordinate measuring devices to jobs based on device conditions, locations, and job requirements, minimizing costs and ensuring compliance with accuracy and maintenance constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If coordinate measuring devices are deployed to multiple locations without optimization, then device availability increases, but device utilization efficiency deteriorates due to idle devices
Solution Approach 1:
The system dynamically reassigns coordinate measuring devices to different jobs and locations based on real-time device conditions, job requirements, and maintenance needs. This dynamic optimization ensures devices are continuously utilized for appropriate tasks while preventing idle time, thereby improving both availability and utilization efficiency simultaneously
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor device conditions, maintenance status, and job completion states. This feedback enables the optimization algorithm to adjust device assignments in real-time, ensuring devices are deployed effectively without remaining idle, thus resolving the contradiction between availability and utilization efficiency
2Loss of time
If devices are assigned based on general criteria without considering individual properties, then assignment speed increases, but measurement precision deteriorates due to mismatched device-job assignments
Solution Approach 1:
The system pre-establishes digital twins for each coordinate measuring device that capture individual properties, capabilities, and conditions. These digital twins are prepared in advance and used by the optimization algorithm to rapidly match devices with suitable jobs while ensuring measurement precision requirements are met, thus achieving both fast assignment and high precision
Solution Approach 2:
The optimization algorithm considers multiple parameters including device capabilities, individual properties, maintenance status, and job requirements simultaneously. By evaluating and weighting these parameters, the system achieves rapid yet precise device-job matching that maintains measurement precision while speeding up the assignment process
3Productivity
If devices are used continuously without maintenance optimization, then productivity increases, but device reliability deteriorates due to lack of maintenance
Solution Approach 1:
The system performs preliminary maintenance scheduling by predicting future maintenance needs based on device conditions and usage patterns. Maintenance tasks are planned and scheduled in advance during periods of lower utilization, allowing continuous productivity while ensuring devices receive necessary maintenance before reliability deteriorates
Solution Approach 2:
The system dynamically adjusts device assignments and maintenance schedules based on real-time device conditions. When a device shows signs of needing maintenance, the system automatically reassigns it to lower-priority tasks or schedules maintenance during optimal time windows, thereby maintaining both productivity and reliability through adaptive management
4Reliability
If a large fleet of devices is maintained to ensure coverage, then device availability increases, but system complexity and costs increase
Solution Approach 1:
The system creates a universal optimization platform that manages diverse coordinate measuring devices through a common interface and methodology. The digital twin approach and optimization algorithm work across different device types and locations, reducing management complexity while maintaining availability by efficiently utilizing the existing fleet without requiring additional devices
Data Source
AI summary
A method for optimizing simultaneous use of a multitude of coordinate measuring devices at a multitude of locations, comprising: obtaining job information about a multitude of coordinate measuring jobs at the multitude of locations, the job information comprising the position of one or more objects, a time for performing the job, required capabilities of involved devices, and required certifications and/or calibrations for the involved devices; determining an actual position and a condition of each device, the condition comprising a measuring precision and a measuring speed, individual conditions of a plurality of components of the device, a maintenance status and a certification and/or calibration status; performing a job analysis involving the obtained job information and the determined positions and conditions; and performing, based on a result of the analysis, an assignment optimization for assigning at least a subset of the devices to the jobs.