Coordinate Measuring Fleet Assignment Using Digital Twins
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Solution Overview
Problem
Existing methods fail to optimize the simultaneous use and assignment of multiple coordinate measuring devices across various locations, leading to inefficiencies in resource allocation and idle devices, despite advancements in automatic identification of device capabilities.
Innovation Solution
A computer-implemented method and system that determines the actual position and condition of each device, using digital twins and AI algorithms to optimize device assignment based on job requirements, maintenance status, and device capabilities, minimizing costs and ensuring accurate measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large fleet of coordinate measuring devices is maintained to ensure availability, then device availability is improved, but fleet size and costs increase
Solution Approach 1:
The system performs preliminary actions by predicting future device conditions (maintenance needs, availability) before actual events occur. Digital twins simulate device states and predict when maintenance will be needed, allowing proactive scheduling that prevents downtime without requiring excess devices. This enables optimal fleet sizing based on predicted rather than worst-case scenarios.
Solution Approach 2:
The system implements continuous feedback loops where actual device data (from sensors, maintenance records, usage patterns) is fed back into digital twin models. This feedback refines predictions about device availability and maintenance needs, enabling dynamic adjustment of fleet size and maintenance schedules to match actual performance rather than static assumptions.
2Productivity
If devices are assigned based on simple rules, then assignment speed is improved, but optimization quality deteriorates
Solution Approach 1:
The system replaces manual or simple rule-based assignment mechanisms with AI-driven optimization algorithms. These algorithms process multiple constraints (device capabilities, location, maintenance status, job requirements) simultaneously to generate optimal assignments automatically. The substitution of complex human decision-making or simple heuristics with sophisticated software optimization achieves both speed and quality.
Solution Approach 2:
The system dynamically changes assignment parameters based on real-time device conditions and job requirements. Rather than using fixed assignment rules, the system adjusts device selection criteria based on current digital twin predictions, available capabilities, and maintenance schedules, enabling adaptive optimization that responds to changing conditions while maintaining high assignment speed through automated processing.
3Device complexity
If device capabilities are not individually tracked, then system complexity is reduced, but assignment precision deteriorates
Solution Approach 1:
The system creates digital copies (digital twins) of each physical device that replicate all individual characteristics, capabilities, and status. These digital twins serve as virtual models that can be analyzed and optimized without affecting the physical devices. This copying approach enables precise tracking of individual device properties while keeping the physical system simple, as the complexity is moved to the virtual representation.
Data Source
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AI summary
The invention pertains to a method (100) for optimizing simultaneous use of a multitude of coordinate measuring devices at a multitude of locations, the method comprising: obtaining job information (110) about a multitude of coordinate measuring jobs at the multitude of locations, each job involving one or more of the devices and one or more objects to be measured, the job information comprising the position of the one or more objects, a time for performing the job, required capabilities of the involved devices, and required certifications and/or calibrations for the involved devices; determining an actual position (120) and a condition (130) 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 (140) involving the obtained job information and the determined positions and conditions; and performing, based on a result of the analysis, an assignment optimization (150) for assigning at least a subset of the devices to the jobs, the assignment optimization including at least an optimization regarding usage and maintenance for each device, and paths between the positions of the objects to be measured and the positions of the devices.