Robot Fleet Job Parsing With Digital Twin Workflow Simulation
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Solution Overview
Problem
The existing additive manufacturing and supply chain management systems face inefficiencies, product inconsistency, and unreliability in 3D printing, leading to increased costs and supply chain risks due to inadequate monitoring and optimization, as well as limitations in conventional machine vision systems for capturing and processing data, especially in dynamic environments.
Innovation Solution
A robot fleet management platform with a governance-enabling intelligence layer that includes artificial intelligence services, machine learning, and digital twin capabilities for optimizing additive manufacturing processes, supply chain management, and enhanced vision applications, enabling smarter product design, monitoring, and decision-making across the value chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional machine vision systems are used for monitoring additive manufacturing processes, then device complexity is reduced, but manufacturing precision and measurement precision deteriorate due to inadequate data capture and processing capabilities
Solution Approach 1:
The patent introduces an intermediary processing layer between the additive manufacturing process and the vision system. This layer includes image processing algorithms, machine learning models, and data fusion mechanisms that enhance the capabilities of conventional vision systems without requiring complete system replacement. The intermediary processes raw visual data to extract meaningful information about print quality, layer adhesion, and dimensional accuracy.
Solution Approach 2:
The monitoring system employs dynamic adjustment of imaging parameters, processing algorithms, and analysis thresholds based on real-time manufacturing conditions. The system adapts its complexity level according to the specific manufacturing stage, material being used, and detected anomalies, allowing it to maintain high precision while managing computational resources efficiently.
2Productivity
If advanced AI services and digital twin capabilities are integrated into the robot fleet management platform, then productivity and reliability improve, but device complexity increases due to the governance-enabling intelligence layer
Solution Approach 1:
The platform architecture is segmented into distinct functional layers: data collection layer, processing layer with AI services, digital twin layer, and application layer. Each layer performs specific functions and can be independently developed, deployed, and maintained. The governance-enabling intelligence layer acts as a coordinating segment that manages interactions between other segments without requiring complete integration of all components simultaneously.
Solution Approach 2:
The digital twin framework serves multiple functions simultaneously: it acts as a virtual replica for simulation, a prediction engine for quality outcomes, an optimization tool for process parameters, and a training environment for machine learning models. This multi-functionality reduces the need for separate specialized systems while maintaining high productivity benefits.
3Reliability
If real-time monitoring and optimization systems are implemented in additive manufacturing, then manufacturing precision and reliability improve, but loss of time increases due to data processing and analysis requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing images during manufacturing, pre-training machine learning models on historical data, and pre-establishing quality thresholds and acceptance criteria. Digital twins are created in advance to simulate various manufacturing scenarios and predict potential quality issues before they occur in actual production, reducing the need for extensive real-time analysis.
Solution Approach 2:
The monitoring system implements selective skipping of detailed analysis for routine, high-confidence manufacturing conditions. When manufacturing parameters remain within established normal ranges and no anomalies are detected, the system skips comprehensive image processing and jumps directly to quality certification, significantly reducing processing time while maintaining reliability through spot checks and continuous monitoring.
Data Source
AI summary
A robot fleet management platform includes a job parsing system that applies filters to identify portions of a job request suitable for robot automation. Based on the identified portions and a first fleet objective of the job request, a task system establishes tasks that define a robot type and task objective. A proxy service associates a robot of a robot fleet to each task and adaptation instructions to define how to adapt the robot fleet to perform the tasks. A workflow system generates a workflow defining a performance order of the tasks. A simulation system applies the workflow in an environment that includes digital models of the robot fleet and the tasks. The simulation is used to iteratively redefine the tasks and workflow until a second fleet objective is satisfied. A generation system generates a job execution plan in response to the simulation satisfying the first and second fleet objectives.