Robot Fleet Task Planning With Digital Twins for AM Reliability
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Solution Overview
Problem
Existing additive manufacturing processes, including 3D printing, face inefficiencies, process variations, product inconsistency, and unreliability, leading to final products that may not meet customer expectations or specifications, and resulting in increased operating costs and supply chain risks.
Innovation Solution
A robot fleet management platform that includes a governance-enabling intelligence layer with artificial intelligence services, such as machine learning and digital twin services, to optimize additive manufacturing processes, manage supply chains, and improve demand management by providing real-time insights and decision-making support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional additive manufacturing processes are used, then manufacturing flexibility is improved, but manufacturing precision and product consistency deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where sensor data from the additive manufacturing process is continuously collected and fed into machine learning models. These models analyze the data in real-time and provide feedback adjustments to process parameters, enabling dynamic compensation for variations and maintaining consistent product quality despite the flexibility of additive manufacturing.
Solution Approach 2:
The patent replaces traditional mechanical control systems with AI-based machine learning models that process sensor data and optimize manufacturing parameters. This substitution enables more precise control and prediction of manufacturing outcomes, improving product consistency while preserving the flexibility of additive manufacturing processes.
2Ease of manufacture
If traditional additive manufacturing processes are used, then manufacturing capability is improved, but reliability deteriorates
Solution Approach 1:
The patent employs machine learning models to predict potential process failures and quality issues before they occur during additive manufacturing. By analyzing historical and real-time sensor data, the system identifies trends and patterns that indicate impending problems, allowing preventive actions to be taken before defects are generated, thereby improving process reliability.
Solution Approach 2:
The system continuously monitors manufacturing processes through sensor networks and uses machine learning to provide real-time feedback on process health and quality metrics. This feedback enables dynamic adjustments to maintain reliable operation and detect deviations from expected performance patterns.
3Measurement precision
If more sensor data is collected, then measurement capability is improved, but device complexity increases
Solution Approach 1:
The patent implements a unified machine learning platform that processes data from multiple sensor types (thermal, mechanical, optical, etc.) through a single integrated system. This multi-functional approach allows the same AI infrastructure to handle diverse sensor inputs, reducing overall system complexity while maintaining enhanced measurement capabilities across all process parameters.
Data Source
AI summary
A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.