Distributed Ledger and Digital Twins for Reliable 3D Printing
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Solution Overview
Problem
Current 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 methods.
Innovation Solution
A robot fleet management platform with a governance library and intelligence layer that utilizes artificial intelligence services, machine learning, and digital twins to optimize additive manufacturing processes, improve supply chain management, and enhance decision-making through real-time data analysis and simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional additive manufacturing processes are used without advanced monitoring and optimization, then device complexity is reduced, but manufacturing precision and reliability deteriorate
Solution Approach 1:
The patent implements a distributed ledger system that continuously monitors and records manufacturing parameters, material properties, and process outcomes. This feedback mechanism enables real-time optimization of additive manufacturing processes, improving precision while managing system complexity through automated data collection and analysis across the value chain network.
Solution Approach 2:
The patent introduces an intelligence layer with AI services that act as intermediaries between the physical additive manufacturing processes and the digital monitoring systems. This intermediary layer processes complex data from multiple sources, translates it into actionable insights, and controls manufacturing parameters, thereby improving precision without directly increasing physical system complexity.
2Reliability
If comprehensive data collection and monitoring systems are implemented in additive manufacturing, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the monitoring and data collection system into distributed components across the value chain network, with each entity (manufacturing nodes, material suppliers, logistics providers) maintaining its own localized data collection and validation capabilities. This segmentation improves reliability through distributed verification while managing complexity by avoiding a single centralized complex system.
Solution Approach 2:
The distributed ledger system serves multiple functions simultaneously: it records manufacturing data, tracks material provenance, monitors process parameters, validates quality metrics, and enables predictive analytics. This multi-functionality improves supply chain reliability comprehensively while avoiding the complexity of separate specialized systems for each function.
3Productivity
If real-time data analysis and AI optimization are deployed, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent implements AI-driven optimization that focuses computational resources on critical manufacturing parameters and high-impact decisions rather than continuously analyzing all possible variables. This partial action approach maintains productivity improvements while reducing unnecessary computational energy consumption by concentrating analytical efforts where they provide the greatest value.
Data Source
AI summary
An information technology system for a distributed manufacturing network includes an additive manufacturing management platform with an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from a set of distributed manufacturing network entities and execute simulations on digital twins of the set of distributed manufacturing network entities to make classifications, predictions, and optimization-related decisions for the set of distributed manufacturing network entities. The information technology system includes a distributed ledger system integrated with a digital thread and configured to provide unified views of workflow and transaction information to the set of distributed manufacturing network entities.