Distributed Manufacturing Ledger With AI Workflow Optimization
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Solution Overview
Problem
Existing additive manufacturing processes are prone to inefficiencies, product inconsistencies, and unreliability, leading to increased costs and supply chain risks due to process variations, material waste, and unoptimized printing parameters, while conventional machine vision systems struggle with capturing rich object information and dynamic environments, and robotics implementations lack integration with advanced technologies for enhanced automation.
Innovation Solution
A robot fleet management platform with artificial intelligence services and a distributed manufacturing network utilizing a cloud-based management platform, digital twins, and a distributed ledger system to optimize additive manufacturing processes, improve vision capabilities, and enhance robotics automation, enabling smarter product design, manufacturing, and supply chain management through real-time data analysis and adaptive intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional additive manufacturing processes are used, then manufacturing capability is provided, but process variations lead to product inconsistencies and unreliability
Solution Approach 1:
The patent implements a feedback mechanism where sensor data from the additive manufacturing process is continuously collected and analyzed. The system compares actual process parameters against optimal parameters and automatically adjusts printing conditions in real-time to maintain consistency and eliminate variations that lead to product defects.
Solution Approach 2:
The patent replaces manual or mechanical process control with automated systems including AI-driven parameter optimization, robotic material handling, and computer-controlled printing parameters. This substitution eliminates human error and mechanical inconsistencies, leading to more reliable and consistent manufacturing outcomes.
2Loss of information
If traditional manufacturing and supply chain management are used, then operations can be maintained, but data complexity and volume overwhelm users and miss opportunities for insight
Solution Approach 1:
The patent introduces an intermediary layer consisting of AI algorithms and data analytics platforms that sit between the raw data sources and human users. This intermediary automatically processes, filters, and translates complex manufacturing and supply chain data into actionable insights, eliminating information overload while preserving critical intelligence.
Solution Approach 2:
The patent creates digital twins—virtual copies of physical manufacturing processes and supply chain operations. These digital replicas allow users to analyze complex data scenarios without overwhelming the system, enabling insight generation while managing computational complexity through simplified virtual models.
3Measurement precision
If conventional machine vision systems are used, then object detection is provided, but rich object information and dynamic environment capture are insufficient
Solution Approach 1:
The patent merges multiple sensing modalities including cameras, LIDAR, depth sensors, and environmental sensors into an integrated vision system. This combination allows simultaneous capture of detailed object information and dynamic environmental context, overcoming the limitations of conventional single-modality vision systems.
Solution Approach 2:
The patent transitions from 2D image capture to multi-dimensional sensing by incorporating depth information, spatial mapping, and temporal dynamics. This dimensional expansion enables comprehensive object characterization and dynamic environment understanding that conventional 2D vision systems cannot achieve.
4Adaptability or versatility
If additive manufacturing is used, then manufacturing flexibility is provided, but material waste and unoptimized printing parameters increase costs
Solution Approach 1:
The patent performs preliminary optimization of printing parameters, support structures, and toolpath generation before the actual manufacturing process. By pre-calculating optimal configurations using AI algorithms, the system minimizes material waste and printing time while maintaining manufacturing flexibility for custom designs.
Solution Approach 2:
The patent dynamically adjusts printing parameters such as layer thickness, infill density, printing speed, and temperature based on real-time analysis and historical data. These parameter optimizations reduce material consumption and energy usage while maintaining product quality and design flexibility.
Data Source
AI summary
A distributed manufacturing network includes a distributed ledger system and an artificial intelligence system. The distributed ledger system is integrated with digital threads of a set of distributed manufacturing network entities for storing information on event, activities and transactions related to the distributed manufacturing network entities. The artificial intelligence system is configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.