Adaptive Additive Manufacturing Workflow With AI Feedback Control
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Solution Overview
Problem
Existing additive manufacturing processes face inefficiencies, product inconsistencies, and unreliability, leading to increased costs and supply chain inefficiencies, while conventional vision technologies struggle with capturing rich object information and dynamic environments, and robotics implementations fail to leverage emerging technologies for full automation.
Innovation Solution
An information technology system with a robot fleet management platform, adaptive intelligence services, and a cloud-based management platform that integrates artificial intelligence, digital twins, and distributed ledgers to optimize additive manufacturing workflows, monitor processes, and automate robotic fleets for enhanced efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional additive manufacturing processes are used, then manufacturing capability is provided, but efficiency is low and product consistency is poor
Solution Approach 1:
The patent implements a feedback mechanism where vision systems capture images of manufactured parts, AI models analyze defects and characteristics, and the results feed back to adjust manufacturing parameters in real-time. This closed-loop control ensures consistent product quality while maintaining high manufacturing efficiency through automated monitoring and adjustment.
Solution Approach 2:
The patent replaces manual inspection and quality control mechanisms with automated vision systems and AI-based analysis. Computer vision cameras, image processing algorithms, and machine learning models substitute for human operators, enabling continuous monitoring without reducing manufacturing speed while ensuring consistent defect detection and product quality.
2Loss of information
If more data is collected from IoT sensors and smart devices, then insights opportunities increase, but complexity and volume overwhelm users
Solution Approach 1:
The patent introduces AI models and analytics platforms as intermediaries between raw IoT sensor data and decision-makers. These intermediaries automatically process, analyze, and synthesize data from multiple sources, transforming overwhelming raw data into actionable insights that reduce complexity while maximizing information value for manufacturing optimization.
Solution Approach 2:
The system implements self-service capabilities where AI models automatically detect patterns, identify defects, and suggest optimizations without human intervention. The system serves itself by autonomously processing data, generating insights, and even adjusting manufacturing parameters, thereby reducing the burden on users while extracting maximum value from collected data.
3Measurement precision
If vision technology is used to capture object information, then manufacturing monitoring is enabled, but rich object information and dynamic environments are not captured
Solution Approach 1:
The patent employs multi-functional vision systems that can detect various defect types (surface defects, dimensional deviations, material inconsistencies), track moving parts, and adapt to different lighting conditions and camera angles. This universal vision system handles diverse manufacturing scenarios and dynamic environments through integrated image processing and AI analysis capabilities.
Solution Approach 2:
The vision system is designed to be dynamic, with adjustable camera angles, movable positioning systems, and real-time parameter adjustment capabilities. The system adapts to moving parts on the production line, adjusts to changing lighting conditions, and dynamically modifies inspection parameters based on the specific manufacturing context, thereby capturing rich object information in dynamic environments.
Data Source
AI summary
An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.