Adaptive Additive Manufacturing Control for Real-Time Defect Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Additive manufacturing processes face challenges in rapidly optimizing and adjusting process control parameters in response to changes, leading to suboptimal quality and efficiency in produced parts.
Innovation Solution
The implementation of a machine learning-based system for real-time adaptive control of free form deposition processes, utilizing a training data set that includes simulation, characterization, and inspection data to classify defects and adjust process parameters in real-time, enabling rapid optimization and improvement of process yield and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional additive manufacturing control methods are used, then the process is simpler to implement, but the ability to rapidly optimize and adjust process control parameters in response to changes is insufficient
Solution Approach 1:
The patent implements a closed-loop feedback control system where sensors continuously monitor process parameters and defect conditions, feed this information to a machine learning model, which then adjusts process control parameters in real-time. This feedback mechanism enables rapid adaptation to changing process conditions while maintaining systematic control through the coordinated interaction of monitoring, analysis, and actuation components.
Solution Approach 2:
The machine learning model autonomously analyzes sensor data, identifies defects, and determines optimal process parameter adjustments without requiring continuous human intervention. The system self-corrects process deviations by automatically modifying control parameters based on real-time feedback, enabling the manufacturing process to self-optimize and adapt to changing conditions.
2Manufacturing precision
If real-time adaptive control using machine learning is implemented, then process optimization and quality improvement are enhanced, but the system complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary between sensor data and process control adjustments. It processes complex sensor inputs, identifies defect patterns, and translates these into appropriate control parameter modifications. This intermediary layer manages the complexity by encapsulating the analytical complexity within the ML model while presenting a simplified control interface for parameter adjustment.
Solution Approach 2:
The patent replaces traditional mechanical and empirical control methods with an intelligent software-based machine learning system. Instead of relying on fixed mechanical control mechanisms or human operator judgment, the system uses computational algorithms to analyze process data and determine optimal control actions, substituting physical control complexity with computational intelligence.
3Productivity
If traditional process control is used, then the system is easier to operate, but productivity and process yield are suboptimal
Solution Approach 1:
The machine learning-based control system autonomously monitors process parameters, identifies optimization opportunities, and adjusts control settings without requiring continuous manual intervention. This self-service capability maintains ease of operation by automating complex decision-making processes while significantly improving productivity through rapid, data-driven optimization of manufacturing parameters and defect detection.
4Speed
If rapid adjustment of process parameters is enabled, then responsiveness to process changes improves, but the risk of instability increases
Solution Approach 1:
The closed-loop feedback system continuously monitors process parameters and adjusts them based on real-time feedback from sensors and machine learning analysis. This continuous feedback mechanism enables rapid response to process changes while maintaining stability through systematic, data-driven adjustments that consider the current process state and historical performance patterns.
Solution Approach 2:
The control system dynamically adapts process parameters based on real-time process conditions and defect detection. Rather than using fixed static control settings, the system continuously adjusts parameters to optimize performance while maintaining stability through adaptive control strategies that respond to changing process dynamics and material behavior.
Data Source
Figure 1
Figure 2
Figure 3A~3C
AI summary
Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.