Closed-Loop Additive Manufacturing Control for Real-Time Error Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Additive manufacturing processes are prone to errors due to their large design and parameter space, leading to inefficiencies in material usage, energy consumption, and time, as well as the need for human intervention to correct errors.
Innovation Solution
A computer-implemented method using a trained machine learning model to monitor and predict manufacturing parameters in real-time, generating instructions for corrective action when deviations occur, thereby enabling automated error detection and correction during additive manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additive manufacturing processes use large design and parameter space to achieve complex geometries, then manufacturing capability is improved, but error susceptibility increases
Solution Approach 1:
The patent implements a closed-loop control system that continuously monitors manufacturing parameters during additive manufacturing and provides feedback to automatically adjust parameters. This feedback mechanism detects deviations from optimal parameter ranges and triggers corrective actions, thereby maintaining reliability despite the large design and parameter space used for complex geometries
2Manufacturing precision
If manual error assessment and parameter adjustment by experienced operators is used, then error correction accuracy is improved, but labor requirements and process time increase
Solution Approach 1:
The system enables self-service by implementing automated error detection and correction capabilities. The closed-loop control system automatically monitors parameters, identifies deviations, and adjusts manufacturing parameters without requiring human intervention. This eliminates the need for experienced operators to manually assess and correct errors, thereby maintaining correction accuracy while significantly reducing process time and labor requirements
Solution Approach 2:
The patent replaces the mechanical system of manual operator assessment and adjustment with an automated computational system. Machine learning models and algorithms substitute for human expertise in detecting parameter deviations and determining corrective actions, thereby maintaining high correction accuracy while eliminating the time and labor associated with manual intervention
3Reliability
If real-time monitoring and correction of manufacturing parameters is implemented, then manufacturing reliability is improved, but system complexity increases
Solution Approach 1:
The patent implements a multi-functional integrated system where a single closed-loop control platform performs multiple functions: monitoring manufacturing parameters, detecting deviations using machine learning models, determining corrective actions, and executing parameter adjustments. This universal system consolidates what would otherwise require separate specialized systems, thereby improving manufacturing reliability while managing overall system complexity through integration
Data Source
AI summary
Broadly speaking, embodiments of the present techniques provide a method, apparatus and system for automatically detecting and correcting errors in manufacturing parameters of a manufacturing process using closed-loop control. Advantageously, the present techniques not only monitor manufacturing parameters but also provide instructions to enable any unacceptable variation in a manufacturing parameter to be corrected during the manufacturing process.