Additive Manufacturing Digital Twin for Real-Time Quality Control
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Solution Overview
Problem
Current methods for constructing digital twins in additive manufacturing struggle to accurately regulate and control the quality of metal parts due to limitations in processing lifecycle data, which hinders real-time monitoring and simulation capabilities, leading to increased manufacturing costs and inefficiencies.
Innovation Solution
A digital twin system with lifecycle data management is constructed, incorporating a multi-source signal fusion module, defect prediction module based on deep learning, and multi-physics simulation, enabling real-time data acquisition, processing, and feedback loops to accurately characterize and control the molding quality of additive manufactured parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline lifecycle data processing is performed to characterize final properties of additive manufactured parts, then comprehensive quality characterization is achieved, but manufacturing cost increases and real-time processing requirements cannot be met
Solution Approach 1:
The patent performs preliminary actions by establishing mapping relationships between process parameters and quality characteristics before actual manufacturing. A digital twin model is pre-trained with lifecycle data to enable real-time quality prediction during manufacturing, avoiding the need for costly offline processing of every part while maintaining comprehensive quality characterization capability.
Solution Approach 2:
The patent creates a digital twin (virtual copy) of the additive manufacturing process that replicates the physical process and its quality characteristics. This digital copy processes lifecycle data in advance to learn quality patterns, then uses these learned patterns for real-time quality assessment during actual manufacturing, separating the computationally intensive processing from the real-time requirement.
2Device complexity
If single-source monitoring data is used for digital twin construction, then system complexity is reduced, but molding quality characterization accuracy is insufficient
Solution Approach 1:
The patent merges multiple data sources including process parameters, monitoring data from sensors, material information, and quality inspection results into a unified digital twin model. This multi-source data fusion comprehensively characterizes molding quality by integrating information from throughout the manufacturing lifecycle, achieving accurate quality assessment while managing system complexity through standardized data integration frameworks.
3Reliability
If experimental methods are used to establish mapping relationships between process parameters and defects, then defect characterization is achieved, but the root causes of certain defects remain unrevealed
Solution Approach 1:
The patent introduces a digital twin as an intermediary between physical process parameters and observed defects. The digital twin model simulates the underlying physical and chemical processes during additive manufacturing, acting as a mediator that reveals root causes of defects by modeling mechanisms such as melt pool dynamics, phase transformations, and stress evolution that are not directly observable in physical experiments alone.
Data Source
AI summary
The present invention relates to a method for constructing a digital twin system for an additive manufacturing process with lifecycle data management. The digital twin system for an additive manufacturing process constructed by the method includes a data acquisition and supervisory control system, a data management and storage system, a manufacturing executing system, a configuration model, a simulation system, and a multi-physics simulation system. The present invention can fully and accurately characterize the molding quality of parts, thereby saving a lot of time required for offline data processing, and achieving real-time acquisition and updating of data.