Digital Twin Diagnostics for Additive Manufacturing Component Health

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Solution Overview

Problem

Current additive manufacturing systems face challenges in diagnosing failed builds and identifying performance issues, requiring significant time and human labor, and struggle to efficiently determine the root cause of failures.

Innovation Solution

The implementation of a physics-assisted machine learning model that generates physics features from raw data, uses classifiers to diagnose components, and determines health status, enabling rapid diagnosis and enhanced accuracy by considering wear and tear effects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual diagnosis by experts is used, then diagnostic accuracy can be achieved, but time consumption and human labor requirements increase significantly

Engineering Contradiction:

Improvediagnostic accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual expert diagnosis with an automated machine learning system that analyzes sensor data from additive manufacturing devices. The system uses trained models to automatically identify failures and diagnose issues, eliminating the need for human experts to manually examine and diagnose each build failure, thus reducing time consumption while maintaining diagnostic accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces sensor data as an intermediary between the manufacturing process and diagnostic analysis. Sensors continuously collect data during the additive manufacturing process, providing objective measurements that the machine learning system can analyze. This intermediary data layer enables automated diagnosis without requiring direct human intervention, resolving the contradiction between accuracy and time efficiency.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If manual expert analysis is performed, then root cause identification can be achieved, but the process becomes difficult and time-consuming

Engineering Contradiction:

Improveroot cause identificationVSAvoiddiagnosis time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent performs preliminary data collection and processing during the manufacturing process itself. Sensors continuously monitor and record operational parameters, and the system pre-processes this data into features that are ready for analysis. When a failure occurs, the diagnostic system can immediately analyze the pre-prepared data without requiring time-consuming manual investigation, enabling rapid root cause identification.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual root cause analysis with automated machine learning algorithms that systematically evaluate sensor data to identify failure causes. The system uses trained models to automatically determine root causes based on patterns in the data, eliminating the difficult and time-consuming manual analysis process while maintaining or improving identification accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If traditional diagnostic methods are used, then simplicity of implementation is maintained, but diagnostic speed and efficiency decrease

Engineering Contradiction:

Improvediagnostic speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements a universal diagnostic system that handles multiple types of failures and diagnostic scenarios through a single machine learning framework. The system can diagnose various additive manufacturing device failures using the same underlying technology, making the complexity worthwhile by providing fast, consistent diagnostic speed across different failure modes without requiring separate diagnostic procedures for each case.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11507072B2Systems, and methods for diagnosing an additive manufacturing device using a physics assisted machine learning model

Publication Date: 2022.11.22 GENERAL ELECTRIC CO

AI summary

A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.