Additive Manufacturing Reconfiguration via AI Layer Defect Feedback

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current additive manufacturing processes lack real-time monitoring capabilities, leading to inefficiencies and defects, particularly in constructing complex shapes and high-precision objects.

Innovation Solution

A method for real-time monitoring and reconfiguration of additive manufacturing processes, utilizing AI systems to inspect each layer of the object being constructed, detect anomalies, and provide reconfiguration recommendations to adjust parameters and ensure desired operating conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If real-time monitoring is implemented in additive manufacturing, then manufacturing precision and defect detection improve, but device complexity increases

Engineering Contradiction:

Improveobject construction precisionVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent implements real-time monitoring that captures images of each layer during construction, compares them against the 3D model, and provides feedback control to detect and correct deviations as they occur, thereby improving manufacturing precision through continuous feedback loops

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces an intermediary monitoring system that acts as a mediator between the additive manufacturing process and the final object quality, using image capture devices and comparison algorithms to bridge the gap between construction process and precision outcomes

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If real-time monitoring and reconfiguration are implemented, then productivity improves through defect reduction, but loss of time increases due to monitoring overhead

Engineering Contradiction:

Improvemanufacturing productivityVSAvoidmonitoring time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The monitoring process operates continuously during the additive manufacturing construction without interrupting the layer-by-layer building process, capturing images and performing comparisons in real-time as each layer is deposited, thereby maintaining continuous productive action

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system performs preliminary comparison of each layer against the 3D model immediately after deposition, detecting potential defects early before they propagate to subsequent layers, allowing for preventive reconfiguration that saves time overall

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If AI-based anomaly detection is used, then manufacturing precision improves, but use of energy increases

Engineering Contradiction:

Improvedefect detection accuracyVSAvoidenergy consumption

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The patent replaces complex physical measurement systems with optical imaging and computational analysis, using cameras and image processing algorithms to detect anomalies instead of mechanical sensors, thereby reducing energy consumption while maintaining detection accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12282316B2Real-time reconfiguration of additive manufacturing

Publication Date: 2025.04.22 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method for additive manufacturing includes identifying a discrepancy between a three-dimensional model and an object model. The three-dimensional model is a model of a three-dimensional object that is being constructed by an additive manufacturing process, and the three-dimensional object is being constructed based on the object model. The method further includes determining a reconfiguration recommendation based on the identified discrepancy. The method further includes reconfiguring the additive manufacturing process based on the reconfiguration recommendation.