Multi-Laser Powder Bed Fusion Defect Prediction From Spatter CFD

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Solution Overview

Problem

Existing multi-laser additive manufacturing technologies lack a predictive tool for defect formation due to spatter particles causing local variation in powder bed layer thickness, leading to increased susceptibility to defects like lack of fusion.

Innovation Solution

A computational fluid dynamics-based predictive model that simulates spatter particle behavior in the additive manufacturing chamber, predicts spatter landing patterns, and integrates this information into a defect model to estimate defect locations and densities based on part placement, orientation, and scan strategy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If multi-laser additive manufacturing is used to increase production rate and part size, then productivity is improved, but manufacturing precision deteriorates due to spatter-induced local thickness variation

Engineering Contradiction:

Improverate of productionVSAvoidlayer thickness uniformity

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system performs preliminary computational fluid dynamics modeling and spatter particle tracking simulations before actual manufacturing to predict spatter landing patterns and identify high-risk defect zones. This advance planning allows optimization of build parameters and laser strategies to mitigate thickness variation before production begins

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a virtual digital twin model that replicates the physical manufacturing chamber and simulates spatter behavior. This digital copy allows repeated testing and optimization of manufacturing parameters without affecting actual production quality or requiring physical prototypes

Inventive Principle:

Principle #26Copying

2Productivity

If multi-laser additive manufacturing is used to increase production rate, then productivity is improved, but reliability deteriorates due to increased defect formation

Engineering Contradiction:

Improverate of productionVSAvoiddefect-free production

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements a feedback loop where CFD simulation results and predicted spatter patterns continuously inform and adjust manufacturing parameters. The defect risk maps generated from simulations provide feedback that guides real-time process optimization to maintain high reliability in multi-laser operations

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system applies preliminary countermeasures by identifying high-risk defect zones through simulation before manufacturing begins. Build strategies, laser parameters, and chamber conditions are pre-adjusted in these identified risk zones to prevent defect formation before it occurs during actual production

Inventive Principle:

Principle #9Preliminary anti-action

3Manufacturing precision

If iterative trial and error process is used to optimize part quality, then manufacturing precision is improved, but loss of time increases

Engineering Contradiction:

Improvepart qualityVSAvoidoptimization time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs all necessary quality optimization simulations and defect risk assessments before actual manufacturing begins. By predicting spatter patterns and identifying defect risks in advance, the system eliminates the need for iterative trial-and-error testing during production, saving significant time

Inventive Principle:

Principle #10Preliminary action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables accurate prediction of defect locations and densities, optimizing part design and reducing the need for empirical prototyping, thereby enhancing the quality and efficiency of multi-laser additive manufacturing processes.

Implementation Method 1

execute computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber

Methodology Applied

Scientific EffectComputational fluid dynamics:

Implementation Method 2

execute computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber

Methodology Applied

Scientific EffectGas flow:

Implementation Method 3

irradiated by the laser

Methodology Applied

Scientific EffectLaser irradiation: Laser

Implementation Method 4

partial melting when irradiated by the laser

Methodology Applied

Scientific EffectMelting: Melting

Data Source

PatentUS12485489B2Uncertainty quantification or predictive defect model for multi-laser powder bed fusion additive manufacturing

Publication Date: 2025.12.02 RTX CORP

AI summary

A process for uncertainty quantification for a predictive defect model for multi-laser additive manufacturing of a part including executing computational fluid dynamics modeling of a gas flow in an additive manufacturing machine manufacturing chamber; assigning a spatter particle size, velocity and direction relative to a melt pool on a powder bed disposed on a build plate within the manufacturing chamber; executing computational fluid dynamics post processing for spatter particle tracking; predicting a spatter particle landing pattern; feeding the spatter particle landing pattern prediction into a defect model; producing a layer thickness map, the layer thickness map configured to demonstrate a location of locally thicker layers on the part; and predicting defect location and density to accumulate lack-of-fusion risk as a function of part placement, orientation, and scan strategy.