Metal Printing Height Maps for Porosity and Shape Control

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Solution Overview

Problem

Challenges in controlling the shape and porosity of end objects in metal printing processes, particularly due to agent application and metal powder fusion, limit control over sintering and fusion, leading to potential deformation.

Innovation Solution

Utilization of machine learning models, specifically deep learning and neural networks, to predict and compensate for end object shape by predicting height maps, porosity, and adjusting manufacturing parameters to reduce deformation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If agent application and metal powder fusion are used in additive manufacturing, then manufacturing capability is improved, but control over end object shape and porosity deteriorates

Engineering Contradiction:

Improvemanufacturing capabilityVSAvoidcontrol over end object shape and porosity

Core Design Contradiction:

Ease of manufactureVSManufacturing precision

Solution Approach 1:

The system performs preliminary prediction of porosity distribution and object deformation before the sintering process using machine learning models. Based on these predictions, compensation parameters are calculated in advance to adjust the digital model, thereby preemptively counteracting the loss of control during agent application and metal powder fusion.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements a feedback loop where actual sintering results are measured and compared with predicted outcomes. This feedback information is used to continuously refine and update the machine learning models, improving the accuracy of porosity prediction and deformation compensation for subsequent manufacturing cycles.

Inventive Principle:

Principle #23Feedback

2Manufacturing precision

If machine learning models are used to predict and compensate for deformation, then manufacturing precision is improved, but device complexity increases

Engineering Contradiction:

Improvecontrol over end object shapeVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The system introduces machine learning models as intermediary components between the digital model and the physical manufacturing process. These models act as virtual proxies that predict porosity and deformation, enabling precise control without requiring complex hardware modifications to the additive manufacturing equipment itself.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces complex mechanical control mechanisms with computational approaches. Instead of using sophisticated hardware to physically control porosity and shape during sintering, the system uses machine learning algorithms to predict outcomes and compensate through digital model adjustments, substituting mechanical complexity with computational processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12459041B2Porosity prediction

Publication Date: 2025.11.04 PERIDOT PRINT LLC

AI summary

Examples of methods for predicting porosity are described herein. In some examples, a method includes predicting a height map. In some examples, the height map is of material for metal printing. In some examples, the method includes predicting a porosity of a precursor object. In some examples, predicting the porosity of the precursor object is based on the predicted height map.