Additive Manufacturing Parameter Screening for Material Property Optimization

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Solution Overview

Problem

Existing methods for optimizing process parameters in additive manufacturing (AM) are limited by their ability to analyze a small number of parameters, failing to systematically identify the most significant ones that affect the material properties of printed parts.

Innovation Solution

A statistical and experimental methodology is developed, comprising a screening phase to identify significant process parameters and an optimization phase to determine optimal values for these parameters, using experimental designs such as Plackett-Burman and central composite designs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a random parameter approach with various parameter values is used to optimize AM process parameters, then the effect of each parameter can be determined through parametric study, but the number of process parameters that can be analyzed is limited to a small number due to cost and time constraints

Engineering Contradiction:

Improveparameter effect analysisVSAvoidnumber of parameters analyzed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The optimization process is divided into two distinct phases: a screening phase that identifies significant parameters from a large set, and an optimization phase that fine-tunes these identified parameters. This segmentation allows efficient handling of many parameters by first filtering them down to a manageable subset for detailed analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The method extracts and identifies a subset of significant process parameters from the complete set of AM process parameters through statistical analysis in the screening phase. This extraction allows the system to focus computational and experimental resources only on the most influential parameters, thereby analyzing more parameters overall within cost and time constraints.

Inventive Principle:

Principle #2Taking out (Extraction)

2Manufacturing precision

If statistical design with response surface is employed to find optimal parameters, then optimization can be achieved, but only a small number of process parameters can be optimized due to cost and time constraints

Engineering Contradiction:

Improveoptimal parameter determinationVSAvoidnumber of parameters optimized

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The optimization methodology is segmented into two stages: screening to identify significant parameters, and optimization to determine their optimal values. This allows the statistical design and response surface methods to be applied only to the subset of significant parameters, enabling optimization of more parameters overall while maintaining manufacturing precision within resource constraints.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The screening phase performs preliminary identification of significant parameters before the optimization phase begins. This preliminary action filters out insignificant parameters, allowing the subsequent optimization process to focus computational and experimental resources on only the relevant parameters, thereby increasing the total number of parameters that can be optimized within cost and time limits.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If a large number of process parameters are analyzed in AM processes, then comprehensive optimization can be achieved, but the complexity of identifying significant parameters increases

Engineering Contradiction:

Improveparameter analysis comprehensivenessVSAvoidparameter identification complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The method extracts the subset of significant parameters from the complete parameter set through statistical analysis in the screening phase. This extraction reduces the complexity of parameter identification by automatically filtering out insignificant parameters, allowing comprehensive analysis of many parameters while maintaining manageable identification complexity through systematic statistical methods.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The screening phase provides feedback by identifying which parameters have significant effects on output responses. This feedback mechanism guides the optimization phase to focus only on the identified significant parameters, reducing the complexity of the overall process while maintaining comprehensive analysis capability across all initial parameters.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12330217B2Method and system for optimizing process parameters in an additive manufacturing process

Publication Date: 2025.06.17 SIEMENS ENERGY GLOBAL GMBH & CO KG

AI summary

A method is provided for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material. The method involves defining a set of target output material properties to be optimized and identifying an initial set of process parameters pertaining to the AM process. The method broadly comprises a screening phase and an optimization phase. The screening phase involves generating and executing a first experiment design, and determining, based on a first output response, a subset of significant process parameters that affect the target output material properties. The optimization phase involves generating and executing a second experiment design, and determining, based on a second output response, optimal values for the significant process parameters that maximize or minimize the target output material properties.