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

Patent metadata

Field	Value
Publication number	US-12330217-B2
Application number	US-201917599392-A
Country	US
Kind code	B2
Filing date	Mar 29, 2019
Priority date	Mar 29, 2019
Publication date	Jun 17, 2025
Grant date	Jun 17, 2025

Abstract

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.

First claim

The invention claimed is:

1. A method for determining optimal values of significant process parameters in an additive manufacturing (AM) process for printing a part from a specified process material, comprising:
defining a set of target output material properties to be optimized,
identifying an initial set of process parameters pertaining to the AM process,
a screening phase comprising:
executing a first plurality of experimental print runs based on a first experiment design applied to said initial set of process parameters,
obtaining a first output response by measuring the target output material properties for each print run,
based on the first output response, determining, from said initial set of process parameters, a subset of process parameters that affect the target output material properties, and
an optimization phase comprising:

executing a second plurality of experimental print runs based on a second experiment design applied to said subset of process parameters,
obtaining a second output response by measuring the target output material properties for each print run, and
based on the second output response, determining optimal values for each process parameter in said subset of process parameters, for which a maximization or minimization of the target output material properties is predicted,
wherein the first experiment design comprises a two-level Placket Burman design, involving at least a “low” level and a “high” level of each of the parameters of the initial set of process parameters,
wherein the number of first experimental print runs is at least k+1, and
where k is the number of process parameters in the initial set of process parameters.

2. The method according to claim 1 ,
wherein the first experiment design additionally includes a plurality of center point based experimental print runs, involving a “mid” level for at least some of the parameters of the initial set of process parameters.

3. The method according to claim 1 ,
wherein said levels of the initial set of process parameters are determined through analytical modeling.

4. The method according to claim 1 ,
wherein determining the subset of process parameters that affect the target output material properties from the initial set of process parameters comprises performing a regression model fit of the measured output response of the first plurality of experimental print runs in relation to the process parameters of the initial set of process parameters.

5. The method according to claim 1 ,
wherein the second experiment design comprises a central composite design.

6. The method according to claim 5 ,
wherein the central composite design involves three levels of each of said process parameters, defined respectively by a “high” level, a “mid” level and a “low” level,
wherein, in a factorial space of the central composite design:
center points are defined by said “mid” levels of the respective process parameters, and
additional points for estimating curvature are defined at edges of the factorial space.

7. The method according to claim 1 ,
wherein determining the optimal values for each process parameter comprises: performing a Nelder-Mead optimization on a fitted regression model of the measured output response of the second plurality of experimental print runs in relation to the process parameters.

8. The method according to claim 1 , further comprising a validation phase, comprising:
determining a number of replicates required for each target output material property,
based on the determined number of replicates, executing a third plurality of validation print runs using the determined optimal values of said process parameters,
obtaining a third output response by measuring the target output material properties of each print run, and
validating the optimization based on a comparison of the third output response with a defined prediction interval and/or confidence interval.

9. The method according to claim 1 ,
wherein the AM process comprises a laser powder-bed fusion (LPBF) process.

10. The method according to claim 9 ,
wherein the initial process parameters comprise at least one of: a pre-exposure parameter, a post-exposure parameter, a core parameter, and a skin parameter.

11. The method according to claim 9 ,
wherein the set of initial process parameters comprise at least one qualitive parameter.

12. The method according to claim 1 ,
wherein said set of target output material properties to be optimized comprises at least one of: density, hardness, roughness, mechanical yield strength, stiffness and fatigue life.

Assignees

• Siemens Energy Global Gmbh & Co Kg
• Univ Waterloo

Inventors

• Bonakdar Ali
• Liravi Farzad
• Toyserkani Ehsan
• Ali Usman
• Chenouri Shoja'Edin
• Mahmoodkhani Yahya

Classifications

• G05B2219/49023

  3-D printing, layer of powder, add drops of binder in layer, new powder · CPC title

• G05B13/024 Primary

  in which a parameter or coefficient is automatically adjusted to optimise the performance · CPC title

• B22F10/28

  Powder bed fusion, e.g. selective laser melting [SLM] or electron beam melting [EBM] · CPC title

• B22F10/38

  to achieve specific product aspects, e.g. surface smoothness, density, porosity or hollow structures · CPC title

• B22F12/41

  characterised by the type, e.g. laser or electron beam · CPC title