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Machine learning approach for fatigue life prediction of additive manufactured components

A fatigue life, additive manufacturing technology, applied in machine learning, nuclear methods, additive manufacturing, etc., can solve the problems of not describing different characteristics, difficult to develop, define and calibrate mathematical models, etc., to optimize product design and achieve good product quality. Effect

Pending Publication Date: 2021-12-07
SIEMENS IND SOFTWARE NV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] A problem with fatigue prediction for additive manufacturing is that the fatigue life of any experimental part is always caused by the combined influence of multiple parameters that affect the fatigue life
The second problem is that due to the large number of parameters and interactions that occur, it is difficult to develop, define and calibrate mathematical models that describe how these different parameters interact
However, the method does not describe how the different properties of each segment should be calculated

Method used

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  • Machine learning approach for fatigue life prediction of additive manufactured components
  • Machine learning approach for fatigue life prediction of additive manufactured components

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Embodiment Construction

[0022] The invention starts with test data from experimental tests or numerical simulations provided by the user in the first block "calibration" (step 1a "experimental test data" and step 1b "numerical simulation data"). The test data from step 1a, step 1b is fed into machine learning (step 2 "training machine learning model"). In an advantageous embodiment, a Gaussian process regression with a squared exponential covariance function is used. Train the machine learning algorithm in step 2. In parallel, the end user defines the use case (second block "Use Case Definition"), defines its components in step 3 "Complex User Components", and defines the parameters corresponding to the different parameters in step 4 "User Definition of Zones and Associated Parameters" The different regions of (eg, sections machined for better surface roughness, sections containing increased porosity...).

[0023] The use case from the use case definition achieved through steps 3 and 4 is fed to a ...

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Abstract

The invention relates to a method and system for fatigue life prediction of additive manufactured components accounting for localized material properties. The method and the system are employed for prediction of fatigue life properties of an additive manufactured element, with a data collection step (1a, 1b) in which a plurality of data points for maximum stress vs. cycles to failure for different given processing steps of the element are collected, with a training step (2) in which a machine learning system is trained with the collected data, and with an evaluation step (5, 6) in which the trained machine learning system is confronted with actual processing steps and used to predict the fatigue life properties of the element.

Description

technical field [0001] The invention relates to a method and a system for fatigue life prediction of additively manufactured components taking into account local material properties. Background technique [0002] The fatigue properties of an additively manufactured part (how long a part is subjected to a certain repetitive load) strongly depends on the exact way the part is printed and post-processed. figure 1 The distribution of fatigue behavior of additively manufactured titanium alloy samples obtained from an extensive literature review is shown. figure 1 The maximum stress versus fatigue failure cycle is shown. [0003] The spread is obtained based on how the part is printed and post-processed. Printing and post-processing affect both pure material properties and the occurrence of artifacts such as roughness or the presence of porosity. In order to accurately predict the fatigue behavior of components, it is therefore necessary to treat the fatigue life in terms of ma...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B22F3/105B29C64/393G05B23/02
CPCG05B2219/49023G05B23/024G05B23/0283B33Y50/00B29C64/386B29K2995/0086B22F10/20Y02P10/25G06N20/10B22F10/80G06N20/00G05B13/027G05B19/4099G05B23/0243G05B23/0254G05B2219/49007
Inventor 尼古拉斯·拉门斯
Owner SIEMENS IND SOFTWARE NV