Patent Description:
This background description may contain ideas pertinent to the invention which reveal problems of systems or processes, only some parts of which may be fully disclosed to the public, if any. The disclosure should therefore not be interpreted as evidence that the aspects mentioned herein are part of the public disclosure or prior art.

The ASTM defines fatigue strength as
"the value of stress at which failure occurs after a defined number of load cycles".

The ASTM defines fatigue limit as
"the limiting value of stress at which failure occurs as the number of cycles becomes very large".

In reverse this means that a fatigue limit is the stress level below which an infinite number of loading cycles can be applied to a material without causing fatigue failure. Where materials do not have a distinct limit the term fatigue strength or endurance strength is used and is defined as the maximum value of completely reversed bending stress that a material can withstand for a specified number of cycles without a fatigue failure.

It is known that properties such as surface roughness, porosities, heat treatments, and others can affect how the material reacts to a fatigue load.

From CN <NUM><NUM><NUM> A a method is known to determine dimensions and loads of a flat test piece for a material test. From CN <NUM><NUM><NUM> A a method is known to determine the impact of a defect on fatigue life. Both publications consider the material to be a deterministic given property.

Document <CIT> discloses a method to improve additive manufacturing using the trained algorithm to classify defects.

Document <CIT> shows a method to predict defects in an on-line monitoring fashion.

Document <CIT>] relates to a method and for fatigue life prediction of additive manufactured components accounting for localized material properties. To train a machine learning system with the collected data comprising data point relating to maximum stress vs. cycles to failure for different given processing steps, surface and volume conditions of an element, specimen or material structure. To collect training data for a machine learning system many tests need to be performed.

The machine learning method proposed in <CIT> relies on training a machine learning system respectively a covariance function which determines how correlated two individual test samples are. A simplified interpretation of such a covariance function is to regard it as a "distance" metric containing all the different influencing parameters (for example the surface roughness or the print orientation in an additive manufacturing component). When two samples are "close" together, their results are equally tied together, while the opposite holds for samples "far away" from each other.

In general, such covariance functions (many different functions exist) are solely driven by the input parameters (surface roughness, build orientation. ) and are basically not affected by the output (the actual fatigue performance of the sample).

While characterization of fatigue performance is usually performed through elementary coupon testing, it is often difficult (if not impossible) to reproduce the exact conditions present in the component on these elementary coupons. This limitation can be both attributed to production limitations (for example certain angles cannot be printed in additive manufacturing without additional supports) as well as due to different material responses (for example due to different thermal behavior of smaller coupons compared to larger components).

As a result, an end-user is often faced with the challenge of selecting which coupons to manufacture given that none of them will perfectly match the conditions in the component. There are different approaches that may conventionally be used to cope with such problem:.

The issue with fatigue characterization of additive manufacturing materials has been recognized in the past by the American Society for Testing and Materials [ASTM]. ASTM spent significant effort in trying to define new standards and methods adapted to this methodology. Printing ASTM compliant geometries with additive manufacturing involves significant costs the invention aims to reduce. Another focus of the invention is ensuring that proper material conditions are achieved.

It is one objective of the invention to assist an end-user in determining which are the optimal tests to be performed in order to obtain the most relevant data for fatigue prediction of a specific component.

For the purpose of describing the invention fatigue behavior or fatigue may be characterized as the aspect of how long a component can withstand a certain cyclic load without failure.

The object of the invention is achieved by the independent claim. The dependent claims describe advantageous developments and modifications of the invention.

The invention solves the objective proposing a method of the incipiently defined type comprising the additional steps:.

Based on the prior art described above and the problems associated, the invention is based on a finite element driven procedure to assist an end-user in determining which are the optimal tests to be performed in order to obtain the most relevant data for fatigue prediction of a specific component. Many advanced materials (for example composites or additive manufactured parts) exhibit a complex fatigue behavior. The final performance of such materials and their derived components is highly dependent on the specific material parameters (which may - for example - be surface condition or presence of defects) and the respective manufacturing process (curing cycle for composites, printing parameters for additive manufacturing). The complexity of component performance assessment is further increased by the spatial variability of these different parameters (which may - for example - be surface roughness in additive manufacturing is dependent on the orientation of a surface of the component within the build space of an additive manufacturing machine which may be a printer). While characterization of fatigue performance is usually performed through elementary coupon testing, it may be difficult or even impossible to reproduce the exact conditions present in the component on these elementary coupons. This limitation can be both attributed to production limitations (for example certain angles cannot be printed by additive manufacturing without additional supports) as well as due to different material response (for example due to different thermal behavior of smaller coupons vs larger components). As a result, conventionally, an end-user is often faced with the challenge of selecting which coupons to manufacture given that none of them will perfectly match the conditions in the component. The invention proposes a method where machine learning models are combined with finite element analysis and a description of manufacturing limitations, to automatically determine which are beneficial or ideal coupons to manufacture and test to achieve improved or even maximum accuracy in fatigue prediction of the component.

This invention proposes to use a machine learning model, which may also be understood as a trained covariance functions, to calculate how "close" or "far away" a potential new test coupon is from the critical condition in the component. According to the invention this distance metric is independent of the actual sample performance, the selection of optimal test coupons can be performed purely computer implemented, respectively numerically without requiring testing. After classification and identification of the optimal coupons, actual testing may be performed to enrich the material model training data and reduce the uncertainty on the predictions near the critical region of the component.

Prior art in testing relies on either experience (no guarantee for success) or sample cutting from the actual component (high cost, not always feasible). The procedure according to the invention allows for a repeatable and operatorindependent selection of the best test campaign and can account for multiple objective (for example in the example described above, focus is put on the critical region, but an identical methodology could be used to focus on overall accuracy of the material model). The key part in this invention is using the machine learning model (and its covariance function) to identify which samples are most correlated to the condition of interest.

The concept of identifying the impact of a hypothetical new training point on machine learning predictions is a well-known area in AI/machine learning.

Test coupons usually have a predetermined geometry (so they fit in the test machine and/or according to some standard) and are normally printed in a near-net shape. Those coupons are tested in a universal testing machine which applies an unidirectional load along the length of the coupon.

A stress related parameter may be any parameter related to the stress conditions determined in the strength analysis. The stress related parameter may be an "equivalent stress" value which may include effects such as residual stress (which may essentially shift the "mean stress" value). As such, residual stress may not be handled as a separate parameter but may be indirectly included in the stress parameter. According to a preferred embodiment said stress related parameter may be an equivalent stress being determined from the mean stress or residual stress and the variable stresses. Such calculations are known from conventional durability solvers.

According to a preferred embodiment the temperature history of the component may be a material condition. The material model - in such case - may comprise a relation that predicts how the fatigue life changes with different temperature histories.

A component design may be provided preferably in CAD-file from a computer engineering platform or database.

A strength analysis of the component may be a finite element analysis performed on a computer with an engineering software.

These steps may be sequenced automatically or with at least one manual input possibility in-between to decide for one or several options to proceed.

According to the invention, said material model is a machine learning fatigue model, wherein said machine learning model is provided by a method comprising the steps of:.

This kind of model is known from document <CIT> including training options.

Another preferred embodiment provides that said component is at least in the critical area additive manufactured.

Still another preferred embodiment provides that said component is made at least in the critical area of composite material.

In particular for these both material types the method according to the invention is most suitable since conventional methods of such kind aren't either sufficiently accurate or involve to many tests, wherein the invention reduces the test number due to the application of the material model.

Another preferred embodiment provides that in case the component is at least in the critical area additive manufactured said at least one material condition is at least one of:.

Another preferred embodiment provides that in case the component is at least in the critical area of composite material said at least one material condition is at least one of:.

Another preferred embodiment provides that the method according to the invention may comprise an additional step before step (d) of providing at least one material condition, the additional step comprising:.

This embodiment enables in the simulation option to first make the test coupon and test if the requirements are met before manufacturing the component. In general, this feature enables to accurately consider manufacturing process parameters and options in the test coupon selection.

To enable a full consideration of load spectrums without excessive analysis during the strength analysis the method may comprise an additional step before step.

The invention may be implemented as a system adapted for carrying out a method according to any of the claims, the system comprising at least one processing unit being adapted to perform computer-implemented steps (a), (c), (g). According to another preferred embodiment the system comprises at least one processing unit which may be adapted to also perform computer-implemented steps (b) and/or (d) and/or (e) and/or (f).

Embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings, of which:.

The illustration in the drawings is in schematic form. It is noted that in different figures, similar or identical elements may be provided with the same reference signs.

Although the present invention is described in detail with reference to the preferred embodiment, it is to be understood that the present invention is not limited by the disclosed examples, and that numerous additional modifications and variations could be made thereto by a person skilled in the art without departing from the scope of the invention.

It should be noted that the use of "a" or "an" throughout this application does not exclude a plurality, and "comprising" does not exclude other steps or elements. Also, elements described in association with different embodiments may be combined. It should also be noted that reference signs in the claims should not be construed as limiting the scope of the claims.

<FIG> shows a simplified schematic illustration of a system SYS for predicting fatigue life of a component CMP by applying the method according to the invention.

Starting with a component's CMP design CDS being put under a load condition LDC the strength analysis FEM of the component CMP is performed. During the strength analysis SVM the critical area CRA and at least one stress-related parameter SRP are determined. Together with at least one material condition MCD these results are provided to a material model MTM. Said at least one material condition MCD may be at least one of:.

An analysis of these parameters provided by the material model MTM (preferably by calculation of the covariance) may generate a sorted list of coupons wherein the coupon with the highest correlation is the one to be tested. The material model MTM generates as an output a test coupon COP specification TCS for being tested in a testing machine TST. The test coupon COP specification TCS may comprise the full geometric specification, preferably a complete material specification and preferably a complete manufacturing process plan.

<FIG> shows a schematical flow diagram of a method according to the invention.

As a preparation step (a0) the method according to the invention may comprise an additional step before step (a) by determining a load condition LDC for said component CMP, which may be done by:.

Subsequently the following steps are performed:.

Optionally the method may comprise an additional step (d0) (illustrated with the dotted line frame) before below step (d) of providing at least one material condition MCD, the additional step (d0) comprising:.

Subsequent step (d) comprises providing at least one material condition MCD of said component CMP at least for said critical area of the component CMP.

According to the invention a material model MTM is (e) provided receiving as an input [step (f)]:.

In step (g) said material model MTM generates as an output a test coupon COP specification TCS for being tested in a testing machine TST. As explained below the material model MTM may have been trained to select a test coupon specification TCS from said set of test coupon specifications SCS when receiving as an input:.

An additional subsequent step as illustrated in <FIG> is the generation of the test coupon COP and conduction of the coupon test.

Said material model MTM may be a machine learning fatigue model MLS. The machine learning model may be provided by a method comprising the steps [(e)] of:.

Claim 1:
A method for generating a test coupon (COP) specification (TCS) for predicting fatigue life of a component (CMP), comprising:
(a) determining a load condition (LDC) for said component (CMP),
(b) providing a component (CMP) design (CDS),
(c) performing a strength analysis (FEM) of the component (CMP) design under said load condition (LDC) determining:
(i) a critical area (CRA) of the component (CMP),
(ii) at least one stress-related parameter (SRP) of said critical area (CRA),
(d) providing at least one material condition (MCD) of said component (CMP) at least for said critical area of the component (CMP),
characterized by the additional steps:
(e) providing a material model (MTM),
(f) providing as an input to said material model (MTM):
(i) said at least one stress-related parameter (SRP) and
(ii) said at least one material condition (MCD),
(g) said material model (MTM) generating as an output said test coupon (COP) specification (TCS) for being tested in a testing machine (TST),
wherein said material model (MTM) is a machine learning fatigue model (MLS),
wherein said machine learning model is provided by a method comprising the steps of:
(i) Providing a set of test coupon specifications (SCS) to said material model (MTM), wherein said test coupon specifications (SCS) respectively comprise different of said at least one material condition (MCD) of said coupon specifications (SCS),
(ii) collecting data points (DPT) for maximum stress vs. cycles to failure for said test coupon specifications (SCS),
(iii) training said material model (MTM) with the collected data to select a test coupon specification (TCS) from said set of test coupon specifications (SCS) when receiving as an input:
(i) said at least one stress-related parameter (SRP) and
(ii) said at least one material condition (MCD).