Patent Description:
Modern production techniques like additive manufacturing (e.g. 3D-printing) and assembling of (possibly many) components by robots usually require a simulation of the manufacturing process before the process is actually performed. The aim of such a simulation is to verify that the manufacturing process is actually feasible in reality. For example, in 3D-printing a major problem is the printability of beam structures which can be impaired by deformation effects due to gravity.

Available tools for such simulation involve for example finite element methods (FEM). The physical properties analyzed in such simulations usually reflect the reaction of the product to the application of some force or torque. Examples include elasticity, stiffness, shearing resistance, bending stiffness and printability in case of 3D-printing.

However, even on modern computers, state-of-the art simulation techniques like FEM require significant run-times, often in the order of several hours, which makes it very expensive. Also, as the results of such simulations are often used during a design process to verify a current design, the whole process is delayed and less responsive. The number of designs to be tried by a designer may be limited due to long simulation times.

Therefore, it is the objective of the present invention to provide a method that significantly speeds up the simulation process of modern manufacturing techniques.

<CIT> concerns a method of additively manufacturing a highly customized product tailored to a particular individual.

<NPL> concerns a machine learning-based design approach for hierarchical materials. The designs created by the machine learning model, which is trained with a database of hundreds of thousands of geometries from finite element analysis, are validated using additive manufacturing and experimentation.

<NPL> concerns additively manufactured structures that can be tailor-made to optimally distribute mechanical loads while remaining light-weight. To analyze the locally unique mechanical behavior of structures made from a large number of small lattice cells, a strategy which employs neural networks and deep learning to predict the maximum stresses in the realm of linear elasto-plasticity of a detail-level finite-element model is presented. The strategy is demonstrated on a single lattice cell specimen. Good agreements between experimental, finite element and neural network results are found at a significant reduction in computation time.

This objective is met by the appended claims.

The method according to the invention minimizes the need for costly and lengthy simulations to determine a physical property of a region of a sports article by estimating the physical property. One aspect that can be assessed by this technique is the producibility of a product. A good example is the producibility of a machined part on a milling machine. The producibility of the part depends on its complexity and if the milling machine offers the number and types of axis required. In the case of additive manufacturing, producibility effectively means printability. If a 3D geometry is printable on a certain printer depends on the technology the printer uses for creating the geometry. One key issue in 3D printing is the capability to print overhanging geometries. With a technology like selective laser sintering, where the part is printed within a powder, overhanging parts of geometry pose no challenge. Using other printing technologies like Fused Deposition Modeling or Stereolithography, the part is cured in free air or submerged within a liquid. In both cases, overhangs are only possible until a certain angle. After that, the geometry will deform or collapse. For parts exceeding this angle, either support structures have to be added or printing is no longer possible. There are a variety of parameters that vary with the print geometry and printer technology that all affect the producibility/printability of a given structure. Examples of other parameters that influence printability include the thickness of structures, print speed, resolution / detail and drainage (of powder, resin, etc.).

To characterize the region according to the method of the invention, a plurality of structural features within the region is determined. These features correspond to the material structure in the region and could for example describe the distribution of material in the region. A value is determined for each of those structural features within the region. Thus, the values of the features are characteristic for a particular region. As different regions typically have a different distribution of material, their corresponding structural feature values are different.

In the next step a mapping is used to estimate a value of the physical property. The mapping is based on a plurality of samples which had been obtained for example by a costly simulation that had been conducted before. For example, a FEM simulation might have been used to generate the samples. The samples associate, i.e. map, a particular feature value to the result of such a FEM simulation, for example printability in the 3D-printing example mentioned above. The samples are then used firstly to obtain the mapping using for example a machine learning algorithm. Then a small number of remaining samples may be used to test the mapping.

In the final step, instead of using for example a costly FEM simulation, the mapping is used to estimate the physical property. This reduces the computation time significantly. Thus, the FEM simulation is run once for a particular sports article, but its results can be reused for different sports articles having a different structure.

The region may be divided into sub-regions. These sub-regions may correspond to neighborhoods of certain structural features. For example, in a midsole, the structural features may be beams and for a shoe upper they may be patches.

The region may be bounded by a hexahedron. A hexahedron is any polyhedron, i.e. three-dimensional geometrical shape, with six faces. It can be used in the three-dimensional case to subdivide a region in a regular or irregular manner.

The region may be bounded by a quadrilateral. A quadrilateral is a polygon, i.e. a two-dimensional geometrical shape, with four edges. It can be used in the two-dimensional case to subdivide a region in a regular or irregular manner.

The structural features may be associated with the sub-regions. A feature may be associated with each sub-region. In this way, a feature vector is obtained which is suitable to be fed for example into a neural network. Thus, the feature values associated with the sub-regions yield a "fingerprint" of a particular region.

The sub-regions may be voxels or pixels which subdivide the region. Voxels refer to a three-dimensional volume, whereas pixels refer to a two-dimensional area. For example, in case of a midsole to be 3D-printed, voxels may be used to subdivide the regions of the midsole. In case of an upper on which patches are to be placed, pixels may be used to subdivide regions of the upper which in this case is regarded as a two-dimensional manifold.

Each sub-region may be associated with a feature value for a structural feature within the sub-region. The value associated with the sub-region may depend on the amount of material present in the sub-region. In an example, the feature value for a particular sub-region may be a binary "<NUM>" if material is present in this sub-region, otherwise, it may be a binary "<NUM>". Thresholding may be used in case of partial filling. For example, if there is <NUM>% or more material present in a sub-region, the feature value may be a "<NUM>", otherwise "<NUM>". In this way, a feature vector is obtained that corresponds to the structure of the region. In the example above, regions with different structures are associated with different binary vectors.

The information contained in each sub-region (e.g. a pixel or voxel) can be binary as described previously. However, the information in each sub-region may also be on a greyscale system to represent different amounts of material for example. Additionally, a third system entirely could be used in which a lot more detail is encoded into each sub-region. This would be particularly useful for lattices where the beams may be made of different materials or the same materials with different properties (e.g. caused by different curing), and also for patch arrangements on a lattice, where different materials, different orientations and different built up layers could all be encoded into the voxel value. Therefore, the feature value associated with the sub-region may depend on the type of material present in the sub-region. Alternatively, the feature value associated with the sub-region may depend on the properties of the material present in the sub-region.

The greyscale system and the third system would both give a lot more detail to the information encoded, but the run times for these encoding systems could be longer. When choosing what system would be most appropriate, the run times and the amount of information required must be balanced.

The mapping may be based on a machine learning algorithm that was trained using the samples. Machine learning comprises a vast spectrum of different algorithms which aim to identify and exploit structures and patterns in data for inference and prediction. Advantageously, some algorithms are able to identify non-linear relationships between features and targets.

The mapping may be based on an artificial neural network that was trained using the samples. Artificial neural networks comprise a number of neurons usually organized in layers. Each neuron in a particular layer receives a weighted sum of the output of the cells of the previous layer. This sum is subject to a so-called activation function which usually is a non-linear function. The result of this function is the output of the neuron and fed to the next layer. By stacking layers, the network is able to learn abstractions of the input data which might result in very precise predictions. Also, due to non-linear activation functions, a neural network is able to identify non-linear relationships between features and targets.

The artificial neural network may be a convolutional neural network, CNN. Convolutional layers in a CNN advantageously reduce the size of the features (e.g. number of pixels or voxels) by applying a convolution or cross correlation operation to its input. Thus, the number of free parameters, i.e. the weights of the network, is significantly reduced resulting in reduces processing time. Also, such a network is less prone to overfitting, i.e. learning "noise" in the data.

The association between feature values with a value that is representative of the physical property may be obtained for each sample using a finite element method, FEM. Generally, FEM is able to provide rather precise estimates of physical properties and is a well-established method.

The sports article may be a shoe and the region may be located in the midsole of the shoe. The method according to the invention is advantageously suitable to predict physical properties such as the printability of a midsole by means of a 3D-printing process. Thus, instead of using costly FEM simulations, the method according to the invention is able to predict the printability of a particular midsole design within seconds instead of hours. This speeds up the design process and allows a designer to try more design options. Moreover, he is getting feedback about the feasibility of his design more quickly.

The method may further comprise the step of subdividing the region into voxels, wherein at least one structural feature is associated with each voxel. Generally, a voxel represents a value on a grid in three-dimensional space. The value is characteristic of the structural feature.

The physical property may be one of printability, elasticity, stiffness and shearing resistance of a structural feature within the region. As mentioned, usually a full finite element analysis (FEA) based on FEM is conducted to explore whether a desired design is 3D-printable or to estimate elasticity, stiffness or shearing resistance. The invention provides a shortcut to this time-consuming and expensive approach by estimating the printability and/or other physical properties based on the samples of one or more past FEAs.

The structure may be a beam that connects nodes. For example, and as will be described in more detail herein, a midsole of a sports shoe may be made from a 3D-printed lattice of cells. Each cell might comprise beams that connect nodes. The nodes are usually located at the faces of the cells.

The sports article may be a shoe and the region may be located in the upper of the shoe. The method according to the invention may also be used in the two-dimensional case, for example to predict the physical properties of a shoe upper onto which patches are placed. Exemplary physical properties include bending stiffness and elasticity. Preferably, the physical property is the bending stiffness.

The method may further comprise the step of subdividing the region into pixels, wherein at least one structural feature is associated with each pixel. The value of each structural feature may depend on whether a patch is present in the pixel or not. In this way, the resulting feature vector represents the presence of patches in the particular region. As the patches influence the local properties of the upper like bending stiffness and elasticity, there is a close correspondence between the feature vectors and the physical properties to be estimated.

The structural feature may be a patch of material to be attached to the upper of the shoe. Patches allow to provide the shoe upper with specific functions in certain areas. The method according to the invention allows to estimate such functions without the need for time-consuming FEAs. In an example, the stiffness provided to a shoe upper depending on the number of patches, their shape and location may be quickly estimated.

The present invention also relates to a method of manufacturing at least a part of a sports article, comprising the steps: (a. ) estimating a physical property associated with a plurality of regions of the part of the sports article as described herein; (b. ) optimizing the physical property using the estimate and considering at least one structural constraint for the part of the sports article; and (c. ) manufacturing the part of the sports article.

The structural constraints may for example be set by a designer and may for example relate to aesthetical aspects of the design. In another example, the constraint is more of a technical nature and may for example refer to a minimum bending stiffness. The method may be used to obtain an optimal design respecting the constraint very quickly because time-consuming and expensive simulations can be avoided in the optimization loop. Instead, the physical property to be optimized is estimated by means of samples using the method of the present invention.

The step of manufacturing may comprise additive manufacturing. As mentioned, the method of the present invention can advantageously be applied to simulating an additive manufacturing process, such as a 3D-printing process, and yields a significant speedup compared to conventional full FEM simulations.

The step of manufacturing may comprise: providing a blank; and placing at least one patch on the blank. Such a patch-placement process may be used to manufacture a shoe upper. Again, the method of the present invention allows to speed up the whole process also in this two-dimensional case.

Another aspect of the present invention relates to a part of a sports article which is manufactured according to the method of manufacturing described above. Another aspect of the present invention relates to a sports article comprising such part.

In the following, exemplary embodiments of the invention are described with reference to the figures. The figures show:.

In the following, only some possible embodiments of the invention are described in detail. It is to be understood that these exemplary embodiments can be modified in a number of ways and combined with each other whenever compatible and that certain features may be omitted in so far as they appear dispensable.

<FIG> shows a flow chart of an exemplary method <NUM> according to the present invention with respect to the designing and/or manufacture of a midsole for a sports shoe by means of a 3D-printing process. It is to be understood that the present invention is neither limited to the manufacture of shoes nor to the usage of a particular manufacturing process.

The method according to the invention is based on the idea of looking at volumetric neighborhoods around structure elements. In case of the exemplary embodiment these structure elements are beams inside a lattice structure. The inventors realized that these volumetric neighborhoods have a direct relationship to the printability of that element, since it covers both, the element itself and the network effect with the surrounding. This volume-based method of linking a local neighborhood to physical properties can be used for a number of physical parameters like bending stiffness, shearing stiffness, etc. Basically, all physical or production parameters may be estimated with the method described herein.

Generally, the left side of <FIG> shows the learning process, i.e. establishing the relationship between the structure of the sole and the physical property - printability in the exemplary embodiment. The right side of <FIG> shows how the learned relationship can be used to make predictions or inferences based on the learned relationship.

In the exemplary embodiment relating to 3D-printing a midsole of a sports shoe, the target property to be estimated is the printability of the midsole. Thus, in a first step <NUM>, the geometry of the midsole is provided. This geometry may be provided by a designer. Alternatively, the geometry may be the result or intermediary result of an automated design process that aims to optimize certain objectives functions like for example bending stiffness given a number of constraints, which may for example be provided by a designer. In any case, a region is identified in step <NUM> with a geometry made up of structural features.

For a midsole, the structural features may be beams, for an upper they may be patches.

The printability of a lattice structure is an important aspect of the producibility and production speed of 3D-printed midsoles. The actual boundary conditions and speed highly depend on the printer and printing technology. Examples of such boundary conditions include struts that are not allowed to be narrower than <NUM>, overhanging structures without support and an angle relative to the print platform that should not be below <NUM> degrees. Those constraints generally depend on the local neighborhood and the print speed. As a rule of thumb, the quicker the printing process, the stricter those constraints have to be met - otherwise printing might fail.

In step <NUM> of the exemplary method <NUM>, the region is broken up into neighborhoods, wherein each neighborhood is centered on a structural feature - the window size of the neighborhood can be chosen to be narrowly focused on the structural feature or wider to incorporate more of the neighborhood. The neighborhoods are then subdivided into voxel elements. Generally, a voxel represents a value on a grid in three-dimensional space. For every beam of the midsole, a voxel volume (also denoted as a "window" or a bounding region), is created around it's center or around a different point of interest.

This step requires some parameters to be fixed beforehand, like for example the size of the voxels (window size), the resolution and whether a signed distance field or a binary volume is used. For example, depending on the globality of the physical properties, different window sizes have to be selected. The voxel size (or pixel size in the two-dimensional case) is dictated by the window size and the resolution.

Also, in step <NUM>, a feature value is obtained from the signature of the structural feature, e.g. a beam of a cell of midsole in the exemplary embodiment. More precisely, a vector of feature values is obtained from the representation of the structural feature by the voxels (or pixels in the two-dimensional case).

<FIG> shows an exemplary beam of a cell of a lattice structure that may be part of a midsole to be 3D-printed. In <FIG> the corresponding voxelated region of the beam is shown, wherein a voxel is black, i.e. a binary "<NUM>", if there is material present in the corresponding voxel or white, i.e. a binary "<NUM>", otherwise. In the example, a <NUM>×<NUM>×<NUM> binary signature of the beam is obtained which can then be linearized into a binary feature vector of size <NUM>.

In step <NUM> of the exemplary method <NUM> a high fidelity FEA based printability analysis for the full midsoles is run. The result of this analysis is deformation information that happens during printing. This deformation is directly linked to printability as large deformation leads to problems during printing. By linking this information back to the beam for which it was computed the required training data for the subsequent estimation of printability is obtained. More precisely, the FEA simulation provides a score for each structural feature. A score is a value that represents the physical property being investigated - e.g. tearing resistance, printability etc. Thus, the relationship between the result of the FEA, i.e. the score, and the corresponding element is established in step <NUM>.

In step <NUM> a relationship between the feature vector of each element and the associated physical property of the element, i.e. the score as obtained by the FEA simulation is learned. Thus, the voxelated elements form a limited plurality of samples to a machine learning algorithm. In the example, a standard machine learning algorithm, namely a convolutional neural network (CNN) is used. This network is trained to learn the relationship between voxel based neighborhoods and the corresponding physical properties, i.e. printability. The result of this step is a mapping that directly translates feature vectors (here voxel volumes) into physical properties (here printability) without the detour over a costly FEA system.

So far, steps <NUM>-<NUM> of the exemplary method <NUM> related to the learning side of the method. The result of these steps is a trained neural network with a set of weights corresponding to the "strength" of connections between the neurons of the network. The further steps of the method relate to applying the learned relationships beam structure/beam neighborhood and printability to a previously unknown midsole structure obtained in step <NUM> which is similar to step <NUM> of the learning phase. The phase illustrated on the right side of <FIG> is also referred to as the prediction phase and corresponds to the method steps of claim <NUM>.

In this phase, a printability score for every beam needs to be obtained quickly. To achieve this, a neighborhood is established around beams of the geometry obtained in step <NUM>. In addition, the neighborhood is voxelated in step <NUM> following the same rules that were used during training, i.e. in step <NUM>. In the same step, feature vectors are obtained for each beam as was described with respect to the learning phase. In step <NUM> the feature vectors are fed into the neural network that was trained in the training phase to finally obtain a printability score associated with the corresponding beam. Finally, in step <NUM> all scores for all structural features in the region of the midsole are used to obtain a value for the physical property for the whole region, e.g. printability.

Thus, the printability of the new midsole design can be obtained without using a full FEA simulation. The prediction phase of the neural network is finished in the order of a few seconds compared to the FEA runtime of several hours.

The method of the present invention may not only be used in the context of 3D-printing and for predicting printability based on local neighborhoods. Rather, arbitrary physical properties may be quickly estimated using the method.

Another exemplary embodiment of the present invention relates to the placement of reinforcement patches on a shoe upper. Even with a low number of patches and materials, a huge number of configurations is possible. Usually, every combination has to run through expensive FEA simulations. For every target parameter like e.g. roll-over stiffness, homogenous pressure, etc. a separate FEA run is required. Looking at customer-specific on-demand production, this is not viable, as simulation alone would run several hours. In addition, this traditional approach comes at high costs both in terms of the FEA software and computational power.

Claim 1:
A computer implemented method of manufacturing at least a part of a sports article, comprising the steps:
a. estimating a printability by 3D-printing associated with a region of the part of the sports article, comprising:
i. determining a plurality of structural features within the region,
ii. determining, for each structural feature, a feature value,
iii. mapping each feature value to the printability, wherein the mapping is based on a machine learning algorithm from a limited plurality of samples, and wherein each sample associates a feature value with a value of the printability, and
iv. using the mapping to estimate the printability for the region; and
b. optimizing the printability using the estimate and considering at least one structural constraint for the part of the sports article; and
c. manufacturing the part of the sports article, wherein the step of manufacturing comprises additive manufacturing.