Patent Publication Number: US-2022215145-A1

Title: Machine learning for rapid automatic computer-aided engineering modeling

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of Indian provisional application Serial No. 201941020269, filed on May 22, 2019, the disclosure of which is hereby incorporated in its entirety by reference herein. 
     TECHNICAL FIELD 
     The present disclosure relates to aspects ofuse of machine learning for rapid automatic computer-aided engineering (CAE) modeling, for example, for use in the meshing of parts with complex features. 
     BACKGROUND 
     A 3D object may be modeled as a computerized representation that describes the geometry and other aspects of the object. Computer-Aided Design (CAD) involves the application of computers to aid in the creation and modification of 3D CAD objects. Computer-Aided Engineering (CAE) starts with “meshing” the CAD geometry, assembling such meshes of different parts that constitute an assembly, modeling the connection between meshes, applying forces and boundary conditions to the model to aid in analysis, or optimization of the model. A mesh is a discretization of the modeled object into simpler elements shell or solid elements that includes triangles, quadrilaterals, hexahedral elements. Mesh generation is the practice of creating a mesh by performing a subdivision of the continuous geometric spaces of the modeled 3D object into discrete geometric and topological cells. 
     SUMMARY 
     In one or more illustrative examples, a system for generating a finite element mesh, includes a memory configured to store a representation of geometric features of an object, a machine learning model configured to identify one or more feature families of features of the representation, and feature-specific parameters defining how to mesh the one or morefeature families; and a processor programmed to recognize and classify features of the representation into the feature families utilizing the machine learning model, apply feature-specific mesh parameters to the recognized and classified features of the representation, and generate a mesh of the representation in accordance with the feature-specific parameters. 
     In one or more illustrative examples, a method includes storing, to a memory, a representation of geometric features of an object, a machine learning model configured to identify one or more feature families of features of the representation, and feature-specific parameters defining how to mesh the one or more feature families; recognizing and classifying features of the representation into the feature families utilizing the machine learning model; applying feature-specific mesh parameters to the recognized and classified features of the representation; and generating a mesh of the representation in accordance with the feature-specific parameters. 
     In one or more illustrative examples, a non-transitory computer-readable medium includes instructions that, when executed by a processor, cause the processor to store, to a memory, a representation of geometric features of an object, a machine learning model configured to identify one or more feature families of features of the representation, and feature-specific parameters defining how to mesh the one or more feature families; recognize and classify features of the representation into the feature families utilizing the machine learning model; apply feature-specific mesh parameters to the recognized and classified features of the representation; and generate a mesh of the representation in accordance with the feature-specific parameters. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example block diagram of a process flow for the generation of a mesh by a CAE system using CAD data from a CAD system; 
         FIG. 2  illustrates an example block diagram of a system using machine learning for rapid automatic computer-aided engineering modeling; 
         FIG. 3  illustrates an example data flow diagram for the generation of an artificial intelligence (AI) model; 
         FIG. 4  illustrates an example data flow diagram for the creation of feature-specific settings; 
         FIG. 5  illustrates an example of meshes generated for three example features with different customer-specific settings; 
         FIG. 6  illustrates an example data flow diagram for the generation of a mesh from CAD data; 
         FIG. 7  illustrates an example process for the generation of an AI model; 
         FIG. 8  illustrates an example process for the generation of a mesh from CAD data in accordance with the AI model; 
         FIG. 9  illustrates an example rendering of a CAD file to be converted into a mesh; 
         FIG. 10  illustrates an example rendering of a CAD file highlighting features of the CAD file recognized by the AI model; and 
         FIG. 11  illustrates an example rendering of a mesh generated using feature-specific mesh parameters for the recognized features. 
         FIG. 12  illustrates an example of the automatic correction of quality specifications of the mesh; 
         FIG. 13  illustrates an example process for the automatic correction of quality specifications of a mesh; 
         FIG. 14  illustrates an example of additional different modeling algorithms that may be used to model feature families of CAD data into a mesh; 
         FIG. 15  illustrates an example process for the meshing of the feature families of CAD data  104  into the mesh; 
         FIG. 16  illustrates an example of identifying features or parts which are not needed for a specific simulation type; 
         FIG. 17  illustrates an example process for the meshing of CAD data into the mesh, while reconciling features to be ignored for the specific simulations to be performed; 
         FIG. 18  illustrates an example of thickness assignment performed using the CAE system; 
         FIG. 19  illustrates an example process for the automatic calculation of thickness for assignment to finite element of a mesh generated from CAD data; 
         FIG. 20  illustrates an example of components of different materials or manufacturing processes that may be converted into meshes; 
         FIG. 21  illustrates an example process for the meshing of CAD data into the mesh, while applying meshing algorithms according to material or manufacturing process; 
         FIG. 22  illustrates an example process for the retraining and use of an updated AI model for feature recognition and meshing; 
         FIG. 23  illustrates an example of identifying part assemblies from CAD data; and 
         FIG. 24  illustrates an example process for the meshing of CAD data into the mesh, while identifying locations for sensor placement. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present disclosure are described herein. It is to be understood, however, that the disclosed embodiments are merely examples and other embodiments can take various and alternative forms. The figures are not necessarily to scale; some features could be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the embodiments. As those of ordinary skill in the art will understand, various features illustrated and described with reference to any one of the figures can be combined with features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described. The combinations of features illustrated provide representative embodiments for typical applications. Various combinations and modifications of the features consistent with the teachings of this disclosure, however, could be desired for particular applications or implementations. 
     CAD models may be discretized and then used to simulate various aspects of the modeled object. To do so, the model must be converted into a mesh. Once converted into the mesh, a simulation phase may be performed using CAE simulation software. Some examples of simulation using the mesh include materials modeling, durability simulation, stiffness simulation, crash simulation, manufacturing simulation and optimization. Based on the simulation results, the model may be validated, or changes may be made to the model. 
     However, a challenge in the generation of meshes from CAD models is the time involved. In many cases, the meshing of parts may require hours or days of time expended in creating the mesh and ensuring that the created mesh meets with customer requirements. This may be due to the fact that certain features of the parts are to be meshed using specific meshing algorithms, but identification of the features during meshing may take a large amount of time. 
       FIG. 1  illustrates an example block diagram of a process flow for the generation of a mesh by a CAE system  106  using CAD data  104  from a CAD system  102 . As shown, the CAD system  102  is used by an operator to model one or more objects. In an example, CAD operator may utilize software such CATIA to perform the modeling. In this case, a door panel is illustrated, but this is merely one possibility. The CAD system  102  may generate the CAD data  104  based on the model. The CAD data  104  generally includes information with respect to lines and surfaces of the modeled objects. This CAD data  104  is then provided to the CAE system  106  for conversion into a mesh. First, the CAE system  102  performs automatic feature recognition  108  to identify features of the CAD data  104 . Then, the CAE system performs feature-specific mesh generation  110  on the CAD data  104  to generate a mesh according to best practices of the customer, which are defined by customer-specific settings  112 . 
     As explained in further detail below, the improved approach to generating the mesh from a CAD model includes the use of a machine-learning recognizer  114  for identification of features in the CAD data  104 . The improved approach utilizes a previously-trained machine learning model to recognize and classify features of the mesh using training data including known features within families of features. Feature-specific meshing algorithms may then be associated with the families of features. The approach may additionally recognize and classify features of the representation into the feature families utilizing the machine-learning recognizer  114  and feature database  116 , apply feature-specific meshing algorithms to the recognized and classified features of the representation, and generate a mesh of the representation in accordance with the feature-specific meshing algorithms. The CAE system  102  may further assign thicknesses to each element of the mesh based on the CAD data  104 . Additionally, the CAE system  102  may create an assembly by connecting multiple CAE meshes into a single overall object. Once these steps are completed, the mesh may be exercised for crash, vibration, or other aspects. Further aspects of the disclosure are discussed in detail below. 
       FIG. 2  illustrates an example block diagram of details of the CAE system  106  using machine learning for rapid automatic computer-aided engineering modeling. The CAE system  106  includes a processor  202  that is operatively connected to a memory  204 , a display device  206 , human-machine interface (HMI) controls  208 , and a network device  210 . CAD data  104  may be received to the CAE system  106  and provided to a mesh generation application  216  for conversion into a mesh  214 . A mesh plugin  234  may be utilized by the mesh generation application  216  to recognize and classify features of the CAD data  104  into the feature families utilizing an AI model  218  and assign feature-specific meshing algorithms  228  to the recognized feature families. It should be noted that the CAB system  106  is merely an example, and CAE systems  106  having more, fewer, or differently arranged elements may also be used. As one example, the functionality of the mesh plugin  234  may be incorporated into the mesh generation application  216  in other examples. 
     In the CAE system  106 , the processor  202  may include one or more integrated circuits that implement the functionality of a central processing unit (CPU) and/or graphics processing unit (GPU). In some examples, the processor  202  is a system on a chip (SoC) that integrates the functionality of the CPU and GPU. The SoC may optionally include other components such as, for example, the memory  204  and the network device  210  into a single integrated device. In other examples, the CPU and GPU are connected to each other via a peripheral connection device such as PCI express or another suitable peripheral data connection. In one example, the CPU is a commercially available central processing device that implements an instruction set such as one of the x86, ARM, Power, or MIPS instruction set families. Additionally, alternative embodiments of the processor  202  can include microcontrollers, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), or any other suitable digital logic devices. 
     Regardless of the specifics, during operation, the processor  202  executes stored program instructions that are retrieved from the memory  204 . The stored program instructions include software that controls the operation of the processor  202  to perform the operations described herein. The memory  204  may include both non-volatile memory and volatile memory devices. The non-volatile memory includes solid-state memories, such as NAND flash memory, magnetic and optical storage media, or any other suitable data storage device that retains data when the CAE system  106  is deactivated or loses electrical power. The volatile memory includes static and dynamic random-access memory (RAM) that stores program instructions and data during operation of the CAE system  106 . 
     The GPU may include hardware and software for display of at least two-dimensional (2D) and optionally three-dimensional (3D) graphics to a display device  206 . The display device  206  may include an electronic display screen, projector, printer, or any other suitable device that reproduces a graphical display. In some examples, the processor  202  executes software programs using the hardware functionality in the GPU to accelerate the performance of machine learning or other computing operations described herein. 
     The HMI controls  208  may include any of various devices that enable the CAE system  106  to receive control input. Examples of suitable input devices that receive human interface inputs may include keyboards, mice, trackballs, touchscreens, voice input devices, graphics tablets, and the like. 
     The network device  210  may include any of various devices that enable the CAE system  106  to send and/or receive data from external devices. Examples of suitable network devices  210  include a network adapter or peripheral interconnection device that receives data from another computer or external data storage device, which can be useful for receiving large sets of data in an efficient manner. 
     The CAD data  104  may refer to a computerized representation that describes the geometry and other aspects of an object to be simulated and/or manufactured. In an example, the CAD data  104  may include information indicative of vertices, edges, faces, polygons, and/or surfaces of the object. In another example, the CAD data  104  may further include material property data indicative of the materials of the object. The CAD data  104  may further include features such as stiffness, noise vibration harshness (NVH), crash, durability, and computational fluid dynamics (CFD) data. In some examples, the CAD data  104  may represent a set of separate parts, and the CAD data  104  may, accordingly, include assembly data indicative how to assemble the separate parts to create the object. 
     The CAD data  104  may include one or more features, which are individual elements of the object that may be included on a substrate from a library ofparts. As used herein, features generally refer to combinations of multiple different lower-level geometries that, in combination, form a higher-level construct. As some examples, these elements may be categorized into families of elements, such as heat stakes, clip towers, dog houses, and click fastener elements. As some other examples, these elements may be categorized into fbmilies such as nuts, bolts, molded rubber components, bushings, sleeves, seals, mounts, or bellows. 
     A family may include many different variations of the feature. For instance, a heat stake generally includes a tower element with a set of vanes connecting the tower element to the substrate of the object. Accordingly, the family of heat stakes may include many variations on this design, such as heat stakes having different numbers of ribs, different styles or shapes of ribs, different thicknesses, different heights, or connection with other features. 
     The mesh  214 , or finite element mesh, refers to vertices, edges, and faces that use a polygonal representation, such as triangles and quadrilaterals, to define an object such as a 3D shape. Thus, the mesh  214  is a discretization of the CAD data  104 . In general, the more polygons used to create the mesh  214  of the features, the less discretization error that occurs in the difference between the mesh  214  and the object described by the CAD data  104 . However, the more polygons used, the larger the amount of storage and computing power that is required for use of the mesh  214 . 
     The mesh generation application  216  includes instructions that, when executed by the processor  202  of the CAE system  106 , cause the CAE system  106  to perform various processes and operations described herein. The mesh generation application  216  may be programmed to generate a mesh  214  representation of the object from the information of the CAD data  104 . As some examples, the mesh generation application  216  may make use of techniques such as multi-block structured/mapped mesh generation, unstructured mesh generation, face clustering, a hybrid of these approaches, and so on to build a mesh  214  of the shapes represented by the CAD data  104 . 
     The mesh generation application  216  may use various meshing algorithms to configure the generation of the mesh  214  from the CAD data  104 . As some non-limiting examples, these meshing algorithms may specify how closely the mesh faces adhere to the shape of the object, the level of smoothness, and the density of the tessellation (e.g., the number of subdivisions) per dimension. 
     In artificial intelligence (AI) systems, model-based reasoning refers to an inference method that operates based on an AI model  218  of a worldview to be analyzed. Generally, the AI model  218  is trained to learn a function that provides a precise correlation between input values and output values. At runtime, an AI engine uses the knowledge encoded in the AI model  218  against observed data to derive conclusions such as a diagnosis or a prediction. One example AI engine may include the TensorFlow AI engine made available by Alphabet Inc. of Mountain View, Calif., although other machine learning systems may additionally or alternately be used. As discussed in detail herein, the AI model  218  may be configured to recognize and classify features of the CAD data  104  into the Feature families. 
       FIG. 3  illustrates an example data flow diagram  300  for the generation of an AI model  218 . Referring to  FIG. 3 , and with continuing reference to  FIG. 2 , the AI model  218  may be trained by an AI training application  220  to recognize the features based on a set of training data  222 . The training data  222  may include various variations of the features to be recognized by the AI model  218  as well as ground truth information indicative of what features are included in the training data  222  to be recognized. 
     The training data  222  may include data from many different features, based on CAD data  104  from one or more customers. The training data  222  may be stored to the feature database  116 , in an example. As different customers may utilize different features, when a new customer is added, training data  222  including examples of that customer&#39;s features may be added to the training data  222  to improve the AI model  218  in recognition of those additional features. This additional data may include, for example, a new design of a feature for an existing customer, or a new feature that is not already recognized by the AI model  218 . It should also be noted that while in some examples the features may be features of plastic models, in other examples the features may further include metal parts such as bolts, nuts, gears, connectors, or rubber parts such as gaskets. 
     The training data  222  may, in one example, include collections of 2D views or projections of features of models, where the views are taken as renderings of sample CAD data  104  at many different angles and distances. In such an example, the at model  218  may be trained to recognize the features in 2D form. Accordingly, when recognition is performed by the AI model  218 , the recognition is performed in 2D form using 2D views or projections of the CAD data  104  to be converted into a mesh  214 . 
     Testing data  224 , which may be a subdivision of the training data  222  that is not used for training, may be used to validate the accuracy of the AI model  218  in recognizing feature of the CAD data  104 . Through use of the testing data  224 , the AI training application  220  may provide training results  226 , which may be used to identify weaknesses in the AI model  218  or areas in which the AI model  218  should receive additional data to improve in its recognition of features in feature 
     It should be noted that the AI model  218  may include be trained based on training data  222  that is common across customers. This may allow for the AI training application  220  to take advantage of variations across a wide set of training data  222  in the formulation of the AI model  218 . However, in some examples, customers may wish to have their data remain proprietary and not be shared in the generation of the AI model  218 . In such instances, the AI model  218  for a customer may be created using proprietary training data  222 , as well as whatever common training data  222  is available. 
       FIG. 4  illustrates an example data flow diagram  300  for the creation of feature-specific meshing algorithms  228 . Referring to  FIG. 4 , and with continuing reference to  FIG. 2 , the feature-specific meshing algorithms  228  include meshing algorithms that may be used by the mesh generation application  216  to configure the generation of the mesh  214  from the CAD data  104 . The feature-specific meshing algorithms  228  may include base settings  230 , which may include industry-standard meshing algorithms for the generation of meshes  214  from CAD data  104 . 
     The feature-specific meshing algorithms  228  may further include customer-specific settings  112 , which may be specified by the specific customer to override the base settings  230  in instances where the customer has requirements that deviate from the base settings  230 . 
       FIG. 5  illustrates an example  500  of meshes generated for three example features with different customer-specific settings  112 . With respect to a heat stake identified in CAD data  104  and similarly with a solid time identified in the CAD data  104 , the heat stake may be assigned meshing algorithms that relate to aspects such as target length, minimum length, and till rib height. Additionally, with respect to the modeling of thickness information, the heat stake may also be assigned meshing algorithms with respect to step thickness and average thickness of the feature. As another example, with respect to a hole identified in the CAD data  104 , meshing algorithms such as hole FE target length, number of zones surrounding the hole, zone width, and target length may be specified. It should be noted that these meshing algorithms may be specified as the customer-specific settings  112 , and may override base settings  230  for these values based on customer best-practices. The CAE system  106  may include a user interface through which a customer may input the customer best-practices. 
       FIG. 6  illustrates an example data flow diagram  600  for the generation of a mesh  214  from CAD data  104 . Referring to  FIG. 6 , and with continuing reference to  FIG. 2 , a mesh plugin  234  may be utilized by the mesh generation application  216  to make use of the AI model  218  to identify features of the CAD data  104  as well as to associate feature-specific meshing algorithms  228  corresponding to the identified features with the CAD data  104  for generation of the mesh  214 . For example, if the mesh plugin  234  identifies a heat stake in the CAD data  104 , then the mesh plugin  234  may associate feature-specific meshing algorithms  228  for heat stakes with that identified portion of the CAD data  104 . 
     With respect to  FIG. 2 , while the illustrated CAF system  106  is shown using a single computing device that incorporates the processor  202  and display device  206 , other example CAE system  106  may include multiple computing devices. As one example, one processor  202  generates the AI model  218 , while another processor  202  uses the AI model  218  for generation of the mesh  214  from the CAD data  104 . In another nonlimiting example, the processor  202  is implemented in a server computing device that executes the mesh generation application  216  to generate the mesh  214  for a client computing device that receives the mesh  214  andlor performs simulations using the mesh  214 . 
       FIG. 7  illustrates an example process  700  for the generation of an AI model  218 . In an example, the process  700  may be performed by aspects of the CAE system  106 . For instance, the process  700  may be performed by execution of the AI training application  220  by the processor  202 . As one example, the process  700  may be executed via an application programming interface or visual interface available for use by customers of the AI training application  220 . As another example, the process  700  may be executed by a vendor of the AI training application  220 . It should be noted that the illustrated process  700  is one example, and different operations or orderings of operations may be used. 
     At operation  702 , the processor  202  receives training data  222 . In an example, the training data  222  may include examples of features within a family received from a customer. In another example, the training data  222  may additionally or alternately include examples of features within a family received from a database of different feature designs. 
     The processor  202  trains the AI model  218 , at  704 , to recognize features within the family classification. In an example, the training data  222  may include collections of  217  views or projections of features of models, where the views are taken as renderings of sample CAD data  104  at many different angles and distances. In such an example, the processor  202  may utilize TensorFlow or another AI modeling system to train the AI model  218  to recognize the features in 2D form. In other examples, the training data  222  may be stored as CAD data  104  and may be rendered at many different angles and distances to perform the training. In yet further examples, the AI model  218  may be trained using CAD data  104  to recognize features in 3D and may be applied against 3D CAD training data  222  directly. 
     At  706 , the processor  202  validates the AI model  218  using testing data  224 . In an example, a subdivision of the training data  222  that is not used for training may be used to validate the accuracy of the AI model  218  in recognizing feature of the CAD data  104 . Through use of the testing data  224 , the AI training application  220  may provide training results  226 , which may be used to identify weaknesses in the AI model  218  or areas in which the AI model  218  should receive additional data to improve in its recognition of features in feature families. 
     At operation  708 , the processor  202  saves the AI model  218  for use in recognizing features within the family classification. In an example, the AI model  218  may be used as described in the process  800  for the identification of features in CAD data  104  to aid in the generation of a mesh  214  of the CAD data  104 . After operation  708 , the process  700  ends 
       FIG. 8  illustrates an example process  800  for the generation of a mesh  214  from CAD data  104  in accordance with the AI model  218 . In an example, as with the process  700 , the process  800  may be performed by aspects of the CAE system  106 . For instance, the process  800  may be performed by execution by the processor  202  of the mesh generation application  216  and mesh plugin  234 . 
     The processor  202  receives a CAD file  104  at operation  802 . In an example, the CAD file  104  may be received from storage in the memory  204 . In another example, the CAD file  104  may be received to the processor  202  via the network device  210  (e.g., over a network, from a CAD terminal, etc.)  FIG. 9  illustrates an example  900  rendering of a CAD file  104  to be converted into a mesh  214 . As shown, the CAD file  104  describes a 3D model of a plastic door panel for an automobile. It should be noted that this is only an example, and the techniques described herein may be applicable to other types of parts. For example, the CAD file  104  may represent non-plastic features such as a metal object. 
     At  804 , the processor  202  recognizes and classifies features in the CAD file  104  using the AI model  218 .  FIG. 10  illustrates an example  1000  rendering of a CAD file  104  highlighting features of the CAD file  104  recognized by the AI model  218 . As shown, the processor  202  has identified seven heat stakes, two clip towers, and thirteen clicks in the 3D model described by the CAD file  104 . 
     At operation  806 , the processor  202  applies feature-specific meshing algorithms  228  to the classified features recognized at operation  604 . In an example, the processor  202  identifies, for each of the identified features, the feature-specific settings  228  that correspond to that identified feature. The processor  202  may further associate those feature-specific settings  228  with the elements of the CAD file  104  that comprise the identified feature, such that the mesh  214  generation functionality of the mesh generation application  216  utilizes the associated feature-specific meshing algorithms  228  when meshing the identified feature. 
     At  808 , the processor  202  generates the mesh  214  using the feature-specific meshing algorithms for the recognized features.  FIG. 11  illustrates an example  1100  rendering of a mesh  214  generated using feature-specific meshing algorithms for the recognized features. It should be noted that the CAE system  106  may be programmed to identify based on the generated mesh  214  whether the mesh  214  does, in fact, confirm to stipulated quality specifications. For instance, the mesh  214  may be required to meet a predefined discretization error threshold, and if the mesh  214  does not meet the error threshold, then the CAE system  106  may be programmed to made further adjustments to the mesh  214  (e.g., increase the density of polygons, etc.) to bring the mesh  214  into compliance with the error threshold. 
     Thus, by training an AI model  218  to recognize and classify features of the CAD file  104  based on known features within families of features and applying feature-specific meshing algorithms  228  to those identified features, a mesh  214  may be rapidly generated from the CAD file  104  that corresponds to customer-specific requirements. 
       FIG. 12  illustrates an example  1200  of the automatic correction of quality specifications of the mesh  214 . As shown in the examples  1200 , the CAE system  106  may be programmed to automatically fix mesh quality errors in the model, such as minimum element length, warped element leads, angle or Jacobian failed elements, skewness, aspect ratio, or to minimize the number of triangular elements. 
       FIG. 13  illustrates an example process  1300  for the automatic correction of quality specifications of a mesh  214 . In an example, the process  1300  may be performed by the elements of the CAE system  106  discussed in detail above, such as the processor  202 , memory  204 , mesh generation application  216 , and so on. Beginning with CAD data  104  at  1302 , and customer input parameters at  1304 , feature specific meshing may be performed at  1306 . Feature specific meshing is discussed in detail above, including with respect to the process  800 . Using a machine leaning model at  1308  which is used to identify errors in the quality specifications of the mesh  214 , at  1310 , the processor  202  identifies and attempts to fix elements that failed to meet the quality specifications of the mesh  214 . If such elements are found, the processor  202  trains the machine learning model of  1308  for clearing the new errors in the complex regions. The retraining may accordingly allow the machine learning model to identify errors which may then be fixed at  1312 , such as illustrated in the example  1200 . 
       FIG. 14  illustrates an example  1400  of additional different modeling algorithms that may be used to model feature families of the CAD data  104  into the mesh  214 . As mentioned above, CAE is an approximation. Modeling carefully and interpreting the results therefore becomes important for accuracy of the approximation. Base settings  230  and customer-specific settings  112  are algorithms that may be followed for modeling the specific features of the CAD data  104  based on the CAE approach as defied as base requirements or as defined by customer requirements. In addition to the examples shown in  FIG. 5 , the example  1400  shows additional images of features modeled with different meshing algorithms. For instance, the step thickness and average thickness are modeled for a heat stake element and also fora solid tube element. Additionally, different modelings of a hole are also shown. 
       FIG. 15  illustrates an example process  1500  for the meshing of the feature families of CAD data  104  into the mesh  214 . In an example, the process  1500  may be performed by the elements of the CAB system  106  discussed in detail above, such as the processor  202 , memory  204 , mesh generation application  216 , and so on. Similar to the process  800 , beginning with CAD data  104  at  1502 , and using mesh guidelines at  1504  such as base settings  230  and customer-specific settings  112 , at  1506  the processor  202  utilizes a machine learning model, such as the AI model  218  to recognize features of the mesh  214 . The mesh guidelines may define meshing algorithms for each of the different feature families. At  1508 , as discussed above including with respect to the process  800 , feature specific meshing is performed to the CAD data  104  to generate the mesh  214  in accordance with the meshing algorithms as applied to the identified features per the mesh guidelines. 
       FIG. 16  illustrates an example  1600  of identifying features or parts which are not needed for a specific simulation type. This identification may be performed to ignore these aspects of the CAD data  104 , so as to reduce the computational time and increase the accuracy of the simulation. For instance, the AI model  218  may be trained without regard to capture of the features or parts in the finite elements (FE) that are not required for specific simulations, such as for simulations including noise vibration harshness (NVH), crash, durability, and computational fluid dynamics (CFD). 
     Referring more specifically to the example  1600 , honeycomb projections may be identified on the surface of the element of the CAD data  104 . However, for a crash simulation or for a CFD simulation, such honeycomb projections may not be required to be captured. In contrast, for a durability simulation or for a NVH simulation, the honeycomb projections may be are included and may be necessary as they may provide stiffness to the parts. 
       FIG. 17  illustrates an example process  1700  for the meshing of CAD data  104  into the mesh  214 , while reconciling features to be ignored for the specific simulations to be performed. In an example, the process  1700  may be performed by the elements of the CAE system  106  discussed in detail above, such as the processor  202 , memory  204 , mesh generation application  216 , and so on. Similar to the processes  800  and  1500 , beginning with CAD data  104  at  1702 , and using mesh guidelines at  1704  such as base settings  230  and customer-specific settings  112 , at  1706  the processor  202  utilizes a machine learning model, such as the AI model  218  to recognize features of the mesh  214 . However, here the AI model  218  may be used to recognize features to be ignored for the specific simulations to be run, such as discussed with respect to the example  1600 . For instance, the process  1700  may receive input indicative of the intended simulations to run with the mesh  214  to be generated. At  1708 , feature specific meshing is performed to the CAD data  104  to generate the mesh  214  in accordance with the meshing algorithms as applied to the identified features per the mesh guidelines, but while ignoring the features that are not required for the specific simulations being run. 
       FIG. 18  illustrates an example  1800  of thickness assignment performed using the CAE system  106 . As noted herein, finite element analysis is an approximation process. In one example, to save the computational time, the CAD data  104  FE elements may be captured mid-plane. To allow for thickness information, the 3d structure of the CAD data  104  may be denoted in the mesh  214  by thickness data, which is calculated and assigned to the FE elements. The example  1800  shows both average thickness information, e.g., with thickness of 1.6 mm, as well as step thickness of the mesh, with thicknesses that step from 1.1, to 1.2, to 1.4, to 1.6, and to 1.7 mm. 
       FIG. 19  illustrates an example process  1900  for the automatic calculation of thickness for assignment to finite element of a mesh generated from CAD data  104 . In an example, the process  1900  may be performed by the elements of the CAE system  106  discussed in detail above, such as the processor  202 , memory  204 , mesh generation application  216 , and so on. Beginning with CAD data  104  at  1902 , and customer input parameters at  1904 , feature specific meshing may be performed at  1906 . Feature specific meshing is discussed in detail above, including with respect to the process  800 . Using a machine leaning model at  1908  which is trained for thickness assignment of mid-plane mesh  214  data, at  1910 , the processor  202  performs automatic calculation of mesh  214  thickness with respect to the CAD data  104  for assignment to the finite elements of the mesh  214 . If such finite elements are found, the processor  202  trains the machine learning model of  1908  for assigning the new thicknesses in the complex regions. The retraining may accordingly allow the machine learning model to better identify thicknesses and regions onto which the thicknesses may be assigned. The thicknesses may then be applied at  1912 , such as illustrated in the example  180 . 
       FIG. 20  illustrates an example  2000  of components of different materials or manufacturing processes that may be converted into meshes  214 . While many examples herein relate to plastic features such as heat stakes, clip towers, dog houses, or click fasteners, the systems and methods described herein may identify elements made from different materials as well, such as metals or rubber components. Moreover, the identified features may be generated by different manufacturing processes including forming, molding, extrusion, casting, forming, forging. Moreover, these different manufacturing processes andor materials may be modeled with guidelines specific to the manufacturing process and/or material. This may be done, in an example, through use of meshing algorithms that are tailored to the specific manufacturing processes and/or materials. In some example the AI model  218  may be trained to identify the different components by manufacturing processes and/or materials as well, to facilitate the assignment of meshing algorithms by process and/or by material, instead of or in addition to by feature. 
       FIG. 21  illustrates an example process  2100  for the meshing of CAD data  104  into the mesh  214 , while applying meshing algorithms according to material or manufacturing process. In an example, the process  2100  may be performed by the elements of the CAE system  106  discussed in detail above, such as the processor  202 , memory  204 , mesh generation application  216 , and so on. Similar to the processes  800 , 1500  and  1700 , beginning with CAD data  104  at  2102 , and using mesh guidelines at  2104  such as base settings  230  and customer-specific settings  112 , at  2106  the processor  202  utilizes a machine learning model, such as the AI model  218  to recognize features of the mesh  214 . However, here the AI model  218  may be used to recognize features according to manufacturing processes and/or materials, such as discussed with respect to the example  2000 . At  2108 , feature specific meshing is performed to the CAD data  104  to generate the mesh  214  in accordance with the meshing algorithms as applied to the identified features for the respective manufkturing processes and/or materials per the mesh guidelines. 
       FIG. 22  illustrates an example process  2200  for the retraining and use of an updated AI model  218  for feature recognition and meshing. In an example, the process  2200  may be performed by the elements of the CAE system  106  discussed in detail above, such as the processor  202 , memory  204 , mesh generation application  216 , and so on. At  2202 , with CAD data  104  may be input or received, which may be applied to a machine learning model at  2204 . If, at  2206 , not all of the features of the CAD data  104  are recognized using the model, then the processor  202  may determine that not all features of the model have been trained. If so, control passes to a machine learning wrapper  2208  to update the feature database  116  at  2210  to include examples (or additional examples) of the features that were not recognized. The machine learning wrapper  2208  may be accessed by a customer to add customer-specific features to a customer-specific feature database  116  in one example, but in other examples, the features may be added to a feature database  116  that is shared across customers or sites. Regardless, these examples may take the form of 2D views of the features as discussed in detail above. Next, at operation  2212  the processor  202  may retain the AI model  218  according to the updated features in the feature database  116 . The AI model  218  may be a deep-learning model  2214  as shown, generated using deep learning techniques according to the features included in the feature database  116 . Once retrained, control may return to operation  2204  to again attempt to recognize the features of the CAD data  104 . If all the features are trained at  2206 , then feature recognition is performed at  2216 , and CAE processing, such as meshing and simulation, is performed by the processor  202  at operation  2218 . Thus, the machine learning wrapper  2208  may be used to add new feature classes into the AI model  218  and may collect raw data for this new feature class and help retrain the AI model  218 . The newly trained model may then be used for recognizing corresponding new features in the CAD models  104 . 
       FIG. 23  illustrates an example  2300  of identifying part assemblies from CAD data  104 . In an example, the processor  202  may be programmed to automatically recognize different physical parts for creating sensor or boundary conditions for different model types. This may be advantageous, as manual identification of the specific areas where the sensor or the boundary conditions for CAE are to be incorporated may be time consuming to implement. As shown in the example  2300 , sub-assemblies such as the instrument panel, console, and steering assembly may be included in a vehicular assembly. As shown at  2303 , parts such as a glove box portion of the instrument panel assembly may be identified for simulations, such as for knee-impact analysis. The CAE system  106  may accordingly locate and create accelerometer in the correct locations for use in occupant safety simulations. As one more specific example, the CAE system  106  may infer that the vehicle components in the CAD data  104  relate to a station wagon and may place the sensors in locations consistent with those used for station wagon testing. As another example, the CAE system  106  may infer that the vehicle components in the CAD data  104  relate to a convertible vehicle and may place the sensors in locations consistent with those used for convertible vehicle testing. 
       FIG. 24  illustrates an example process  2400  for the meshing of CAD data  104  into the mesh  214 , while identifying locations for sensor placement. In an example, the process  2400  may be performed by the elements of the CAE system  106  discussed in detail above, such as the processor  202 , memory  204 , mesh generation application  216 , and so on. Beginning with CAD data  104  at  2402 , and using mesh guidelines at  2404  such as base settings  230  and customer-specific settings  112  at  2404 , at  2406  the processor  202  utilizes a machine learning model, such as the AI model  218  to recognize features of the mesh  214 . However, here the AI model  218  may be used to recognize parts and assemblies in the CAD data  104  for determining specific areas to apply sensors, such as discussed with respect to the example  2300 . At  2408 , identification of the specific areas in the CAD data  104  is performed based on the use of the machine learning model. Accordingly, sensor positions for the CAD data  104  may be quickly and automatically applied, which may be useful in meshing and simulation of the mesh  214  at the proper identified sensor locations. 
     The processes, methods, or algorithms disclosed herein can be deliverable to-implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored as data and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software executable object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and firmware components. 
     While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms encompassed by the claims. The words used in the specification are words of description rather than limitation, and it is understood that various changes can be made without departing from the spirit and scope of the disclosure. As previously described, the features of various embodiments can be combined to form further embodiments of the invention that may not be explicitly described or illustrated. While various embodiments could have been described as providing advantages or being preferred over other embodiments or prior art implementations with respect to one or more desired characteristics, those of ordinary skill in the art recognize that one or more features or characteristics can be compromised to achieve desired overall system attributes, which depend on the specific application and implementation. These attributes can include, but are not limited to cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, serviceability, weight, manufacturability, ease of assembly, etc. As such, to the extent any embodiments are described as less desirable than other embodiments or prior art implementations with respect to one or more characteristics, these embodiments are not outside the scope of the disclosure and can be desirable for particular applications.