Siemens Industry Software Gmbh

Siemens Aktiengesellschaft

Siemens Corporation

Siemens Product Lifecycle Management Software Inc.

Siemens Industry Software Nv

Lucia Mirabella

Suraj Ravi Musuvathy

Sanjeev Srivastava

Livio Dalloro

Erhan Arisoy

Louis Komzsik

Arquimedes Martinez Canedo

Utz Wever

Dirk Hartmann

David Madeley

Tsz Ling Elaine Tang

Nurcan Gecer Ulu

Birgit Obst

Yunhua Fu

Stefan Boschert

Elena Arvanitis

Edward Slavin, Iii

George Allen

Ayse Parlak

Yi Xu

Bharat Sanjay Prabhu

Yu-Chin Chan

Wesley Reinhart

Christoph Ernst Ludwig

Nicolas Lammens

Stefan Gavranovic

Hunor Erdelyi

Dehao Liu

Edward Iii Slavin

Methods and systems are disclosed for generation of cellular lattice kernels optimized by multiple objectives for highly specific targeted properties of geometry and topology rather than state of the art methods that rely on a predefined kernel library. Using a characterization of virtual kernel features, bulk material properties can be predicted using approximations from the virtual kernel rather than having to rely solely on experimental finite element simulations of lattice structures.

Computer aided design of custom cellular lattice kernels according to material properties

A computing system may include an initial design space engine and an active region adaptation engine. The initial design space engine may be configured to identify a design domain for which to optimize a topology based on an objective function and determine an active region. The active region adaptation engine may be configured to iteratively adapt the active region until an optimization ending criterion is satisfied. Iterative adaptation of the active region may include expanding the design domain to include branch design elements, performing finite element analysis (FEA) on the expanded design domain, and determining an adapted active region by activating some of the branch design elements based on an active sensitivity threshold and deactivating some of the active design element based on design variable value changes.

Active region adaptations for design domains in topology optimizations

A system and method for accelerated simulation setup includes receiving a description of a new problem for simulation, extracting input data and output data of previous simulation results, generating a representation of data based on the extracted input data and output data, and quantifying similarities between the new problem and the extracted input data and output data to identify a candidate simulation for the new problem. A machine learning component infers a solution output for the new problem based on extrapolation or interpolation of outputs of the candidate simulation, thereby conserving resources by eliminating a simulation generation and execution. Alternatively, an efficient simulation setup can be generated using the queried knowledge, input variables, and input parameters corresponding to the candidate simulation.

Accelerated simulation setup process using prior knowledge extraction for problem matching

Methods for modeling of parts with lattice structures and corresponding systems and computer-readable mediums. A method includes receiving a model of an object to be manufactured. The method includes receiving a user specification of a void region within the model to create a lattice. The method includes performing a trimming operation to create a trimmed lattice by tessellating void surfaces and grouping together at least one row of connected rods to be treated as a single entity.

System and method for modeling of parts with lattice structures

A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

System for automated generative design synthesis using data from design tools and knowledge from a digital twin

A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.

Method and apparatus for robust reduction of shape error in laser powder deposition based additive manufacturing process due to uncertainty

A system and method is provided that facilitates optimizing tool paths based on thermal/structural simulations of a part produced via a 3D-printer. A processor may carry out a first simulation of the part being additively produced according to a first set of tool paths that correspond to instructions usable to drive the 3D-printer to produce the part. The first simulation may include: determining a hexahedral mesh of the part that includes a plurality of hexahedron elements; determining an order of the elements of the mesh to deposit for additively producing the part based on the first set of tool paths; and simulating an incremental deposit of each of the elements of the mesh in the order that the elements are determined to be deposited. For each incremental deposit of an additional respective element, thermal characteristics and structural deformation characteristics of the deposited elements are determined, in which some elements have a change in volume to account for a structural deformation of previously deposited adjacent elements.

System and method for optimizing tool paths based on thermal/structural simulations of a part being produced via a 3D-printer

A system and method are provided for adaptive domain reduction for thermo-structural simulation of an additive manufacturing process. The system may include a processor configured to carry out a simulation of a part being additively produced according to a set of tool paths. The simulation may include determining an original mesh of the part; determining an order of the elements of the original mesh to deposit; and simulating an incremental deposit of each of the elements of the original mesh for a material in the order that the elements are determined to be deposited. For each incremental deposit of an additional respective element the processor may determine thermal characteristics and structural deformation characteristics of the deposited elements. After the deposit of several layers have been simulated, subsequent simulation of elements may be carried out based on a respective modified version of the original mesh in which at least some of the layers of the original mesh previously simulated to be deposited under a current layer are retained and at least some are omitted.

System and method for adaptive domain reduction for thermo-structural simulation of additive manufacturing process

A method for computer-aided control of an automation system is provided by use of a digital simulation model which simulates the automation system and which is specified by a number parameters comprising a number of configuration parameters) describing the configuration of the automation system and a number of state parameters describing the operational state of the automation system. Simulated operation runs of the automation system based on the simulation model can be performed with the aid of a computer, where a simulation run predicts a number of performance parameters of the automation system.

Method for computer-aided control of an automation system

A method of partitioning a model to facilitate printing of the model on a 3D printer includes identifying partition sensitive locations on the model and creating a binary tree with a root note representative of the model. An iterative partitioning process is applied to divide the model into objects by selecting a node of the binary tree without any children nodes, identifying a portion of the model corresponding to the node, and determining candidate cutting planes on the portion of the model based on the partition sensitive locations. During the process, analytic hierarchical processing (AHP) is applied to select an optimal cutting plane from the candidate cutting planes based on partitioning criteria. The optimal cutting plane is used to segment the portion of the model into sub-portions, and two children nodes representative of these sub-portions are created on the node of the binary tree.

Optimized partitioning of solid 3D models for additive manufacturing

A method and system is provided for computer-aided simulation of multi-layer selective laser sintering and melting in an additive manufacturing processes. The method may include receiving a solid model. The method may also include slicing the solid model geometry along a build direction and creating 3D meshes that represent manufacturing layers. In addition, the method may include simulating manufacture of each of the 3D meshes to produce corresponding deformed 3D meshes. Further, the method may include building a 3D mesh model from the deformed 3D meshes and displaying the 3D mesh model.

Computer-aided simulation of multi-layer selective laser sintering and melting additive manufacturing processes

A computer-implemented method for designing a gas turbine exhaust diffuser includes a computer identifying an initial diffuser design and performing a design refinement process. The design refinement process includes generating a new diffuser design by modifying the initial diffuser design according to one or more objective functions and one or more design constraints. A fluid velocity pattern associated with the new diffuser design is determined and used to compute particle tracing lines through an internal portion of the new diffuser design. The particle tracing lines are grouped into particle line bundles extending longitudinally within the new diffuser design and an average path is computed for each particle line bundle. Then, for each respective average path, a rigid tube capable of channeling gases through the internal portion of the new diffuser design is generated and added to the new diffuser design.

Designing the geometry of a gas turbine exhaust diffuser on the basis of fluid dynamics information

A computer-implemented method of optimized lattice partitioning of solid 3-D models for additive manufacturing includes a computer receiving a 3-D model of an object to be printed and functional specifications indicating desired mechanical properties for portions of the object. The computer generates a plurality of lattice template structures based on the 3-D model and a uniform grid structure of an internal surface of the object. The computer determines material behaviors for each of the plurality of lattice template structures using the functional specifications and assigns the lattice template structures to locations in the uniform grid structure based on the material behaviors of the lattice template structures, thereby yielding a printable lattice.

Functional 3-D: optimized lattice partitioning of solid 3-D models to control mechanical properties for additive manufacturing

A computer-implemented method for ranking design parameter significance includes a computer receiving an input dataset representative of a physical object. This input dataset includes a baseline parameters and associated probabilities. The computer also receives performance requirements. For each respective baseline parameter, the computer performs an analysis process. During this analysis process, a range of parameter values are selected for the respective baseline parameter based on its corresponding probability distribution. The range of parameter values are segmented into parameter subsets and multiple instances of a simulation are executed using the performance requirements to yield snapshots. A Proper Orthogonal Decomposition (POD) basis is derived using the snapshots. A sensitivity analysis is performed based on the POD basis to yield a sensitivity measurement representative of an effect of variation of the respective parameter on the performance requirements. The computer may then generate a ranking of the baseline parameters according to their corresponding sensitivity measurements.

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Plainsboro, NJ, United States of America

Lucia Mirabella