Description:DESCRIPTION:
Field of the invention:
[0001] The present disclosure generally relates to the technical field of computer-aided structure design, and in particular relates to, a system and method that design optimal lattice structures for additively manufactured sandwich lattice structures.
Background of the invention:
[0002] Traditional lattice structures have consistent features and dimensions. Sandwich panel structures made of low-density cores and solid sheets are widely used in engineering, medical, aeronautical, and safety applications. These panels are generally made up of high-density top and bottom sheets with a lower-density core. Sandwich panels provide benefits over solid entities of similar volume and material in terms of weight, strength, and specific energy absorption. Cellular core architectures are preferred because of their high strength-to-weight ratios and stiffness. The design of the contact between the core and the sheets is crucial in determining the greatest load transmission capability.

[0003] Strut bond failure has been discovered as a possible failure mechanism in traditional sandwich structures, and similar strut-related failure modes have been reported in sandwich panels using lattice truss cores when subjected to high tensile or impact stresses. The struts assist force transmission from the sheets to the core, assuming acceptable core-sheet bond strength and ductility, with the core's relative density and topology dictating its load-bearing capability. Several elements impact the panel's resilience, including the base material, cellular structure, porosity level, strut thickness, and sheet thickness.

[0004] Computer-aided design (CAD) software has been developed to create 3D representations of objects and includes tools for improving these 3D components with lattices and sheets of various sizes, thicknesses, and densities. Lattices are solid structures that cover or overlay the lattices and are made up of interconnecting beams or struts at junctions. These technologies allow for the rapid redesign of 3D components in order to minimise weight while keeping desirable performance characteristics such as stiffness and flexibility.

[0005] Internal lattice structures for manufacturing have been created using a variety of lattice topologies. In recent years, most commercial software programmes that use the finite element approach for structural analysis have incorporated weight-saving optimisation strategies. These approaches enable the depiction of lightweight material structures using truss components with various unit cell arrangements. Such lattice systems have outperformed standard solid materials in terms of structural performance, most notably in lightweight sandwich core constructions, medical implants, and innovative metamaterials with specialized mechanical and thermal characteristics.

[0006] Leading structural analysis software vendors offer software packages that provide capabilities for incorporating diverse unit cell lattice structures into low-density material areas. Struts, honeycombs, and triply periodic minimum surfaces (TPMS) are the three basic forms of cellular lattice structures. For example, within struts, we can identify instances like SCC, BCC, and OCTET, whereas honeycombs can be triangular, hexagonal, or square. Similarly, TPMS designs include Schwarz, diamond, and gyroid.

[0007] Factors like the base material, strut thickness, unit-cell size, porosity, and sheet thickness can be adjusted to meet specific user requirements. In addition, finite element analysis (FEA) has proven invaluable for assessing the ability of lattice structures to withstand substantial forces while preserving their structural integrity. However, selecting the appropriate lattice core design from the extensive database available can be time-consuming and costly.

[0008] In existing technology, methods and systems for generating lattice recommendations in computer-aided design applications are known. Here, the lattice-generated system initially obtains a mechanical problem definition that includes a 3D model of an object. Next, a numerical simulation model is generated for the 3D model using loading cases. Next, the performance of the different lattice settings in different orientations is predicted in the design space using a lattice structural behavior model in place of the baseline material model in the numerical simulation model.

[0009] Finally, a set of lattice proposals is presented for the design space based on the predicted performance of the different lattice settings in different orientations. However, the conventional lattice-generated techniques may provide ranking to lattice topologies but do not provide optimized lattice structure with low cost.

[0010] Therefore, there is a need for a system that designs optimal lattice structures for additively manufactured sandwich lattice structures. There is also a need for a system and method that provide optimized designs at a low cost without using any manufacturing or material testing. There is also a need for a system and method that select lattice structures effectively. Further, there is also a need for a system and method that eliminates selecting of appropriate lattice core designs from the extensive database, which consumes more time.
Objectives of the invention:
[0011] The primary objective of the invention is to provide a system that designs optimal lattice structures for additively manufactured sandwich lattice structures.

[0012] Another objective of the invention is to provide a system and method that suggest a range of lattice settings for effective selection.

[0013] The other objective of the invention is to provide a system that reduces manufacturing costs by minimizing the weight of materials and components while consuming less fuel during transportation, especially in industries such as aerospace and automotive.

[0014] The other objective of the invention is to provide a system that reduces material waste and weight while maintaining structural performance.

[0015] The other objective of the invention is to provide a system that assists in making decisions by using multiple criteria for selecting an optimized lattice structure.

[0016] The other objective of the invention is to provide a system that optimizes lattice structure, thereby assisting in the preparation of products that outperform competitors in the market.

[0017] The other objective of the invention is to provide a system that gains a competitive advantage by producing lighter, stronger, and more efficient products, thereby attracting customers seeking high-performance solutions.

[0018] The other objective of the invention is to provide a system that reduces material waste, aligns with sustainability goals, contributes to resource efficiency, and reduces the environmental impact of the manufacturing process.

[0019] Yet another objective of the invention is to provide a system that minimizes the need for repetitive simulations and speeds up the design process.

[0020] The further objective of the invention is to provide a system that assists engineers in evaluating different lattice structures and enabling design iterations more rapidly.
Summary of the invention:
[0021] The present disclosure proposes a system and method for selecting optimal designs for lightweight additive-manufactured sandwich lattice structures. The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key or critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0022] In order to overcome the deficiencies of the prior art, the present disclosure is to solve the technical problem by providing a system and method that design optimal lattice structures for additively manufactured sandwich lattice structures.

[0023] According to an aspect, the invention proposes a system for selecting optimal designs for lightweight additive-manufactured sandwich lattice structures. The system suggests a range of lattice settings for effective selection. The system reduces manufacturing costs by minimizing the weight of materials and components to consume less fuel during transportation, especially in industries such as aerospace and automotive. The system reduces material waste and weight while maintaining structural performance. The system assists in making decisions by using multiple criteria for selecting an optimized lattice structure.

[0024] In one embodiment herein, the system comprises a computing device having a processor and a memory for storing one or more instructions executed by the processor. The computing device is configured to receive user data from at least one user to design one or more lattice structures. In one embodiment herein, the user data includes at least one of unit cell size, unit cell shape, and surface thickness.

[0025] In one embodiment herein, the computing device is also configured to analyze the user data using finite element analysis (FEA) simulation for determining the effective mechanical properties of one or more lattice structures. In one embodiment herein, the effective mechanical properties include at least one of relative density, Young's modulus, tensile strength, and compression strength.

[0026] In one embodiment herein, the computing device is also configured to rank the one or more lattice structures by applying multi-criteria decision-making models, thereby listing the one or more lattice structures based on the ranking. In one embodiment herein, the at least two multi-criteria decision-making models include Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA).

[0027] In one embodiment herein, the computing device is also configured to display the list of the one or more lattice structures based on the ranking for the at least one user to select at least one optimal lattice structure from the list of the one or more lattice structures based on predefined criteria. In one embodiment herein, the predefined criteria include user data of the unit cell, mechanical properties, unit cell topology, unit cell dimensions, relative density, deformation under load and loading conditions.

[0028] In one embodiment herein, the computing device is also configured to generate a 3D model of a sandwich lattice panel with the at least one optimal lattice structure selected by the at least one user. In one embodiment herein, the computing device is configured to receive the 3D model of the sandwich lattice panel with the at least one optimal lattice structure. The computing device is configured to calculate the effective mechanical properties for a section of the 3D model that has to be filled with the at least one optimal lattice structure.

[0029] In one embodiment herein, the computing device is configured to determine design parameters for the at least one optimal lattice structure based on the obtained effective mechanical properties. The computing device is configured to incorporate the at least one optimal lattice structure with the determined design parameters into the 3D model.

[0030] In one embodiment herein, the computing device is configured to perform finite element analysis (FEA) to determine FEA data that characterizes effective mechanical properties for the lattice design parameters provided by the at least one user. In one embodiment herein, the computing device is configured to store the FEA data for the lattice design parameters in a database for determining the lattice design parameters and the effective mechanical properties of the lattice structure.

[0031] In one embodiment herein, the computing device is configured to create the 3D model of the sandwich lattice panel with the at least one optimal lattice structure to produce an object using a 3D printer. In one embodiment herein, the computing device is configured to perform the finite element analysis (FEA) simulation on the product in order to provide the mechanical properties to the user for real-time usage.

[0032] According to another aspect, the invention provides a method for selecting one or more lattice structures using a system. First, at one step, the computing device receives user data from the at least one user to design one or more lattice structures. At another step, the computing device analyzes the user data using finite element analysis (FEA) simulation to determine the effective mechanical properties of the one or more lattice structures. At another step, the computing device ranks the one or more lattice structures by applying multi-criteria decision-making models, thereby listing the one or more lattice structures based on the ranking.

[0033] At another step, the computing device displays the list of the one or more lattice structures based on the ranking for at least one user to select at least one optimal lattice structure from the list of the one or more lattice structures. Further, at another step, the computing device generates a 3D model of a sandwich lattice panel with the at least one optimal lattice structure selected by the at least one user.

[0034] Further, objects and advantages of the present invention will be apparent from a study of the following portion of the specification, the claims, and the attached drawings.
Detailed description of drawings:
[0035] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate an embodiment of the invention, and, together with the description, explain the principles of the invention.

[0036] FIG. 1 illustrates a block diagram of a system for selecting one or more lattice structures for designing sandwich lattice structures, in accordance to an exemplary embodiment of the invention.

[0037] FIG. 2 illustrates a flowchart of the complete process of the system for the selection of at least one optimal lattice structure, in accordance to an exemplary embodiment of the invention.

[0038] FIG. 3 illustrates a schematic view of various types of lattice structures in accordance to an example embodiment of the invention.

[0039] FIG. 4A illustrates a schematic view of a solid 3D model of a unit cell sandwich panel setup with both top and bottom plates, in accordance to an example embodiment of the invention.

[0040] FIG. 4B illustrates a schematic view of the unit cell meshing for FE analysis, in accordance to an example embodiment of the invention.

[0041] FIG. 4C illustrates a schematic view of the unit cell upon applying load and constraints for compression load simulation, in accordance to an example embodiment of the invention.

[0042] FIG. 4D illustrates a schematic view of the unit cell upon applying load and constraints for tension load simulation, in accordance to an example embodiment of the invention.

[0043] FIG. 4E illustrates a schematic view of a deformed unit cell during compression load simulation, in accordance to an example embodiment of the invention.

[0044] FIG. 4F illustrates a schematic view of a deformed unit cell during tension load simulation, in accordance to an example embodiment of the invention.

[0045] FIG. 5A illustrates a graphical representation depicting the stress generated by the various types of lattice structures, in accordance to an example embodiment of the invention.

[0046] FIG. 5B illustrates a graphical representation of the comparison of deformation for the various lattice structures obtained from the results of FEA, in accordance to an example embodiment of the invention.

[0047] FIGs. 6A-6B illustrate graphical representations between stress and strain of various lattice structures obtained from FEA results, in accordance to an example embodiment of the invention.

[0048] FIGs. 7A-7B illustrate schematic views of the sandwich lattice structure in an initial state and a deformed state under load, respectively, in accordance to an example embodiment of the invention.

[0049] FIG. 8 illustrates a flowchart of a method for operating the system to select the one or more lattice structures for designing sandwich lattice structures, in accordance to an exemplary embodiment of the invention.
Detailed invention disclosure:
[0050] Various embodiments of the present invention will be described in reference to the accompanying drawings. Wherever possible, same or similar reference numerals are used in the drawings and the description to refer to the same or like parts or steps.

[0051] The present disclosure has been made with a view towards solving the problem with the prior art described above, and it is an object of the present invention to provide a system and method that design optimal lattice structures for additively manufactured sandwich lattice structures.

[0052] According to an exemplary embodiment of the invention, FIG. 1 refers to a system 100 for selecting one or more lattice structures for designing sandwich lattice structures. The system 100 suggests a range of lattice settings for effective selection. The system 100 reduces manufacturing costs by minimizing the weight of materials and components to consume less fuel during transportation, especially in industries such as aerospace and automotive. The system 100 reduces material waste and weight while maintaining structural performance. The system 100 assists in making decisions by using multiple criteria for selecting an optimized lattice structure.

[0053] In one embodiment herein, the system 100 comprises a computing device 102 having a processor 104 and a memory 106 for storing one or more instructions executed by the processor 104. The computing device 102 is configured to receive user data from at least one user to design one or more lattice structures. In one embodiment herein, the user data includes at least one of a unit cell size, unit cell shape, and surface thickness.

[0054] In one embodiment herein, the computing device 102 is also configured to analyze the user data using finite element analysis (FEA) simulation for determining the effective mechanical properties of the one or more lattice structures. In one embodiment herein, the effective mechanical properties include at least one of relative density, Young's modulus, tensile strength, and compression strength.

[0055] In one embodiment herein, the computing device 102 is also configured to rank the one or more lattice structures by applying multi-criteria decision-making models, thereby listing the one or more lattice structures based on the ranking. In one embodiment herein, the at least two multi-criteria decision-making models include Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA).

[0056] In one embodiment herein, the computing device 102 is also configured to display the list of the one or more lattice structures based on the ranking for the at least one user to select at least one optimal lattice structure from the list of the one or more lattice structures based on predefined criteria. In one embodiment herein, the predefined criteria include user data of the unit cell, mechanical properties, unit cell topology, unit cell dimensions, relative density, deformation under load, and loading conditions.

[0057] In one embodiment herein, the computing device 102 is also configured to generate a 3D model of a sandwich lattice panel with the at least one optimal lattice structure selected by the at least one user. The computing device 102 is also configured to calculate the effective mechanical properties for a section of the 3D model that has to be filled with the at least one optimal lattice structure. The computing device 102 is configured to determine design parameters for the at least one optimal lattice structure based on the obtained effective mechanical properties. The computing device 102 is also configured to incorporate the at least one optimal lattice structure with the determined design parameters into the 3D model.

[0058] In one embodiment herein, the computing device 102 is also configured to perform finite element analysis (FEA) to determine FEA data that characterizes effective mechanical properties for the lattice design parameters provided by the at least one user. In one embodiment herein, the computing device 102 is configured to store the FEA data for the lattice design parameters in a database for determining the lattice design parameters and the effective mechanical properties of the lattice structure.

[0059] In one embodiment herein, the computing device 102 is also configured to create the 3D model of the sandwich lattice panel with the at least one optimal lattice structure to produce an object using a 3D printer 108. In one embodiment herein, the computing device 102 is also configured to perform the finite element analysis (FEA) simulation on the product in order to provide the mechanical properties to the user for real-time usage. The 3D model with the at least one optimal lattice structure may be saved as a document or used to generate another representation of the model, like an STL file suitable for additive manufacturing.

[0060] According to another embodiment of the invention, FIG. 2 refers to a flowchart 200 of a complete process of the system 100 for the selection of at least one optimal lattice structure. In one embodiment herein, first step 202 involves creating a 3D model of the unit cell. The unit cell is the smallest repeating element in the lattice structure. Later, at step 204, the dimensions of the unit cell are determined based on the desired properties of the lattice structure, such as the porosity, strut/plate thickness, and cell size. The porosity of the lattice structure is the fraction of the volume of the lattice that is occupied by air. The strut or plate thickness is the thickness of the struts or plates that make up the lattice structure. The cell size is the size of the individual cells in the lattice structure.

[0061] In one embodiment herein, at step 206, the type of lattice structure is selected based on the desired properties of the sandwich panel, such as strength, stiffness, and weight. Once the type of lattice structure has been selected, the lattice is generated by the processor 104 of the computing device 102 at step 208. The processor 104 uses the unit cell dimensions and the type of lattice structure to generate the 3D model of the lattice. FEA is performed on the generated lattice structure to simulate the performance of the lattice structure under load conditions. The results of the FEA are used to optimize the cell size and other parameters of the lattice structure to achieve the desired properties.

[0062] In one embodiment herein, at step 210, the geometry of the sandwich panel, the boundary conditions, and the loading conditions are inputted into the computing device 102. The boundary conditions define how the sandwich panel is fixed in place. The loading conditions define the forces and moments that are applied to the sandwich panel. Later, at step 212, the mesh is generated by dividing the 3D model of the sandwich panel into a number of smaller elements. The mesh is necessary for the FEA to be performed.

[0063] Later, at step 214, the FEA is performed to obtain the results. The results of the FEA include the stresses, strains, and displacements in the sandwich panel. The results of the FEA are used to validate the 3D model of the sandwich panel with the lattice at step 218. The model is validated to ensure that it meets the required standards, such as strength, stiffness, and weight, at step 216. If the sandwich panel requires multiple layers of lattice, the 3D model is updated to include the additional layers.

[0064] Later, at step 220, structural analysis is performed to ensure that the sandwich panel with the multilayer lattice meets the required standards. If the sandwich panel with the multilayer lattice meets the required standards at step 222, the process is complete, and the sandwich panel with the multilayer lattice is validated and fabricated using 3D printing at step 224. For example, if the sandwich panel with the multilayer lattice does not meet the required standards at step 222, the process is continued to step 204 and repeated until the sandwich panel with the multilayer lattice meets the required standards.

[0065] According to another embodiment of the invention, FIG. 3 refers to a schematic view 300 of various types of lattice structures. In one embodiment herein, the lattice creation within the computing device 102 can be controlled through different design variables. The computing device 102 offers various lattice topology types for creating lattices. There are different types of lattice topologies available in the computing device 102, including strut-type lattices like Simple Cubic Cell (SCC), Body-Centered Cubic Cell (BCC), Face-Centered Cubic Cell (FCC), diamond, truncated cube, truncated octahedron, Kelvin cell, iso-truss, re-entrant, Octet, honeycomb structures like Triangular, Triangular rotated, Hexagonal, Hexagonal rotated, Square, Square rotated, Re-entrant, and Triply Periodic Minimal Surface (TPMS) structures like Gyroid, Schwarz, Diamond, Lidinoid, SplitP. Each topology type exhibits unique structural behavior, leading to different optimal designs when utilized for structural component optimization. Additionally, structural behavior varies with lattice orientation, unit size, and thickness, thus expanding the scope of possible solutions.

[0066] According to another embodiment of the invention, FIGs. 4A-4F refer to schematic views of a solid 3D model of the unit cell sandwich panel setup with both top and bottom plates. In one embodiment herein, the setup of the FEA involves various numerical models used to compute the material properties of a specific lattice. The lattice settings encompass aspects like lattice topology, porosity, and isotropic solid material parameters, as shown in FIG. 4A.

[0067] In one embodiment herein, meshing of the unit cell for FE analysis is depicted in FIG. 4B. In one embodiment herein, loading and constraints are applied to the unit cell for compression load simulation, as depicted in FIG. 4C. In one embodiment herein, loading and constraints are applied to the unit cell for tension load simulation, as depicted in FIG. 4D. In one embodiment herein, the deformed unit cell is observed under compression load simulation, as depicted in FIG. 4E. In one embodiment herein, the deformed unit cell is observed under tension load simulation, as depicted in FIG. 4F.

[0068] In one embodiment herein, the lattice corresponds to a sample unit cell of length of around 10 mm and a diameter of around 1 mm. The FEA is generated, and a single lattice behavior model is established for each distinct lattice topology. A collection of approximations for lattice behavior is computed for the next lattice behavior model, utilizing the FEA and varying lattice properties such as different porosity levels and isotropic solid material properties.

[0069] In another embodiment, the above-mentioned process involves the use of Representative Volume Element (RVE) to estimate the structural behavior of a particular lattice with diverse lattice design. The variations in structural behavior resulting from different design variables are systematically studied to create a database of RVEs. This enables the construction of RVEs tailored to different lattice design variables, enhancing the modeling and analysis of lattice structures.

[0070] According to another exemplary embodiment of the invention, FIG. 5A refers to a graphical representation 500 depicting the stress generated by the various types of lattice structures. Based on the stress generated in the unit cells, octet (strut), triangular honeycomb rotated, and diamond (TPMS) lattices show less stress generated in the lattices, thus showing their strength to bear high loads compared to other lattices. The other lattices, such as the square honeycomb, re-entrant honeycomb, and hexagonal honeycomb, generate more stress, which means they are weaker and cannot bear as much load. This information may be useful for the user to design structures that need to be strong and lightweight, such as electric vehicles or aircraft. By using lattices that generate less stress, they can create structures that are both strong and efficient.

[0071] According to another embodiment of the invention, FIG. 5B refers to a graphical representation 502 of the comparison of deformation for the various lattice structures obtained from the results of FEA. The octet (strut), re-entrant (honeycomb), and split P (TPMS) lattice structures exhibit less deformation than the other tested lattice structures. This means that these three lattice structures have the highest strength and are most capable of withstanding a load. This is likely due to the fact that these three lattice structures have a more complex and interconnected geometry than the other lattice structures. This more complex geometry distributes the load more evenly across the lattice structure, which reduces stress concentration and deformation.

[0072] Additionally, the octet (strut), re-entrant (honeycomb), and split P (TPMS) lattice structures are arranged in a way that maximizes their bending stiffness. This bending stiffness prevents the lattice structures from buckling under load, which also contributes to the high strength of these lattice structures. Therefore, the results indicate that the octet (strut), re-entrant (honeycomb), and split P (TPMS) lattice structures are the most promising candidates for applications where high strength and low deformation are required.

[0073] According to another embodiment of the invention, FIGs. 6A–6C refer to graphical representations 600, 602, and 604 between stress and strain for various lattice structures obtained from FEA results. The stress vs. strain graph 600 for a strut-based lattice is shown in FIG. 6A. The graph 600 shows a linear elastic behavior up to a certain strain, beyond which the lattice yields and the stress increases non-linearly. The yield strength and the ultimate strength of the lattice depend on the specific geometry of the lattice and the material properties of the struts.

[0074] The stress vs. strain graph 602 for a honeycomb lattice is shown in FIG. 6B. The graph 602 is similar to the graph 600 for a strut-based lattice, but the honeycomb lattice has a higher yield strength and ultimate strength. This is because the honeycomb lattice has a more efficient distribution of material with less empty space.

[0075] The stress vs. strain graph 604 for a TPMS lattice is shown in FIG. 6C. The TPMS lattice is a type of triply periodic minimal surface (TPMS) lattice. TPMS lattices are highly efficient lattice structures with very high strength and stiffness. The stress vs. strain graph 604 for a TPMS lattice shows a linear elastic behavior up to a very high strain, beyond which the lattice yields and the stress increases non-linearly.

[0076] In one embodiment herein, Table 1 represents the ranks obtained for the lattice structures using the optimization techniques TOPSIS and GRA.

[0001] Table 1:
Type GRA rank TOPSIS rank
SCC 18 22
BCC 20 19
FCC 17 16
Diamond strut 15 17
octet 12 12
Truncated Cube 19 20
Truncated Octahedron 16 15
Kelvin 21 21
Iso-truss 13 14
Re-entrant strut 22 18
Triangle 2 2
Rot. Triangle 1 1
Hexagon 14 13
Rot. Hexagon 7 6
Re-entrant honeycomb 8 7
Square 9 8
Rot. Square 11 11
Gyroid 10 10
Schwarz 3 3
diamond TPMS 6 9
Lidinoid 4 4
Split P 5 5

[0077] According to Table 1, the ranks of the various lattice structures are listed based on their mechanical properties. In one embodiment herein, the mechanical properties may change according to the size of the unit cell. The user selects at least one optimal lattice structure from the list of the various lattice structures based on the predefined criteria. The user may then initiate real simulation of the selected at least one optimal lattice structure using the computing device for detailed examination and optimization.

[0078] According to another embodiment of the invention, FIGs. 7A–7B refer to schematic views of the sandwich lattice structure in an initial state and a deformed state under load. The 3D model of the sandwich lattice panel undergoes further FEA simulation using lattice design parameters. This simulation aims to determine effective mechanical properties, such as tensile strength, relative density, and compressive strength. These determinations are based on characterization data that approximates the previously described FEA experimental simulation results conducted for single-cell design lattices.

[0079] According to another embodiment of the invention, FIG. 8 refers to a flowchart 800 of a method for selecting one or more lattice structures using the system 100. First, at step 802, the computing device 102 receives user data from the at least one user to design one or more lattice structures. At step 804, the computing device 102 analyzes the user data using finite element analysis (FEA) simulation to determine the effective mechanical properties of the one or more lattice structures. At step 806, the computing device 102 ranks the one or more lattice structures by applying multi-criteria decision-making models, thereby listing the one or more lattice structures based on the ranking.

[0080] At step 808, the computing device 102 displays the list of the one or more lattice structures based on the ranking for the at least one user to select at least one optimal lattice structure from the list of the one or more lattice structures. Further, at step 810, the computing device 102 generates a 3D model of a sandwich lattice panel with the at least one optimal lattice structure selected by the at least one user.

[0081] Numerous advantages of the present disclosure may be apparent from the discussion above. In accordance with the present disclosure, the system 100 designs a lattice structure for additive manufacturing. The proposed system 100 suggests a range of lattice settings for effective selection. The proposed system 100 reduces manufacturing costs by minimizing the weight of materials and components to consume less fuel during transportation, especially in industries such as aerospace and automotive. The proposed system 100 reduces material waste and weight while maintaining structural performance. The proposed system 100 assists in making decisions by using multiple criteria for selecting an optimized lattice structure. The proposed system 100 optimizes lattice structure, thereby assisting in preparing products that outperform competitors in the market.

[0082] The proposed system 100 gains a competitive advantage by producing lighter, stronger, and more efficient products, thereby attracting customers seeking high-performance solutions. The proposed system 100 reduces material waste, aligns with sustainability goals, contributes to resource efficiency, and reduces the environmental impact of the manufacturing process. The proposed system 100 minimizes the need for repetitive simulations and speeds up the design process. The proposed system 100 assists engineers in evaluating different lattice structures and enabling design iterations more rapidly.

[0083] It will readily be apparent that numerous modifications and alterations can be made to the processes described in the foregoing examples without departing from the principles underlying the invention, and all such modifications and alterations are intended to be embraced by this application.
, Claims:CLAIMS:
I/We Claim:
1. A system (100) for selecting one or more lattice structures, comprising:
a computing device (102) having a processor (104) and a memory (106) for storing one or more instructions executed by the processor (104), wherein the computing device (102) is configured to:
receive user data from at least one user to design one or more lattice structures;
analyze the user data using finite element analysis (FEA) simulation for determining effective mechanical properties of the one or more lattice structures;
rank the one or more lattice structures by applying multi-criteria decision-making models, thereby listing the one or more lattice structures based on the ranking;
display the list of the one or more lattice structures based on the ranking for the at least one user to select at least one optimal lattice structure from the list of the one or more lattice structures based on predefined criteria; and
generate a 3D model of a sandwich lattice panel with the at least one optimal lattice structure selected by the at least one user.
2. The system (100) as claimed in claim 1, wherein the computing device (102) is configured to:
calculate the effective mechanical properties for a section of the 3D model that has to be filled with the at least one optimal lattice structure;
determine design parameters for the at least one optimal lattice structure based on the obtained effective mechanical properties; and
incorporate the at least one optimal lattice structure with the determined design parameters into the 3D model.
3. The system (100) as claimed in claim 1, wherein the user data includes at least one of a unit cell size, unit cell shape, and surface thickness.
4. The system (100) as claimed in claim 1, wherein the effective mechanical properties include at least one of relative density, Young's modulus, tensile strength and compression strength.
5. The system (100) as claimed in claim 1, wherein the predefined criteria include input parameters of the unit cell, mechanical properties, unit cell topology, unit cell dimensions, relative density, deformation under load and loading conditions.
6. The system (100) as claimed in claim 1, wherein the computing device (102) is configured to:
perform finite element analysis (FEA) to determine FEA data that characterizes effective mechanical properties for the lattice design parameters provided by the at least one user; and
store the FEA data for the lattice design parameters in a database for determining the lattice design parameters and the effective mechanical of the lattice structure.
7. The system (100) as claimed in claim 1, wherein the at least two multi-criteria decision-making models include Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA).
8. The system (100) as claimed in claim 1, wherein the computing device (102) is configured to:
create the 3D model of the sandwich lattice panel with the at least one optimal lattice structure to produce an object using a 3D printer (108); and
perform the finite element analysis (FEA) simulation on the product in order to provide the mechanical properties to the user for real time usage.
9. A method for selecting one or more lattice structures using a system (100), comprising:
receiving, by a computing device (102), user data from at least one user to design one or more lattice structures;
analyzing, by the computing device (102), the user data using finite element analysis (FEA) simulation for determining effective mechanical properties of the one or more lattice structures;
ranking, by the computing device (102), the one or more lattice structures by applying multi-criteria decision-making models, thereby listing the one or more lattice structures based on the ranking;
displaying, by the computing device (102), the list of the one or more lattice structures based on the ranking for the at least one user to select at least one optimal lattice structure from the list of the one or more lattice structures; and
generating, by the computing device (102), a 3D model of a sandwich lattice panel with the at least one optimal lattice structure selected by the at least one user.