Integrated virtual product and process design for casting components

Abstract

To provide methods and systems for improving a process of casting an object by predicting a probability distribution of local properties of a casting.SOLUTION: A method comprises using a single casting process simulation (2) and a process variability parameter (40) to predict a probability distribution of local microstructure-based mechanical properties (8) and local damage factors (9); and applying the corresponding local damage factors (9) to the local microstructure-based mechanical properties (8) at each part of the casting to predict local weakening in the corresponding material performance and a probability distribution thereof. The method further includes statistical analysis for a single cast part or a virtual experimental design method using parametrized sets of input variables in order to determine a local defect probability and process capability for each part of the casting.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a process for casting an object into a cavity in a mold. In particular, the disclosure relates to improving such a casting process by predicting local characteristics of the casting through integrated casting process simulation to optimize the casting design at the design stage, and applying statistical analysis to optimize mold and tool design and casting process conditions at the production stage.

[0002] Casting is a manufacturing process in which a liquid material is poured into a mold with a hollow cavity of the desired shape. Depending on the local heat exchange with the environment (mold, air, etc.), the liquid material cools and solidifies according to the phase transformation physics specific to the material. The solidified part, also known as a casting, is removed from the mold or the mold is broken to complete the process. Casting is frequently used to produce complex shapes that would be difficult or uneconomical to produce otherwise. If the casting material is a metal, this metallic material is heated to a pouring temperature, which is higher than the melting temperature, and poured into a mold. The mold may also contain runners and feeders that allow for the controlled filling and solidification of the casting. The metal is then cooled in the mold, the metal solidifies, and the solidified part (casting) is removed from the mold at a metal temperature and process time depending on the process and material. In a subsequent operation, the extra parts required for the casting process (such as runners and feeders) are removed.

[0003] Simulation of the metal casting process can use several techniques, including numerical methods, to calculate the quality of the cast part, taking into account the filling of the mold, solidification, and further cooling. It can also provide a quantitative prediction of the cast mechanical properties at each point during the entire casting process and additional processing steps such as heat treatment. Such simulation can therefore predetermine the quality of the cast part before production starts. The casting method can then be designed with respect to the required part quality and properties. This has benefits beyond reducing pre-production sampling, since an accurate layout of the entire casting system can also lead to savings in energy, material, and tooling. Simulation can support product design at the design and manufacturing stages for the determination of the casting method, including the fabrication of the molds and tools, and subsequent heat treatment and finishing decisions. This can save costs, resources, and time throughout the casting production route.

[0004] In the design of castings for a particular part, a certain service life must be taken into account. For this purpose, mechanical properties of materials based on a given standard are generally used. Casting properties are related to the basic material and the process conditions for producing the part. In general, there are two main parameters that affect the final mechanical properties: the local structure (local microstructure) of the cast part, which is determined by the alloy composition, metal treatment and local solidification conditions; and the presence or absence of local defects, which depend on the process layout and process conditions. Both the local structure and the local defects determine the final properties of the part.

[0005] When designing a casting, certain quality requirements are specified that must be met during production. These requirements are expressed in property levels for specific areas of the casting that meet the expected service loads. This is done to minimize the risk of failure during service. Since the final process conditions are not known during design, the relationship between process, structure (microstructure) and final properties is considered a risk. This uncertainty results in a safety factor that is applied to the part design, reducing the optimal potential of the cast material. The result is usually a part that is too heavy for the required service loads.

[0006] To achieve a more optimized lightweight casting design, it is proposed to take into account the locally varying properties obtained from the process simulation and use them in the stress analysis. For this purpose, the minimum local properties expected in the casting must be statistically proven at the design stage without experimental verification.

[0007] Furthermore, when the design of the cast part is fixed, decisions regarding the method to be applied and the design of the mould or tool, as well as the setting of robust process conditions, are required. This leads to a significant increase in the variety of parameters that affect the part quality and the associated mechanical properties. To achieve robust mould and tool design and process conditions, statistically proven information is required about the parameters that affect the expected final part properties. The aim is to determine the optimal nominal operating points and robust process windows that ensure the achievement of the required mechanical properties of the part according to the specifications. This information allows the casting and process design based on the expected part quality. It also allows the manufacturer to establish operating conditions that help to ensure a seamless and short ramp-up phase. It also allows to provide quantitative information about the expected reliability of the part before the first casting is produced.

[0008] Some commercially available simulation software solutions can provide a means to predict local mechanical properties of cast objects to a limited extent, such as providing information about porosity that may form during the metal casting process, or about mechanical properties based on the expected microstructure. However, these known simulation software solutions are still unable to provide robust statistical evidence regarding the changing local properties of a given part region of a cast object in such a way that the obtained information can be used for the design of robust cast parts and for the optimization of the overall manufacturing process.

[0009] The present invention aims to provide an improved method and system that overcomes or at least mitigates the aforementioned problems, and aims to provide a method and system for improving the process of casting an object by predicting the probability distribution of local properties of the casting.

[0010] These and other objects are achieved by the features of the independent claims. Further implementations will become apparent from the dependent claims, the description and the drawings.

[0011] According to a first aspect, there is provided a computer-implemented method for improving a process of casting an object by predicting a probability distribution of local mechanical properties of the casting, the method comprising: obtaining input variables defining at least one of a design of the casting, a design of a mold or tool used for casting, a composition of the casting material, a pre-treatment of the casting material, casting process conditions, and post-treatment process conditions; obtaining process variation parameters that define the variation of the process conditions as a probability distribution; performing a simulation of the casting process on a computer device using a numerical model of the casting process; the numerical model is configured to predict local microstructure of each portion of the casting and local defects mapped to a design of the casting as a function of the input variables, the method further comprising: calculating local microstructure-based mechanical properties for each portion of the casting as a function of the local microstructure and calculating a probability distribution of the local microstructure-based mechanical properties as a function of the process variation parameters; calculating a local damage factor for each portion of the casting as a function of the local defects and calculating a probability distribution of the local damage factor as a function of the process variation parameters to represent the probability and magnitude of a detrimental effect of the local defects on local material performance in each portion of the casting; calculating adjusted local mechanical properties by applying a local damage factor to each of the local microstructure-based mechanical properties in each portion of the casting, the local damage factor representing a local weakening of any of the material properties; calculating, for each portion of the casting, a probability distribution of the adjusted local mechanical properties as a function of a combination of the probability distribution of the local microstructure-based mechanical properties and the probability distribution of the local damage factors; Includes.

[0012] This first approach provides an improved method for predicting the outcome of a casting process by using simulated information of microstructure and local defects in a probabilistic manner from a single simulation. This single simulation provides a robust statistical analysis of the local mechanical properties of the cast object, taking into account the effects of the probability and distribution of both the predicted local defects and the local microstructure. This approach differs from known prior art methods that aim at a deterministic prediction of the microstructure and mechanical properties. In known prior art, the statistical analysis is generally based on repeated deterministic simulations. This approach is achieved according to the claimed method by using process variation values ​​as input parameters and taking into account the process variation as described above, as well as the variation revealed in repeated tests.

[0013] The method includes calculating, for each part of the casting, local damage factors that represent the various detrimental effects of predicted local defects on local material performance and variability, thereby improving the prediction of the local weakening of mechanical performance caused by these local defects for each part of the casting.

[0014] In one example implementation of the first perspective, performing the simulation includes using an integrated microstructural prediction model of mold filling and solidification during casting.

[0015] In some embodiments, the integrated microstructure prediction model includes a numerical model that integrates equations that define phase formation, solidification kinetics, segregation, latent heat and solidification, and thermal balance of the mold filling and solidification process.

[0016] In some embodiments, the integrated microstructure prediction model provides output regarding local microstructure including at least one of local information regarding grain size, primary and intermetallic phase distribution, and associated phase sizes of a given portion of the casting.

[0017] In some embodiments, the integrated microstructure prediction model calculates local microstructure-based mechanical properties and associated local stress-strain curves that describe the mechanical performance, such as yield strength, tensile strength, elongation, etc., of a given portion of the casting.

[0018] In one example of a further implementation of the first approach, calculating the adjusted local mechanical properties includes applying each of the local damage factors to a local stress-strain curve in each portion of the casting to calculate a respective local weakening of mechanical performance in each portion.

[0019]

[0008] In some embodiments, calculating the adjusted local mechanical properties comprises: determining a simulated local defect map by mapping the simulated local defects onto a geometric model of the casting; defining one or more evaluation regions within the geometric model that can reflect a virtual local tensile strength test on the local defect map; subjecting each local defect to said local tensile strength test and determining a local stress-strain curve; Includes.

[0020] In one further implementation of the first approach, the predicted local defects include attributes of a given portion of the casting that may locally degrade the material performance, the attributes being e.g.

[0021] The local damage factor is calculated as a function of these attributes, such as the size, shape, or distribution of defects, including entrapped air and gas, cold shuts, weld seams, porosity, and other detrimental inclusions.

[0022] In some embodiments, the local damage factor for each portion of the casting is calculated as a function of the size and other characteristics of local defects in a given sampling area. Each sampling area may correspond to a sub-area of ​​the casting based on a sampling criterion for determining the respective damage factor. The sampling criterion may reflect, for example, the dimensions of a tensile strength sample.

[0023] According to a second aspect, there is provided a computer-implemented method for improving a casting process, the method comprising: obtaining a set of fixed input variables defining a nominal operating point of the casting process according to nominal process conditions; obtaining process variation parameters defining a probability distribution of variation of the nominal process conditions; selecting at least one evaluation area of ​​the casting; determining a design performance of the at least one evaluation area; In this method, determining the design performance comprises: simulating the casting process using a numerical model of the casting process using the fixed input variables to predict, at each point in the evaluation region, a probability distribution of local microstructure and a probability distribution of local defects as a function of the process variation parameters; calculating, at each point of the evaluation region, a probability distribution of a mechanical property based on the local microstructure as a function of the probability distribution of the local microstructure; calculating a probability distribution of a local damage factor as a function of the probability distribution of the local defect at each point of the evaluation area; calculating, at each point of the evaluation region, a probability distribution of an adjusted local mechanical property as a function of a combination of the probability distribution of the local microstructure-based mechanical property and the probability distribution of the local damage factor; This is carried out by.

[0024] In some embodiments, the evaluation region corresponds to an entire geometric model of the casting.

[0025] In some embodiments, each sampling area may correspond to a sub-area of ​​the casting based on sampling criteria for determining a respective damage factor, which may reflect, for example, the dimensions of a tensile strength sample.

[0026] In some embodiments, each evaluation zone corresponds to a sub-zone of the casting based on each applied load level.

[0027] In some embodiments, the load levels correspond to either static cases, dynamic cases, and / or crash-related load cases.

[0028] In some embodiments, the method further includes obtaining a set of fixed input variables that define nominal process conditions based on the nominal operating point.

[0029] In some embodiments, the set of fixed input variables defines the composition of the cast material, the current part design, the design of the tooling used for casting, and the nominal process conditions for filling and cooling.

[0030] In some embodiments, determining the design performance includes determining an adjusted local tensile strength in each evaluation zone and applying a statistical evaluation based on local stress-strain curves corresponding to the local tensile strength samples for each of the evaluation zones, the statistical evaluation defining an adjusted local mechanical property variability based on the variability of one of the set of input variables.

[0031] In one example of a further implementation of the first approach, determining the design performance of the at least one evaluation area includes determining a minimum expected local mechanical property of the at least one evaluation area of ​​the casting based on the probability distribution of the adjusted local mechanical property at each point of the evaluation area.

[0032] In one example of a further implementation of the first approach, the method further comprises: Mapping at least one of the determined expected local mechanical property minimum and the adjusted local mechanical property distribution and / or probability distribution onto a computer-aided engineering (CAE) model (e.g., a finite element model) defining a geometry of the casting and at least one evaluation region; analyzing the CAE model, such as using finite element analysis (FEA), to evaluate mechanical performance based on user requirements defining local loads applied to at least one evaluation region; Includes.

[0033] In some embodiments, the method further comprises tuning the CAE model and / or determining a set of tuned input variables to meet user requirements.

[0034] In one example of a further implementation of the second approach, the method further comprises: obtaining a set of parameterized input variables that define the variability of the input variables; performing a design of experiments by running multiple simulations of a casting process using the numerical model of the casting process for possible combinations of the set of parametric input variables to predict local microstructural variations and local defect variations in each portion of the casting; determining a statistical strength assessment for the set of parametric input variables for each portion of the casting; determining said statistical intensity assessment by calculating, for each portion of the casting, a probability distribution variance of a local microstructure-based mechanical property as a function of the local microstructure variance and the process variability parameter; calculating, for each portion of the casting, a variance in a probability distribution of local damage factors as a function of the variance in the local defects and the process variability parameter; calculating an adjusted local mechanical property probability distribution variance as a combined function of the variance of the local microstructure-based mechanical property probability distribution and the variance of the local damage factor probability distribution; This is done by.

[0035] In one example of a further implementation of the first approach, the method includes determining, for each part of a casting, for each of a set of parameterized input variables, a local defect probability as a function of local defect variability and process variability parameters.

[0036] In one example of a further implementation of the first approach, the method includes a full factorial assessment by running a simulation of the casting process for all possible combinations of a set of parameterized input variables.

[0037] In another example of an implementation of the first approach, the method includes a limited evaluation by reducing a set of parameterized input variables based on statistical support, and running multiple simulations of the casting process for the reduced set of input variables and a fixed set of input variables.

[0038] In one example of a further implementation of the first approach, the method includes determining information regarding design performance for the set of parameterized input variables to define a statistical robustness analysis of the casting process, the determining including: Main effect and correlation diagrams representing the influence of various input parameters on the quality of cast parts; minimum values ​​and distribution of mechanical properties expected for said castings based on predefined quality criteria; a robust process window for the set of input variables represented by said parameters; This is done by determining:

[0039] In some embodiments, determining the design performance for a set of parameterized input variables includes calculating a normal distribution of local mechanical properties for each portion of the casting.

[0040] In some embodiments, determining a robust process window includes adjusting a process window defined by a set of parameterized input variables by reducing process variability for process robustness.

[0041] In some embodiments, determining the robust process window includes adjusting an operating point defined by a set of parameterized input variables to determine an optimal operating point.

[0042] In one example of a further implementation of the first approach, the method further includes evaluating the effect of variation in a set of parameterized input variables on mechanical properties associated with expected part quality using main effect diagrams and correlation diagrams.

[0043] In one example of a further implementation of the first approach, the method further comprises: comparing said minimum values ​​and / or said distribution of said expected mechanical properties with predefined user requirements regarding quality criteria of said castings; determining a set of parameterized input variables tuned to meet the user requirements; Further includes.

[0044] In one example of a further implementation of the first approach, the method further comprises: obtaining at least one of a set of fixed input variables or a set of parameterized input variables via an input / output interface of a computing device; outputting a determined result of at least one executed method step on a display of said computing device; Includes.

[0045] According to a second aspect, a computer system is provided, the computer system including an input / output interface; Display and; a non-volatile machine-readable storage medium containing a computer program product; at least one processor operable to execute the computer program product, to interact with the input / output interface and the display, and to perform processing according to any of the above aspects of the first aspect; Equipped with.

[0046] According to a third aspect, there is provided a computer program product encoded on a non-volatile machine-readable storage medium and operable to cause a processor to perform operations of a method according to any of the above implementations of the first aspect.

[0047] These and other aspects will become more apparent from the examples described below. [Brief description of the drawings]

[0048] Various aspects, embodiments and implementations are described in detail below with reference to example embodiments illustrated in the drawings. [Figure 1] FIG. 1 is a flow diagram showing a method for predicting local mechanical properties of a cast object according to a first approach. [Diagram 2] FIG. 1 is a flow diagram illustrating some aspects of an integrated microstructural prediction model for mold filling and solidification during a casting process according to an example embodiment of the first perspective. [Diagram 3]FIG. 1 is a flow diagram showing a method for determining local stress-strain curves in each portion of an object to calculate adjusted local mechanical properties using an evaluation area as a virtual local tensile strength test according to an example embodiment of the first approach. [Figure 4] FIG. 1 is a flow diagram illustrating a method for determining design performance for an evaluation region of a cast object having different loading requirements and feeding back the results of the design performance as input for a load analysis according to an example embodiment of the first approach. [Diagram 5] FIG. 1 is a flow diagram illustrating a method for determining design performance using a set of parameterized input variables in a virtual Design of Experiments (DoE) approach, according to an example embodiment of the first perspective. [Figure 6] FIG. 1 is a scatter plot showing hypothetical DoE results for predicted mechanical properties when two design parameters are varied. [Figure 7] FIG. 2 is a block diagram illustrating a computer system for predicting local mechanical properties of a cast object according to a second perspective. Detailed explanation

[0049] When designing a new cast part, the design is evaluated based on the load cases expected during its useful life, focusing on the required function. The cast part must perform multiple functions. Therefore, various load cases are considered using CAE with Finite Element Analysis (FEA). This is a computerized method to predict how a product will react to real forces, vibrations, heat, fluid flow, and other physical influences. FEA can show whether the product will break, wear out, or move as designed, and requires input of static, dynamic (durability), or crash-related properties. With regard to material specifications, the design process usually relies on available standards for a given material and process. Deviations of the manufactured part from the accepted standard are only allowed under quality system considerations. That is, specific areas of various loads in the part and the associated acceptable defects are specified, and manufacturing must meet them. This information becomes part of the specification at the time of procurement. Depending on the company's standards, a safety factor is added for safety reasons, lowering the allowable load of the cast part.

[0050] As a result, cast parts are usually over-dimensioned for two reasons: first, by using only standard data instead of local properties, and second, by using additional safety factors to deal with unknown local properties.

[0051] The innovation described herein uses predictions (material performance and local defects) from virtual process simulations to predict local mechanical properties, allowing the design process of cast parts to correlate specific part load levels with the predicted local properties that can be met based on specific process conditions, allowing for lower safety factors and taking into account the predicted local properties to reduce part weight.

[0052] 1 is a flow diagram showing the main steps of a method for improving such a process of designing a cast object according to the present disclosure by predicting, for each part of the object, a probability distribution of the local mechanical properties of the object, taking into account both the predicted local microstructure 6 and local defects 7.

[0053] The input for the casting process simulation 2 of a cast part may consist of information regarding the part design, the composition of the selected casting material, the casting or mold design, any pre-treatment of the casting material, casting and post-treatment process conditions.

[0054] These design parameters are typically nominal and do not account for the inherent variations and fluctuations of any manufacturing process. The process variation parameters 40 add probability distributions that describe the predicted reliability of the process in view of the variations in process conditions.

[0055] At this stage, the casting design, mold design, and process conditions have not been finalized, so assumptions are made based on past references and best practices.

[0056] Additionally, as shown in more detail in FIG. 4, specific evaluation areas 13 or zones may be defined that require specific load levels.

[0057] The numerical model 3 applied in the simulation 2 describes the filling and solidification of the cast part. During filling and solidification, an integrated microstructural prediction model 5 can be applied, which is shown in detail in Figure 2 and will be explained later.

[0058] The output of these numerical models consists of two components: a predicted local microstructure6 describing local information on grain size, primary and intermetallic phase distribution, and associated phase sizes at a given location in the cast object, and the associated material performance in terms of mechanical properties based on the local microstructure, which describe the fundamental behavior of the material during use.

[0059] Meanwhile, local defects 7 are also predicted, which are mapped to the design of the cast object as a function of the input variables 1. The predicted local defects 7 include attributes of a given part of the cast object that may locally degrade the material performance. The attributes may include, for example, entrapped air or gas, cold shuts, weld seams, porosity, and other detrimental inclusions.

[0060] Based on the evaluation of the local microstructure 6, the method can predict local microstructure-based mechanical properties 8 based on the local microstructure 6 and the associated local stress-strain curve 81, as shown in detail in Figure 3. The mechanical properties 8 describe the mechanical performance such as yield strength, tensile strength and elongation of a given portion of the object being cast.

[0061] The predicted local defects 7 are also rated in terms of their detrimental effect on material performance by a local damage factor 9. As outlined above, defects can refer to individual detrimental weak points in the material, such as entrapped air or gas, cold shuts, weld seams, porosity, other detrimental inclusions, or combinations of these. The local damage factor 9 can be a function of the size, shape, distribution of the defects, or can be a function of other characteristics of the predicted individual defects 7.

[0062] A statistical analysis of all defects within a sampling area is performed to ensure that the predicted defects represent a probability distribution for a given sampling area (e.g., the size of a tensile strength sample). The calculated defect probability distribution is used to determine a damage factor for each sampling area.

[0063] The local damage factor 9 is then applied locally to the predicted microstructural stress-strain curve 81 to reduce the optimum material performance resulting in lower tensile strength and ductility values, thereby locally reducing the respective mechanical performance at any location on the part.

[0064] The final output of the illustrated flow is an overall assessment of the part in terms of probability distributions of adjusted local mechanical properties 10 in each part of the cast. This assessment is a function of the process variation parameters 40 and the input variables 1, and involves the calculation of intermediate probability distributions of mechanical properties 8 and local damage factors 9 based on the local microstructure.

[0065] The above methodology can be applied in a single simulation 2 for a given part and process conditions to provide statistically ensured, tailored local mechanical properties 10 that are used in part design to avoid unnecessary safety margins that lead to increased part weight.

[0066] As shown in Figure 2, an integrated microstructure prediction model 5 can be applied during the filling and solidification simulation 3. The integrated microstructure prediction model 5 provides the link between the input parameters 1, such as material specifications and process conditions, the geometrical 3D model 4, the metallurgical treatment and the resulting local microstructure 6, the local defects 7 and the final microstructure (mechanical properties based on the microstructure). The integrated microstructure prediction model 5 comprises an integrated numerical model with equations defining the equilibrium phases 51, made based on the nominal material composition, the growth rates of all phases 52, the segregation of all components involved 53, the release of latent heat and solidification 54 for the mold filling and solidification process at any point of the cast object and any time of the process.

[0067] The output of this numerical model is the local microstructure, such as the phase amount and size of the primary and intermetallic phases, providing information about the local microstructure 6 and local defects 7 occurring in the cast component.

[0068] As shown in FIG. 3 , calculating the adjusted local mechanical property 10 may include determining a local defect map 71 by mapping local defects 7 to a design of the object to be cast, applying a method for determining an adjusted local tensile strength 81 using tensile test or evaluation areas 82 in the object to be cast based on the local microstructure map 6 and the defect map 71, and applying each local defect 7 to the local tensile strength test or evaluation areas 82 to determine a local stress-strain curve 81.

[0069] In particular, local microstructure based mechanical properties 8 are calculated based on the local microstructure 6 and the local stress-strain curves 81 associated with the local microstructure 6, which describe the associated material performance, and to which local damage factors 9 are applied at each portion of the object being cast to calculate the local weakening of mechanical performance at each portion of the object.

[0070] The method uses the predicted information to calculate adjusted local mechanical properties 10 of the entire cast part for a given condition. The local mechanical properties 10 are defined in the form of adjusted values ​​of the stress-strain curve. These are shown in FIG. 3 as adjusted positions of dots along a particular stress-strain curve 81 at an exemplary location of the object to be cast. The optimal material performance of the microstructure-based mechanical properties 8 (the dot at the top right corner of the curve) is reduced to the adjusted mechanical properties 10 (the dot to the left of the optimal performance) at any location of the part, i.e., tensile strength and elongation are reduced to lower values.

[0071] The above methodology, in combination with the process variations 40 defined above, can be applied to a single simulation for a given object and process conditions, thus providing statistically guaranteed local mechanical properties that can be used in part design to avoid unnecessary safety margins that lead to increased part weight.

[0072] The user can also decide on which parts of the part the analysis should be performed, the so-called Evaluation Areas (EV) 13. The analysis of all positions in each Evaluation Area 13 results in a statistically proven minimum mechanical property, which can be used as input for further analysis of the part performance, e.g. using FEA.

[0073] 4 shows a flow diagram for applying the above methodology for such a purpose, i.e., for determining the design performance 14 of a particular evaluation area 13 of an object to be cast, according to an example of the first disclosed embodiment.

[0074] In this case, the required functionality and layout of the cast object are evaluated based on the expected load cases. Since the cast object has to fulfill multiple functions, various load cases are considered in FEA, requiring property inputs related to either static, dynamic (durability) or fracture.

[0075] The method therefore comprises the selection of at least one evaluation area 13 (in this case EV1, EV2, EV3) of the object to be cast according to various user requirements 11, which are the various local loads 11 expected in various parts of the object based on a so-called zonal model that assigns specific zones to predefined stress levels to be considered; however, it may also be the case that a single evaluation area 13 corresponds to the entire geometrical 3D model of the object to be cast.

[0076] As mentioned before, at this stage, preliminary inputs for the casting process simulation 2 of the cast part may be defined. This preliminary input consists of information about the current part design, the composition of the selected material, the initial mould or tool design, and the nominal process conditions for filling and cooling, and is defined as a fixed input variable set 12. At this stage, no final decisions have been made about the mould or tool design or the process conditions, so assumptions are made based on past references or best practices.

[0077] In addition to the fixed set of input variables 12, process variation parameters 40 are further defined and probability distributions that describe the expected reliability of the process are added.

[0078] The method then uses the adjusted probability distribution of local mechanical properties 10 to automatically evaluate predefined evaluation regions 13 within the part, providing statistical information on expected minimum properties and property distributions.

[0079] In one embodiment, this involves performing a simulation 2 of the casting process using a numerical model 3 of the casting process to predict the probability distribution of the local microstructure 6 and the probability distribution of the local defects 7 in each portion of the evaluation zones (EV1, EV2, EV3) 13; calculating a probability distribution of local microstructure-based mechanical properties 8 as a function of the probability distribution of local microstructure 6 in each portion of evaluation area 13; calculating a probability distribution of local damage factors 9 as a function of the probability distribution of local defects 7 in each portion of the evaluation area 13; calculating, for each portion of the evaluation region 13, a probability distribution of adjusted local mechanical properties 10 as a function of a combination of the probability distribution of local microstructure-based mechanical properties 8 and the probability distribution of local damage factors 9; This is carried out by.

[0080] The method includes the evaluation by statistical means of the above information in different evaluation areas or in the whole cast object. The output of this statistical evaluation is a set of minimal local mechanical properties 101 of any evaluation area 13 and the scatter, i.e. the design performance 14, which has the distribution of these local mechanical properties (yield strength, tensile strength, elongation, etc.). This output allows the design process to correlate the load levels of a particular part with the predicted local properties that can be met based on the specific process conditions.

[0081] Information regarding minimal local mechanical properties 101 can be exported from the process simulation tool to the load analysis unit 16 via existing interfaces. The load analysis unit 16 using FEA uses this information as additional input for part design and optimization information.

[0082] The result is a part design (e.g., 3D model 4) that meets the user requirements (i.e., local loads 11) with minimal material consumption. In this way, the part design benefits from the minimum local properties expected in a defined area as a function of the design and the process, and additional information about the robustness of the design can be obtained.

[0083] In other words, this method allows designers to accept uncertainties regarding tool layout and design parameter settings, because the output compensates for these uncertainties by providing additional valuable information about local properties at an early stage, allowing designers to reduce the safety factor and reduce the weight of the part by taking advantage of the predicted local properties.

[0084] At a later stage of product and process development, the manufacturer must define the casting process in detail. The main goal in manufacturing is to use a casting layout, gate layout, and process conditions that provide a robust mold design, robust operating conditions, and a robust process window that can handle the inevitable process variations at an acceptable cost. Since no casting is defect-free, controlling the defects and their impact on the specified quality level (in final mechanical properties) is very important.

[0085] To aid in this, the method according to the present disclosure can also be applied to manufacturing in a selected process window or typical process scatter (see also FIG. 6), or generally to whoever is responsible for the casting manufacturing process. This provides the design and process responsible with information resulting from a statistical analysis based on a set of virtual experiments using a casting process simulation 2, which is now linked to a so-called Design of Experiments (DoE) 19 with design numbers 1 to N. This allows to evaluate the impact of variability on the expected part quality and related mechanical properties, and furthermore to arrive at the optimal operating point and robust process window for the expected mechanical properties of the part relative to the specifications, using the process simulation as a virtual test bench to meet the specifications from the customer.

[0086] The method not only allows simulating the nominal operating point 25, but also allows evaluating the influence of geometry, material and process variables on the tuned mechanical properties through parameterization of the geometry and definition of variations of selected input variables 1.

[0087] FIG. 5 is a flow diagram illustrating such a method for determining information regarding design performance 24 and local fault probabilities 23 of parameterized input variables 18, according to a possible embodiment of the present disclosure.

[0088] The methodology applied is essentially the same as that described above with respect to Figures 1-4. However, instead of defining and running a process simulation 2 for a single (e.g. nominal) operating point, the manufacturer defines, in addition to the fixed input variables 12, a parameterized set of input variables 18 for running a virtual experimental design 19. The input variable set 18 may then be defined to vary part and mold or tool designs (e.g. different gating concepts or cooling layouts), to vary process windows with multiple input variables (e.g. different filling conditions such as injection time and temperature, filling time, mold temperature), to take into account process variances (e.g. the effect of operational interruptions on part quality), etc.

[0089] Depending on the number of parameterized input variables 18, multiple process simulations 2 may be run automatically without human intervention using a numerical model 3 of the casting process for any possible combination of the parameterized input variables 18 for multiple designs 1-N.

[0090] In some embodiments, this involves performing a full factorial evaluation by running a simulation 2 of the casting process for all possible combinations of the parameterized input variables 18 .

[0091] In some embodiments, the method reduces the number of simulated variants by statistical techniques of virtual experimental design (using techniques such as Reduced Factorial or Sobol), thereby reducing the number of parameterized input variables 18 and performing a simulation 2 of the casting process with a reduced number of parameterized input variables 18 and a set of fixed input variables 12.

[0092] The local microstructure based mechanical property 8 variation and its probability distribution for each part of the casting can then be calculated as a function of the local microstructure 6 variation. Also, the local damage factor 9 variation and its probability distribution can be calculated based on the list of local defect 21 variations. These calculations use process variation parameters 40 to calculate the probability distribution.

[0093] The initial output is information about the local mechanical property variability. This information is in the form of local mechanical property probability distribution variability 20 and defect variability 21 as a function of process or design variations. The local mechanical property probability distribution variability 20 is a complex function of the local microstructure based mechanical property 8 probability distribution variability and the local damage factor 9 probability distribution variability. Several defect levels, distributions and associated mechanical properties for the varied conditions are automatically analyzed for all designs 1 to N by the built-in statistical tools.

[0094] The intermediate result of this process is a statistical strength assessment 22 of each part of the object to be cast as a function of the variations 1-N in the local mechanical properties variation 20 and in the local defects variation 21. The statistical analysis provides information on the minimum properties and their distribution for any of the investigated designs 1-N.

[0095] The method also provides information about the local defect probability 23 or other predicted quality metric for any of the investigated designs 1 to N.

[0096] The final output for the manufacturer is information 24 about the process performance, providing quantitative data (in the form of main effect and correlation diagrams 100) about the correlation between the input variables and the expected casting quality, as well as information about the minimum values ​​101 and distributions 102 of the expected mechanical properties for defined design or process variations, so that selected design and process decisions can be evaluated before the first real part is produced, and a statistically proven robustness 103 (in the form of a robust process window) for producing reliable parts under all circumstances can be arrived at.

[0097] The results benefit manufacturers in terms of safe production start-up and ramp-up, and the available information can be communicated to foundry users as proof that reliable operating conditions have been selected.

[0098] The above design performance information 24 helps determine a robust process window 103 (gray area between 26 and 27) for a set of parameterized input variables 18, as shown in FIG.

[0099] This figure is a scatter plot illustrating the output of a virtual design of experiments (Virtual DoE) for an example of two process variables. Each dot / point reflects a predicted mechanical property as a function of a combination of process variable 1 and process variable 2. The nominal operating point 25 can be described as reflecting a set of fixed variables 12 for determining the design performance 14, or as a nominal center point of the process window for a set of input variables 18, parameterized with a given scatter, for evaluating the design performance 24. The bold line 26 indicates the process limit for meeting the required specifications. Designs above this line will not meet the specifications, and designs below this line will show results that meet the required specifications. For the number of investigation results N, the grey line 27 reflects the Pareto frontier, i.e. the limit of the optimal combination of robust process conditions.

[0100] In this illustrated embodiment, the method of the present disclosure includes, in a first step, adjusting a process window defined by a fixed set of input variables 12 and a set of parameterized input variables 18 by reducing process variations for process robustness to determine a robust process window 103.

[0101] Once the robust process window 103 has been established, the next step may be to adjust the operating point 25 defined by the parameterized input variables 18 to determine the optimal operating point 25 .

[0102] FIG. 7 is a schematic block diagram illustrating an example of a hardware configuration for a computer system 28 for predicting local mechanical properties of an object being cast in accordance with the present disclosure.

[0103] The computer system may include one or more processors (CPUs) 32 configured to execute instructions that cause the computer system to perform a method according to any of the above-described embodiments.

[0104] The computer system may include a computer-readable storage medium 31 configured to store software-based instructions as part of a program product that is executed by the CPU 32 .

[0105] The computer system may comprise a memory 33 arranged to (temporarily) store data for applications and processes.

[0106] The computer system may have an input / output interface 29 for interaction between the system and a user 38. The input / output interface 29 may be connected to or may include an input interface (such as a keyboard and / or mouse) for receiving input from the user 38. The computer system may also further include an output device (such as an electronic display 30) for conveying information to the user 38.

[0107] The computer system may further include a communications interface 34 for communicating directly or indirectly with external devices, such as a remote client 37 via a computer network 35 .

[0108] The aforementioned hardware elements within a computer system may be connected via an internal bus configured to handle data communication and processing operations.

[0109] The computer system may further be connected to a database 36 configured to store data used as input for the above-mentioned simulations (material properties of the cast material, experimental data, etc.). The type of connection between the computer system and the database 36 may be direct or indirect. The computer system and the database 36 may both be included in the same physical device and connected via an internal bus, or may be part of different physical devices and connected directly via a communication interface 34 or indirectly via a computer network 35.

[0110] Various ways of thinking and implementation of the invention have been described with some examples. However, upon reviewing the specification, drawings, and claims of this application, a person skilled in the art will understand and be able to embody many variations in addition to the described examples in implementing the claimed invention. The words "comprise", "have", and "include" in the claims do not exclude the presence of elements or steps not recited. The absence of a plurality of elements recited in the claims does not exclude the presence of a plurality of the elements. The functions of several elements recited in the claims may be performed by a single processor or other unit. The fact that several items are recited in separate dependent claims does not exclude them from being implemented in combination, and may be implemented to advantage in combination. The computer program may be stored or distributed on a suitable medium, such as an optical storage medium or a solid medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0111] Any signs used in the claims should not be construed as limiting the scope of the invention.

Claims

1. 1. A computer-implemented method for improving a casting process by predicting a probability distribution of local mechanical properties of a casting, the method comprising: obtaining input variables defining at least one of a design of a casting, a design of a mold or tool used for casting, a composition of a casting material, a pre-treatment of the casting material, casting process conditions, and post-treatment process conditions; obtaining a process variation parameter that defines a variation of the process condition by a probability distribution; performing a simulation of the casting process on a computer device using a numerical model of the casting process; the numerical model is configured to predict local microstructure of each portion of the casting and local defects mapped to a design of the casting as a function of the input variables; The method includes, by the computer device: calculating local microstructure-based mechanical properties for each portion of the casting as a function of the local microstructure and calculating a probability distribution of the local microstructure-based mechanical properties as a function of the process variation parameters; calculating a local damage factor for each portion of the casting as a function of the local defects and calculating a probability distribution of the local damage factor as a function of the process variation parameters to represent the probability and magnitude of a detrimental effect of the local defects on local material performance in each portion of the casting; calculating adjusted local mechanical properties in each portion of the casting by applying a local damage factor to each of the local microstructure-based mechanical properties that represents a local weakening of any of the material performances; calculating, for each portion of the casting, a probability distribution of the adjusted local mechanical properties as a function of a combination of the probability distribution of the local microstructure-based mechanical properties and the probability distribution of the local damage factors; The method of claim 1, further comprising:

2. The method of claim 1 , wherein performing the simulation includes using an integrated microstructural prediction model of mold filling and solidification during casting.

3. 2. The method of claim 1, wherein calculating the adjusted local mechanical properties comprises applying each of the local damage factors to a local stress-strain curve in each portion of the casting to calculate a respective local weakening of mechanical performance in each portion.

4. the predicted local defects include attributes of a given portion of the casting that may locally degrade the material performance, such attributes including, for example, entrapped air and gas, cold shuts, weld seams, porosity, and other deleterious inclusions; calculating a local damage factor for each of the sampling areas of the casting as a function of the attributes, such as size, shape, or distribution of local defects within each sampling area; The method of claim 1.

5. 1. A computer-implemented method for improving a casting process, comprising: obtaining a set of fixed input variables defining a nominal operating point of the casting process under nominal process conditions; obtaining process variation parameters defining a probability distribution of variation of the nominal process conditions; selecting at least one evaluation area of ​​the casting; determining a design performance of the at least one evaluation area; determining the design performance includes: simulating the casting process using a numerical model of the casting process using the fixed input variables to predict, at each point in the evaluation region, a probability distribution of local microstructure and a probability distribution of local defects as a function of the process variation parameters; calculating, at each point of the evaluation region, a probability distribution of a mechanical property based on the local microstructure as a function of the probability distribution of the local microstructure; calculating a probability distribution of a local damage factor as a function of the probability distribution of the local defect at each point of the evaluation area; calculating, at each point of the evaluation region, a probability distribution of an adjusted local mechanical property as a function of a combination of the probability distribution of the local microstructure-based mechanical property and the probability distribution of the local damage factor; A method of doing something.

6. 6. The method of claim 5, wherein determining the design performance of the at least one evaluation region comprises determining a minimum expected local mechanical property of the at least one evaluation region of the casting based on the probability distribution of the adjusted local mechanical property at each point of the evaluation region.

7. 6. The method of claim 5, obtaining a set of parameterized input variables that define variability of a plurality of input variables; Implementing an experimental design; executing said experimental design, performing a plurality of simulations of the casting process using the numerical model of the casting process for possible combinations of the set of parameterized input variables to predict local microstructural variations and local defect variations in each portion of the casting; determining a statistical strength assessment for the set of parametric input variables for each portion of the casting; determining said statistical strength evaluation by calculating, for each portion of the casting, a probability distribution of local microstructure-based mechanical property variance as a function of the local microstructure variance and the process variability parameter; calculating, for each portion of the casting, a variance in a probability distribution of local damage factors as a function of the variance in the local defects and the process variability parameter; calculating an adjusted local mechanical property probability distribution variance as a function of the combined variance of the local microstructure-based mechanical property probability distribution and the variance of the local damage factor probability distribution; By method.

8. 8. The method of claim 7, further comprising determining information regarding design performance for the set of parameterized input variables to define a statistical robustness analysis of the casting process, said determining comprising: Main effect and correlation diagrams representing the influence of different input parameters on the quality of the cast parts; the minimum values ​​and distribution of mechanical properties expected for said castings based on predefined quality criteria; a robust process window for the set of parameterized input variables; The method of claim 1, wherein the at least one of

9. comparing at least one of the minimum values ​​and distributions of the expected mechanical properties with predefined user requirements for quality criteria of the casting; determining a set of parameterized input variables tuned to meet the user requirements; The method of claim 8 further comprising:

10. an input / output interface configured to receive at least one of a set of input variables, fixed input variables, and parameterized input variables; a non-volatile machine-readable storage medium containing a computer program product; at least one processor operable to execute the computer program product, to interact with the input / output interface, and to perform processing according to the method of claim 1, claim 5, or claim 7; A display configured to output a determined result of at least one step of the performed method; A computer system comprising:

11. A computer program product encoded on a non-volatile machine-readable storage medium and operable to cause a processor to perform operations according to the method of claim 1, claim 5 or claim 7.

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