Functional restructuring of CAD models for FEA purposes

International audienceDigital Mock-ups (DMUs) are widespread and stand as reference model for product description. However, DMUs produced by industrial CAD systems essentially contain geometric models and their exploitation often requires user's input data to derive finite element models (FEMs). Here, analysis and reasoning approaches are developed to automatically enrich DMUs with functional and kinematic properties. Indeed, geometric interfaces between components form a key starting point to analyse their behaviours under reference states. This is a first stage in a reasoning process to progressively identify mechanical, kinematic as well as functional properties of the components. Inferred semantics adds up to the pure geometric representation provided by a DMU and produce also geometrically structured components and assemblies. Functional information connected to a structured geometric model of a component significantly improves the preparation of FEMs and increases its robustness because idealizations can take place using components' functions and components' structure helps defining sub-domains of FEMs

Deriving Functional Properties of Components from the Analysis of Digital Mock-ups

International audienceDigital Mock-ups (DMUs) are widespread and form a common basis for product description. However, DMUs produced by industrial CAD systems essentially contain geometric models and their exploitation often requires new input data to derive various simulation models. In this work, analysis and reasoning approaches are developed to automatically enrich DMUs with functional and kinematic properties. Indeed, interfaces between components form a key starting point to analyze their behaviours under operational reference states. This is a first stage in a reasoning process to progressively identify mechanical, kinematic as well as functional properties of the components. The overall process relying on the interfaces between components addresses also the emerging needs of conventional representations of components in industrial DMUs. Inferred semantics add up to the pure geometric representation provided by a DMU, to allow for easier exploitation of the model in different phases of a Product Development Process (PDP)

Idealized models for FEA derived from generative modeling processes based on extrusion primitives

International audienceShape idealization transformations are very common when adapting a CAD component to FEA requirements. Here, an idealization approach is proposed that is based on generative shape processes used to decompose an initial B-Rep object, i.e. extrusion processes. The corresponding primitives form the basis of candidate sub domains for idealization and their connections conveyed through the generative processes they belong to, bring robustness to set up the appropriate connections between idealized sub domains. Taking advantage of an existing construction tree as available in a CAD software does not help much because it may be complicated to use it for idealization processes. Using generative processes attached to an object that are no longer reduced to a single construction tree but to a graph containing all non trivial construction trees, is more useful for the engineer to evaluate variants of idealization. From this automated decomposition, each primitive is analyzed to define whether it can idealized or not. Subsequently, geometric interfaces between primitives are taken into account to determine more precisely the idealizable sub domains and their contours when primitives are incrementally merged to come back to the initial object

Prediction of CAD model defeaturing impact on heat transfer FEA results using machine learning techniques

Essential when adapting CAD model for finite element analysis, the defeaturing ensures the feasibility of the simulation and reduces the computation time. Processes for CAD model preparation and defeaturing tools exist but are not always clearly formalized. In this paper, we propose an approach that uses machine learning techniques to design an indicator that predicts the defeaturing impact on the quality of analysis results for heat transfer simulation. The expertise knowledge is embedded in examples of defeaturing process and analysis, which will be used to find an algorithm able to predict a performance indicator. This indicator provides help in decision making to identify features candidates to defeaturing

Estimation of CAD model simplification impact on CFD analysis using machine learning techniques

This paper adresses the way machine learning techniques based on neural networks can be used to predict the impact of simplification processes on CAD model for heat transfer FEA purposes

Template-based geometric transformations of a functionally enriched DMU into FE assembly models

International audiencePre-processing of CAD models derived from Digital Mock-Ups (DMUs) into finite element (FE) models is usually completed after many tedious tasks of model preparation and shape transformations. It is highly valuable for simulation engineers to automate time-consuming sequences of assembly preparation processes. Here, it is proposed to use an enriched DMU with geometric interfaces between components (contacts and interferences) and functional properties. Then, the key concept of template-based transformation can connect to assembly functions to locate consistent sets of components in the DMU. Subsequently, sets of shape transformations feed the template content to adapt components to FE requirements. To precisely monitor the friction areas and the mesh around bolts, the template creates sub-domains into their tightened components and preserves the consistency of geometric interfaces for the mesh generation purposes. From a user-selected assembly function, the method is able to robustly identify, locate and transform groups of components while preserving the consistency of the assembly needed for FE models. To enlarge the scope of the template in the assembly function taxonomy, it is shown how the concept of dependent function enforces the geometric and functional consistency of the transformed assembly. To demonstrate the proposed approach, a business oriented prototype processes bolted junctions of aeronautical structures

Idealized models for FEA derived from generative modeling processes based on extrusion primitives

International audienceShape idealization transformations are very common when adapting a CAD component to FEA requirements. Here, an idealization approach is proposed that is based on generative shape processes used to decompose an initial B-Rep object, i.e. extrusion processes. The corresponding primitives form the basis of candidate sub domains for idealization and their connections conveyed through the generative processes they belong to, bring robustness to set up the appropriate connections between idealized sub domains. Taking advantage of an existing construction tree as available in a CAD software does not help much because it may be complicated to use it for idealization processes. Using generative processes attached to an object that are no longer reduced to a single construction tree but to a graph containing all non trivial construction trees, is more useful for the engineer to evaluate variants of idealization. From this automated decomposition, each primitive is analyzed to define whether it can idealized or not. Subsequently, geometric interfaces between primitives are taken into account to determine more precisely the idealizable sub domains and their contours when primitives are incrementally merged to come back to the initial object

Estimation of CAD model simplification impact on CFD analysis using machine learning techniques

This paper adresses the way machine learning techniques based on neural networks can be used to predict the impact of simplification processes on CAD model for heat transfer FEA purposes

Prediction of CAD model defeaturing impact on heat transfer FEA results using machine learning techniques

Essential when adapting CAD model for finite element analysis, the defeaturing ensures the feasibility of the simulation and reduces the computation time. Processes for CAD model preparation and defeaturing tools exist but are not always clearly formalized. In this paper, we propose an approach that uses machine learning techniques to design an indicator that predicts the defeaturing impact on the quality of analysis results for heat transfer simulation. The expertise knowledge is embedded in examples of defeaturing process and analysis, which will be used to find an algorithm able to predict a performance indicator. This indicator provides help in decision making to identify features candidates to defeaturing

A priori evaluation of simulation models preparation processes using artificial intelligence techniques

Controlling the well-known triptych costs, quality and time during the different phases of the Product Development Process (PDP) is an everlasting challenge for the industry. Among the numerous issues that are to be addressed, the development of new methods and tools to adapt to the various needs the models used all along the PDP is certainly one of the most challenging and promising improvement area. This is particularly true for the adaptation of Computer-Aided Design (CAD) models to Computer-Aided Engineering (CAE) applications, and notably during the CAD models simplification steps. Today, even if methods and tools exist, such a preparation phase still requires a deep knowledge and a huge amount of time when considering Digital Mock-Up (DMU) composed of several hundreds of thousands of parts. Thus, being able to estimate a priori the impact of DMU adaptation scenarios on the simulation results would help identifying the best scenario right from the beginning. This paper addresses such a difficult problem and uses artificial intelligence (AI) techniques to learn and accurately predict behaviours from carefully selected examples. The main idea is to identify rules from these examples used as inputs of learning algorithms. Once those rules obtained, they can be used on a new case to a priori estimate the impact of a preparation process without having to perform it. To reach this objective, a method to build a representative database of examples has been developed, the right input (explanatory) and output (preparation process quality criteria) variables have been identified, then the learning model and its associated control parameters have been tuned. One challenge was to identify explanatory variables from geometrical key characteristics and data characterizing the preparation processes. A second challenge was to build a effective learning model despite a limited number of examples. The rules linking the output variables to the input ones are obtained using AI techniques such as well-known neural networks and decision trees. The proposed approach is illustrated and validated on industrial examples in the context of computational fluid dynamics simulations