https://popups.uliege.be/esaform21/index.php?id=83 Mini Symposia fr Wed, 03 Mar 2021 09:35:22 +0100 Wed, 14 Apr 2021 09:55:25 +0200 https://popups.uliege.be/esaform21/index.php?id=83 0 https://popups.uliege.be/esaform21/index.php?id=1572 Finite element modeling (FEM) has recently become the most attractive computational tool to predict and optimize many industrial problems. However, the FEM becomes ineffective as far as complex multi-physics parameterized problems, such as induction heating process, are concerned because of high computational cost. This work aims at studying the possibility of applying a new approach based on the reduced order modeling (ROM) to obtain approximate solutions of a parametric problem. Basically, the effect of induction heating process parameters on some physical quantities of interest (QoI) will be analyzed under the real-time constraint. To achieve this dimensionality reduction, a set of precomputed solutions is first collected, at some sparse points in the space domain and for a properly selected process parameters, by solving the full-order models implemented in the commercial finite element software FORGE®. A Proper Orthogonal Decomposition (POD) based reducedorder model is then applied to the collected data to find a low dimensional space onto which the solution manifold could be projected and an approximated solution for new process parameters could be efficiently computed in real time. Besides, the POD is applied to build a reduced basis and to compute their corresponding modal coefficients. It is then followed by artificial intelligence techniques for regression purpose, such as sparse Proper Generalized Decomposition, to fit the low dimensional POD modal coefficients. Hence, the problem can be solved with a much lower dimension compared to the initial one. It was shown that a good approximation of the QoI was provided, in low-data limit, using a single POD modal coefficient as a response for the regression methods. However, the obtained approximation accuracy needs to be enhanced. Mon, 22 Mar 2021 20:13:16 +0100 https://popups.uliege.be/esaform21/index.php?id=1572 https://popups.uliege.be/esaform21/index.php?id=1879 In freeform bending the desired geometry is created by defined movements of the die while a continuous feed takes place. To compensate the differences and variations in properties of the semi-finished product, the motion profile has to be adjusted. Currently, this calibration is done once before the manufacturing process of a certain profile. Therefore, numerous iterations consisting of bending and measuring certain radii based on a default motion profile are performed. The measured data is subjected to a curve fit, which is not sufficiently suitable for all profiles and materials setups due to the fixed predefined function that is used. Furthermore, the tool setup is not taken in account. This results in wrong kinematics and production rejects. In this work, an enhanced geometrical model is introduced which incorporates tool parameters - such as distances, clearances and positioning aspects - as a starting point for further calculations. Furthermore, different calibration methods are tested and compared to each other using FEM simulations to fit the calculated curve to the actually used specimen. This work establishes the basis for further compensation and calibration strategies in order to improve the handling of varying properties of semi-finished products within the freeform bending process. Tue, 23 Mar 2021 10:17:01 +0100 https://popups.uliege.be/esaform21/index.php?id=1879 https://popups.uliege.be/esaform21/index.php?id=1954 Shortened product development processes in automotive industry combined with the upcoming lack of experts do challenge sheet metal part production fundamentally. Tryout time and manufacturing costs of large forming dies today are significantly influenced by their digitally supported engineering. The forming process by such tools is beside other influences is affected by elastic deformations of forming dies and press structure as well as contact areas between die and sheet metal part. In deep drawing such contact areas are influenced by the blank properties and the flange behavior in terms of thickening and thinning. Recent developments in sheet metal forming simulation do consider advanced friction models and structural modeling of die and press components improving simulation accuracy. Nevertheless thinning or thickening of sheet metal results into localized surface pressure distribution during deep drawing. For this reason, it is not sufficient to use the currently common practice of homogeneous surface pressure distribution in sheet metal forming simulation. In this respect, this paper presents a numerical approach for consideration of straining effects in the sheet metal part during forming operation. For this purpose, a systematic process improvement was developed in this paper to identify contact areas via a numeric simulation parameter. Validating the numerical investigation, a rectangle cup die is used, considering major strain. The main results of this contribution for that reason show how simulated contact areas can be estimated by reverse engineering of real forming parts. Hereby straining based contact areas lead to a novel contact area design in process planning, resulting in efficient die tryout. Tue, 23 Mar 2021 11:15:03 +0100 https://popups.uliege.be/esaform21/index.php?id=1954 https://popups.uliege.be/esaform21/index.php?id=2004 The need of solving industrial problems using faster and less computationally expensive techniques is becoming a requirement to cope with the present digital transformation of most industries. Recently, data is conquering the domain of engineering with different purposes: (i) defining data-driven models of materials, processes, structures and systems, whose physics-based models, when they exists, remain too inaccurate; (ii) enriching the existing physics-based models within the so-called hybrid paradigm; and (iii) using advanced machine learning and artificial intelligence techniques for scales bridging (upscaling), that is, for creating models that operating at the coarse-grained scale (cheaper in what respect the computational resources) enables integrating the fine-scale richness. The present work addresses the last item, aiming at enhancing standard structural models (defined in 2D shell geometries) for accounting all the fine-scale details (3D with rich through-the-thickness behaviors). For this purpose, two main strategies will be combined: (i) the in-plane-out-of-plane proper generalized decomposition -PGD- serving to provide the fine-scale richness; and (ii) advance machine learning techniques able to learn and extract the regression relating the input parameters with those high-resolution detailed descriptions. Tue, 23 Mar 2021 12:23:56 +0100 https://popups.uliege.be/esaform21/index.php?id=2004 https://popups.uliege.be/esaform21/index.php?id=2017 This work retraces the main recent advances in the so-called non-intrusive model order reduction, and more concretely, the construction of parametric solutions related to parametric models, with special emphasis on the technologies enabling allying accuracy, frugality and robustness. Thus, different technologies will be revisited beyond the usual metamodeling techniques making use of polynomial basis or kriging, for addressing multi-parametric models, with sometimes several tens of parameters, while keeping the complexity (DoE size) scaling with the number of parameters. Moreover, sparsity can be profitable for increasing accuracy while avoiding overfitting, and when combined with ANOVA-based decompositions the benefits are potentially huge. Tue, 23 Mar 2021 12:27:29 +0100 https://popups.uliege.be/esaform21/index.php?id=2017 https://popups.uliege.be/esaform21/index.php?id=2050 We have seen in Part I of this paper that model order reduction allows the involvement of physics-based models in design, as in the past, but now also in online decision-making, without requiring unreasonable computing resources. On the other hand, machine learning techniques were not ready to cope with the processing speed and the lack of data. It was therefore necessary to adapt a number of techniques, and to create others, capable of operating online and even in the presence of a very small amount of data: the so-called "physics-informed artificial intelligence" techniques. For that purpose, we have adapted and proposed a number of techniques, which have proven and are proving every day in many industrial applications their capabilities and performances. Six major uses of AI in engineering concern: (i) visualization of multidimensional data; (ii) classification and clustering, supervised and unsupervised, where it is assumed that members of the same cluster have similar behaviors; (iii) model extraction, that is, discovering the quantitative relationship between inputs (actions) and outputs (reactions) in a consistent manner with respect to the physical laws. When addressing knowledge extraction, item (iv) above, as well as the need of explaining for certifying, item (v), advances are much limited and both items need for major progresses, as the one enabling discarding useless parameters, or discovering latent variables whose consideration becomes compulsory for explaining experimental findings, or combining parameters that act in a combined manner. Discovering equations is a very timely topic because it finally enables transforming data into knowledge. Tue, 23 Mar 2021 12:33:56 +0100 https://popups.uliege.be/esaform21/index.php?id=2050 https://popups.uliege.be/esaform21/index.php?id=2475 Sheet metal alloys are used in many industries to save material, reduce weight and improve the overall performance of products. For the last decades, many types of elements have been developed to resolve the locking problems encountered in the simulation of thin structures. Among these approaches, a family of assumed-strain solid-shell elements has proved to be very efficient and attractive in simulating thin 3D structures with various constitutive models. Furthermore, these elements are able to account for anisotropic behavior of thin structures since isotropic yield functions cannot capture the real physics of some forming processes. In this work, von Mises isotropic yield criterion with Johnson-cook hardening model are combined with a linear prismatic solid-shell element to simulate sheet metal forming processes. A new element assembly technique has been developed to permit the assembly of prismatic elements in a tetrahedral element-based software. This technique splits the prism into multiple tetrahedral elements in such a way that all the cross-terms are accounted for. Furthermore, a tetrahedral based partitioning code has been modified to account for the new prismatic element shape without changing the core structure of the code. More accurate results were obtained using low number of solid-shell elements compared to its counterpart tetrahedral element (MINI element). This reduction in the number of elements accelerated the simulation, especially in the coupled magnetic-structure simulation used for magnetic pulse forming process. The proposed element and criteria are implemented into FORGE (in-house code developed at CEMEF) for simulating magnetic pulse forming process. Tue, 23 Mar 2021 20:18:15 +0100 https://popups.uliege.be/esaform21/index.php?id=2475 https://popups.uliege.be/esaform21/index.php?id=2589 The high-potential of lightweight components consisting of similar or dissimilar materials can be exploited by Solid-State Joining techniques. Whereas defects such as pores and hot cracking are often an issue in fusion-based joining processes, via solid-state joining processes they can be avoided to enable high-quality welds. To define an optimal process window for obtaining anticipated joint properties, numerous time and cost consuming experiments are usually required. Building a predictive model based on regression analysis enables the identification and quantification of process-property relationships. On the one hand, mechanical property and performance predictions based on specific process parameters are needed, on the other hand, inverse determination of required process parameters for reaching desired properties or performances are demanded. If these relations are obtained, optimized process parameter sets can be identified while vast numbers of required experiments can be reduced, as underlying physical mechanisms are utilized. In this study, different regression analysis algorithms, such as linear regression, decision trees and random forests, are applied to the refill Friction Stir Spot Welding process for establishing correlations between process parameters and joint properties. Experimental data sets used for training and testing are based on a Box-Behnken Design of Experiments (DoE) and additional test experiments, respectively. The machine-learning based regression analyses are benchmarked against linear regression and DoE statistics. The results illustrate a decryption of relationships along the process-property chain and its deployment to predict mechanical properties governed by process parameters. Wed, 24 Mar 2021 18:27:26 +0100 https://popups.uliege.be/esaform21/index.php?id=2589 https://popups.uliege.be/esaform21/index.php?id=4086 The continuous research for progressively lighter components moves the attention on the massive adoption of Al alloys. The achievement of such an ambitious goal passes through the definition of innovative manufacturing methodologies able to overcome some of the most hindering limitation of Al alloys, i.e. their poor formability at room temperature. A viable approach is based on the modification of the blank properties through a local heat treatment (to achieve an optimized spatial distribution of ductility/strength), so that the subsequent forming operation can be carried out at room temperature. The implementation of such approach relies on finite element simulations, where the use of a proper constitutive material model plays a fundamental role. In the present work an innovative methodology, already proposed by the authors in a previous research, is again adopted to enrich the characterization of a strain-hardenable Al alloy (AA5754), initially purchased in a pre-strained condition (H32), and locally annealed by means of a laser treatment: in particular, Thanks to the adoption of the DIC, the investigation of the anisotropy showed a strict correlation between the value of the Lankford parameter and the material condition reached at the end of the local treatment. The experimental data were fitted by a sigmoidal function and implemented in a modified Hill plasticity model for the simulation of the tensile test of a locally treated dogbone specimen, showing a good accordance with the experimental results. Tue, 30 Mar 2021 12:28:09 +0200 https://popups.uliege.be/esaform21/index.php?id=4086 https://popups.uliege.be/esaform21/index.php?id=4293 Thermally supported clinching (Hotclinch) is a novel promising process to join dissimilar materials. Here, metal and fibre-reinforced thermoplastics (FRTP) are used within this single step joining process and without the usage of auxiliary parts like screws or rivets. For this purpose, heat is applied to improve the formability of the reinforced thermoplastic. This enables joining of the materials using conventional clinching-tools. Focus of this work is the modelling on mesoscopic scale for the numerical simulation of this process. The FTRP-model takes the material behaviour both of matrix and the fabric reinforced organo-sheet under process temperatures into account. For describing the experimentally observed phenomena such as large deformations, fibre failure and the interactions between matrix and fibres as well as between fibres themselves, the usage of conventional, purely Lagrangian based FEM methods is limited. Therefore, the combination of contact-models with advanced modelling approaches like Arbitrary-Lagrangian-Eulerian (ALE), Coupled-Eulerian-Lagrangian (CEL) and Smooth-ParticleHydrodynamics (SPH) for the numerical simulation of the clinching process are employed. The different approaches are compared with regard to simulation feasibility, robustness and results accuracy. It is shown, that the CEL approach represents the most promising approach to describe the clinching process. Thu, 01 Apr 2021 17:51:44 +0200 https://popups.uliege.be/esaform21/index.php?id=4293