https://popups.uliege.be/esaform21/index.php?id=85 Coordinator: Prof. Matteo Strano Co-organisers: Prof. A. Gil Andrade-Campos, Prof. Sam Coppieters Description: Contributions on the following subjects are welcomed: METHODS FOR METAMODELING, CONTROL AND OPTIMIZATION OF FORMING PROCESSES: shape and topological optimization; optimization of manufacturing processes and machines; process control; new metamodeling techniques. INVERSE ANALYSIS: identification of constitutive, friction, heat transfer or damage parameters; identification of boundary conditions or unknown process conditions; design of experimental procedures and measurement techniques for inverse analysis; numerical methods and algorithms for inverse analysis. Stochastic approaches: reliability assessment; robust design; optimisation under uncertainty. Mini Symposia fr Wed, 03 Mar 2021 09:36:17 +0100 Wed, 14 Apr 2021 09:56:13 +0200 https://popups.uliege.be/esaform21/index.php?id=85 0 https://popups.uliege.be/esaform21/index.php?id=1486 The development of full-field measurement techniques paved the way for the design of new mechanical tests. However, because these mechanical tests provide heterogeneous strain fields, no closed-form solution exists between the measured deformation fields and the constitutive parameters. Therefore, inverse identification techniques should be used to calibrate constitutive models, such as the widely known finite element model updating (FEMU) and the virtual fields method (VFM). Although these inverse identification techniques follow distinct approaches to explore full-field measurements, they all require using an optimisation technique to find the optimum set of material parameters. Nonetheless, the choice of a suitable optimisation technique lacks attention and proper research. Most studies tend to use a least-squares gradient-based optimisation technique, such as the Levenberg-Marquardt algorithm. This work analyses optimisation algorithms, gradient-based and -free algorithms, for the inverse identification of constitutive model parameters. To avoid needless implementation and take advantage of highly developed programming languages, the optimisation algorithms available in optimisation libraries are used. A FEMU based approach is considered in the calibration of a thermoelastoviscoplastic model. The material parameters governing strain hardening, temperature and strain rate are identified. Results are discussed in terms of efficiency and the robustness of the optimisation processes. Mon, 22 Mar 2021 20:01:49 +0100 https://popups.uliege.be/esaform21/index.php?id=1486 https://popups.uliege.be/esaform21/index.php?id=2158 The industrial demand for products with better quality and lower production costs have encouraged the widespread application of the finite element analysis (FEA) in the development and optimization of sheet metal forming processes. To ensure that the FEA solutions are reliable and robust it is important to take into account the uncertainties that inevitably arise in a real industrial environment. In this context, a numerical study on the influence of the material and process uncertainty in the stamping results of a square cup is presented. In this analysis, it is assumed uncertainty in the elasticity properties, hardening law parameters, anisotropy coefficients, blank thickness, friction coefficient and in the blank holder force. The effect of the uncertainty in these input parameters is evaluated in the punch force, equivalent plastic strain, thickness and cup geometry. Firstly, quasi-Monte Carlo method was used to evaluate the variability in the simulation outputs, considering the uncertainty of the input parameters. This analysis shows that the geometry is the output most sensitive to the uncertainty of the input parameters. Afterwards, a variance-based sensitivity analysis was carried out to identify the input parameters that most influence the output variability. It was concluded that the hardening law parameters and the anisotropy coefficients have the most influence in the stamping results variability of a square cup. Tue, 23 Mar 2021 13:53:05 +0100 https://popups.uliege.be/esaform21/index.php?id=2158 https://popups.uliege.be/esaform21/index.php?id=2364 Tube hydroforming (THF) is a plastic forming process that uses tubes with an initial circular cross section, in which pressurized fluid and axial feeds are applied for producing parts with various cross-sectional shapes. Despite of the complexity of THF process, a great progress in the automotive and aerospace industry has been made due to its advantages, such as, consolidation and weight reduction over conventional stamped and welded parts. The analysis of THF process is typically based on deterministic approaches, excluding scattering effects that influence the process reliability. Thus, robust design of tube hydroforming aims to vanish noise factors effects on process responses by considering the influence of process parameters variability. If this fluctuation is not monitored, then the fluctuation of the hydroformed parts quality may contribute to high scrap rates. In this work, the influence of variability in the THF material and process parameters (e.g. yield stress, strength coefficient, strain hardening exponent, plastic anisotropy, initial tube thickness and bulged length) on the bursting pressure is analyzed resorting to a response surface model. The statistically significant variables, which mostly influence the free bulge hydroforming process, are identified through an analysis of variance. Assuming that the input parameters variability follows the normal distribution, the probability distribution of the bursting pressure is evaluated by involving random process variables into the built response surface model. It was shown that the initial tube thickness is the most statistically significant variable, whereas the strain hardening exponent is the least statistically significant variable. Tue, 23 Mar 2021 17:52:10 +0100 https://popups.uliege.be/esaform21/index.php?id=2364 https://popups.uliege.be/esaform21/index.php?id=2415 This paper aims to compare different heterogeneous test designs from the perspective of the confidence interval quantification of inversely identified parameters, where the influence of a DIC optical system systematic and random error are taken into account. Because the errors in optical measurement can arise from many reasons and sources, our methodology relies on the system's errors determined from initial sets of pictures acquired at the load-free state for hundreds of specimens (over 850 tests over the past three years). In this way, a prior probability distribution of systematic and random error, arisen from the system initial settings and testing procedures are determined. Further, by conducting an inverse identification procedure of linear orthotropic elastic material parameters, the influence of the error distributions is studied for different types of heterogeneous specimens. The presented methodology determines the DIC bias and random error propagation through the inverse identification procedure to individual parameters. For each specimen design, confidence intervals of identified material parameters were determined. The results show the appropriateness of a specimen design for the identification of particular material parameters. Tue, 23 Mar 2021 18:30:15 +0100 https://popups.uliege.be/esaform21/index.php?id=2415 https://popups.uliege.be/esaform21/index.php?id=2502 Nowadays, virtual predictions are essential in the design and development of new engineering parts. A critical aspect for virtual predictions is the accuracy of the constitutive model to simulate the material behavior. A state-of-the-art constitutive model generally involves a large number of parameters, and according to classical procedures, this requires many mechanical experiments for its accurate identification. Fortunately, this large number of mechanical experiments can be reduced using heterogeneous mechanical tests, which provide richer mechanical information than classical homogeneous tests. However, the test’s richness is much dependent on the specimen's geometry and can be improved with the development of new specimens. Therefore, this work aims to design a uniaxial tensile load test that presents heterogeneous strain fields using a shape optimization methodology, by controlling the specimen's interior notch shape. The optimization problem is driven by a cost function composed by several indicators of the heterogeneity present in the specimen. Results show that the specimen's heterogeneity is increased with a non-circular interior notch. The achieved virtual mechanical test originates both uniaxial tension and shear strain states in the plastic region, being the uniaxial tension strain state predominant. Tue, 23 Mar 2021 20:46:27 +0100 https://popups.uliege.be/esaform21/index.php?id=2502 https://popups.uliege.be/esaform21/index.php?id=2622 Advanced inverse material identification procedures rely on the richness of strain fields generated in a complex specimen. Currently, the design of a complex specimen is mainly based on engineering judgement and experience that are often user-specific. This intuitive approach forms the crux of the problem, addressed in the current research. To this end, the paper embarks on devising a generic and automated method to design mechanical heterogeneous experiments. A notched tensile specimen is optimized to maximize a previously proposed heterogeneity indicator-IT. The effectiveness of this procedure for identifying the anisotropic parameters of the Hill48 yield criterion is validated using two independent methodologies, namely the identifiability method and the Finite Element Model Updating (FEMU) approach to assess the parameter identification quality. The latter approach is based on carefully generated synthetic experiments including the metrological aspects of Digital Image Correlation (DIC) while having access to the ground truth material behavior. For the plane stress Hill48 anisotropic yield criterion, it is shown that the IT-based design procedure correlates with both the identifiability method and the identification accuracy obtained through FEMU. Wed, 24 Mar 2021 18:33:07 +0100 https://popups.uliege.be/esaform21/index.php?id=2622 https://popups.uliege.be/esaform21/index.php?id=2640 The development and identification of the complex microscale austenite to ferrite transformation model during continuous cooling based on the Cellular Automata method and DigiCore library is the main goal of the work. The model is designed to predict phase transformation from a fully austenitic range and involves nucleation of ferrite grains with their further growth. The major driving force for the CA grain growth is based on the carbon concentration differences across the microstructure. The model parameters are identified with the inverse analysis method with the goal function defined on the basis of the dilatometric investigation. The basic assumption of the developed model, experimental procedure, as well as subsequent identification stages, are presented within the work. Wed, 24 Mar 2021 18:35:55 +0100 https://popups.uliege.be/esaform21/index.php?id=2640 https://popups.uliege.be/esaform21/index.php?id=2742 Rotary draw bending (RDB) is a forming process that is commonly used to bend tubes with small wall thicknesses and small bending radii. One of the limitations of this process is the formation of wrinkles caused by compressive stress on the inner bend. In order to design the bending process without wrinkles and to determine the necessary process parameters, adjustment tests are required. Within this work, a fuzzy controller is to be developed which automatically prevents the formation of wrinkles and thus eliminates the need for time-consuming set-up tests to determine the necessary process parameters. The fuzzy controller is based on fuzzy set theory and fuzzy logic. In connection with a rule base it is possible to simulate the human decision process. A fuzzy controller is programmed based on a max-min controller, with the required rules resulting from previous bending tests. After the fuzzy controller has been implemented, it must be connected to the bending machine by suitable interfaces. The input values, which indicate wrinkles, are measured by sensors during the bending process and provide the controller with data. The fuzzy controller then uses the control base to specify the required control variables. After programming has been completed, practical validation tests were carried out. In the validation tests using different tube wall thicknesses and materials, a significant reduction of wrinkles is achieved. Bending of completely wrinkle-free tubes is also possible due to the automated finding of optimal tool settings. Using the fuzzy controller eliminates the need for costly adjustment bends, resulting in significant time and cost savings. Wed, 24 Mar 2021 18:54:12 +0100 https://popups.uliege.be/esaform21/index.php?id=2742 https://popups.uliege.be/esaform21/index.php?id=3654 In addition to thermomechanical treatment, one of the main factors affecting the mechanical properties of steel is the chemical composition. The chemical composition may vary for a special high-strength low-alloy steel to meet certain mechanical property requirements. This work presents an approach, based on the method of physical-chemical modelling developed at the Z.I. Nekrasov Iron and Steel Institute of the National Academy of Sciences of Ukraine, to optimise the chemical composition of high-strength structural steels. The principle of this method is to describe the chemical composition of a melt by a complex of integral model parameters of interatomic interaction, characterising the chemical and structural state of the melt. The experimental data were analysed to obtain the regression model for mechanical properties based on the parameters of interatomic interaction. Finally, a multi-criteria optimisation method was applied to obtain an optimal set of microalloying elements which ensure the required mechanical properties. Mon, 29 Mar 2021 13:57:27 +0200 https://popups.uliege.be/esaform21/index.php?id=3654 https://popups.uliege.be/esaform21/index.php?id=3677 Thermal analysis plays a key role in the design of hybrid manufacturing processes of High-Performance Thermoplastic Composites (HP-TPC) parts. Indeed, an inadequate temperature distribution, during the transformation of these materials, could not only lead to mechanical and surface defects but also to inefficient energy consumption. These problems become difficult to avoid with the interaction of different materials within the part, and also with the influence of subsequent stages on the process. To overcome this challenge, the methodology proposed in this work aims to determine the spatial and temporal distribution of the heat sources that must be applied at each sequential stage of a process to reach a thermal objective within the part. The methodology is based on the concept of conformal cooling [1]. A surface enveloping the part is created [2]. Once a computational model is set up, the optimization problem is treated as an inverse problem subjected to constraints that depend on the process response in terms of temperature cycles. Thus, it requires the calculation of the direct problem, the adjoint-state solution, and the development of the sensitivity equations to implement a first-order gradient-based algorithm. As an application example, a thermo-stamping of HP-TPC with a metal insert followed by an over-molding process has been chosen because of the different stages and materials involved. The first results show a reduction of temperature gradients on the part surface at each stage while arriving at the established temperature level. Further analysis will include a constraint problem taking into account adhesion and/or energy criteria. Mon, 29 Mar 2021 14:09:33 +0200 https://popups.uliege.be/esaform21/index.php?id=3677 https://popups.uliege.be/esaform21/index.php?id=3882 Manufacturing process optimisation usually amounts to searching optima in high-dimensional parameter spaces. In industrial practice, this search is most often directed by human-subjective expert judgment and trial-and-error experiments. In contrast, high-fidelity simulation models in combination with general-purpose optimisation algorithms, e.g. finite element models and evolutionary algorithms, enable a methodological, virtual process exploration and optimisation. However, reliable process models generally entail significant computation times, which often renders classical, iterative optimisation impracticable. Thus, efficiency is a key factor in optimisation. One option to increase efficiency is surrogate-based optimisation (SBO): SBO seeks to reduce the overall computational load by constructing a numerically inexpensive, data-driven approximation („surrogate“) of the expensive simulation. Traditionally, classical regression techniques are applied for surrogate construction. However, they typically predict a predefined, scalar performance metric only, which limits the amount of usable information gained from simulations. The advent of machine learning (ML) techniques introduces additional options for surrogates: in this work, a deep neural network (DNN) is trained to predict the full strain field instead of a single scalar during textile forming („draping“). Results reveal an improved predictive accuracy as more process-relevant information from the supplied simulations can be extracted. Application of the DNN in an SBO- framework for blank holder optimisation shows improved convergence compared to classical evolutionary algorithms. Thus, DNNs are a promising option for future surrogates in SBO. Mon, 29 Mar 2021 14:55:15 +0200 https://popups.uliege.be/esaform21/index.php?id=3882 https://popups.uliege.be/esaform21/index.php?id=4146 The subjective perception of the quality of sheet metal components mainly depends on geometric characteristics and surface structure. Additionally, particular attention must be paid in this context to avoiding surface defects such as skid lines during the sheet metal forming process. For this reason, current research activities focus on predicting such surface defects as precisely as possible in the early development stages of sheet metal components by using FEM simulation. However, the modelling approaches available today do not yet provide an adequate basis for such a numerical prediction regarding the appearance of surface defects of sheet metal components such as car body outer skin panels, especially of skid lines. Consequently, the research work reported about in this paper concentrates on the development of an empirical methodology for predicting and quantifying the formation of skid lines during deep-drawing processes by using FEM simulation. For this purpose, an experimental tool was developed to produce different skid line formations by using various process parameters and thus to investigate process-influencing factors on the example of the steel sheet material DC06. In principle, the investigations carried out showed that the punch radius and the blank holder force indeed do represent crucial influencing factors for the formation of skid lines. Finally, the results obtained were used to develop a forming simulation criterion, which allows predicting skid lines formations based on calculated strain state variables such as major strain, thinning and unbending strain. Wed, 31 Mar 2021 15:37:04 +0200 https://popups.uliege.be/esaform21/index.php?id=4146