Numerical product design: Springback prediction, compensation and optimization

Numerical simulations are being deployed widely for product design. However, the accuracy of the numerical tools is not yet always sufficiently accurate and reliable. This article focuses on the current state and recent developments in different stages of product design: springback prediction, springback compensation and optimization by finite element (FE) analysis. To improve the springback prediction by FE analysis, guidelines regarding the mesh discretization are provided and a new through-thickness integration scheme for shell elements is launched. In the next stage of virtual product design the product is compensated for springback. Currently, deformations due to springback are manually compensated in the industry. Here, a procedure to automatically compensate the tool geometry, including the CAD description, is presented and it is successfully applied to an industrial automotive part. The last stage in virtual product design comprises optimization. This article presents an optimization scheme which is capable of designing optimal and robust metal forming processes efficiently

Solving optimisation problems in metal forming using FEM: A metamodel based optimisation algorithm

During the last decades, Finite Element (FEM) simulations of metal forming processes have\ud become important tools for designing feasible production processes. In more recent years,\ud several authors recognised the potential of coupling FEM simulations to mathematical opti-\ud misation algorithms to design optimal metal forming processes instead of only feasible ones.\ud This report describes the selection, development and implementation of an optimisa-\ud tion algorithm for solving optimisation problems for metal forming processes using time\ud consuming FEM simulations. A Sequential Approximate Optimisation algorithm is pro-\ud posed, which incorporates metamodelling techniques and sequential improvement strate-\ud gies for enhancing the e±ciency of the algorithm. The algorithm has been implemented in\ud MATLABr and can be used in combination with any Finite Element code for simulating\ud metal forming processes.\ud The good applicability of the proposed optimisation algorithm within the ¯eld of metal\ud forming has been demonstrated by applying it to optimise the internal pressure and ax-\ud ial feeding load paths for manufacturing a simple hydroformed product. Resulting was\ud a constantly distributed wall thickness throughout the ¯nal product. Subsequently, the\ud algorithm was compared to other optimisation algorithms for optimising metal forming\ud by applying it to two more complicated forging examples. In both cases, the geometry of\ud the preform was optimised. For one forging application, the algorithm managed to solve\ud a folding defect. For the other application both the folding susceptibility and the energy\ud consumption required for forging the part were reduced by 10% w.r.t. the forging process\ud proposed by the forging company. The algorithm proposed in this report yielded better\ud results than the optimisation algorithms it was compared to

Computer aided design and optimization of bi-layered tube hydroforming process

Tube hydroforming is one of the unconventional metal forming processes in which high fluid pressure and axial feed are used to deform a tube blank in the desired shape. However, production of bi-layered tubular components using this process has not been investigated in detail in spite of the large number of research studies conducted in this area. Bi-layered tubing can be useful in complex working environments as it offers dual properties that a single layer structure doesn’t have. Consequently, for wider implementation of this technology, a detailed investigation on bi-layered tube hydroforming is required. In this research, both single and bi-layered tube hydroforming processes were numerically modelled using the finite element method (ANSYS LS-DYNA). Experiments were conducted to check the numerical models validation. In addition, Response Surface Methodology (RSM) using the Design-Expert statistical software has been employed along with the finite element modelling to attain a detailed investigation of bi-layered tube hydroforming in the X-type and T-type dies. The process outputs were modelled as functions of both the geometrical factors (tube length, tube diameter, die corner radius, and thicknesses of both layers.) and the process parameters (internal pressure coordinates, axial feed, and coefficient of friction.). Furthermore, the desirability approach was used in conjunction with the RSM models to identify the optimal combinations of each the geometrical factors and process parameters that achieve different objectives simultaneously. In addition, a different optimization approach that applies the iterative optimization algorithm in the ANSYS software was implemented in the process optimization. The finite element models of single and bi-layered tube hydroforming processes were experimentally validated. A comparison of both processes was carried out under different loading paths. Also, response surface modelling of the bi-layered tube hydroforming process outputs was successfully achieved, and the main effects and interaction effects of the input parameters on the responses were discussed. Based on the RSM models, the process was optimized by finding the inputs levels at which the desired objectives are satisfied. Finally, a comparison of the RSM based optimization approach and the iterative optimization algorithm was performed based on the optimum results of each technique

Multi-objective Optimization of Tube Hydroforming Using Hybrid Global and Local Search

An investigation of non-linear multi-objective optimization is conducted in order to define a set of process parameters (i.e. load paths) for defect-free tube hydroforming. A generalized forming severity indicator that combines both the conventional forming limit diagram (FLD) and the forming limit stress diagram (FLSD) was adopted to detect excessive thinning, necking/splitting and wrinkling in the numerical simulation of formed parts. In order to rapidly explore and capture the Pareto frontier for multiple objectives, two optimization strategies were developed: normal boundary intersection (NBI) and multi-objective genetic algorithm (MOGA) based on the concept of dominated solutions . The NBI method produced a uniformly distributed set of solutions. For the MOGA method, a stochastic Kriging model was used as a surrogate model. Furthermore, the MOGA constraint-handling technique was improved, Kriging model updating was automated and a hybrid global-local search was implemented in order to rapidly explore the Pareto frontier. Both piece-wise linear and pulsating pressure paths were investigated for several case studies, including straight tube, pre-bent tube and industrial tube hydroforming. For straight tube hydroforming, the optimal load path was obtained using the NBI method and it showed a smaller corner radius compared to that predicted by the commercial program LS-OPT4.0. Moreover, the hybrid method coupling global search (MOGA) and local search (sequential quadratic programming: SQP) was applied for straight tube hydroforming, and the results showed a significant improvement in terms of the stress safety margin and reduced local thinning. For a commercial refrigerator door handle, the MOGA method was utilized to inversely analyze the loading path and the calculated path correlated well with the production path. For a hydroformed T-shaped tubular part, the amplitude and frequency of the pulsating pressure were optimized with MOGA. Thinning was reduced by 25% compared with experimental results. A multi-stage (prebent) tube hydroforming simulation was performed and it indicated that the reduction in formability due to bending can be largely compensated by end feeding the tube during hydroforming. The loading path optimized by MOGA showed that the expansion into the corner of the hydroforming die increased by 16.7% compared to the maximum expansion obtained during experimental trials

Computer aided optimization of tube hydroforming processes

Tube hydroforming is a process of forming closed-section, hollow parts with different cross sections by applying combined internal hydraulic forming pressure and end axial compressive loads or feeds to force a tubular blank to conform to the shape of a given die cavity. It is one of the most advanced metal forming processes and is ideal for producing seamless, lightweight, near net shape components.. This innovative manufacturing process offers several advantages over conventional manufacturing processes such as part consolidation, weight reduction and lower tooling and process cost. To increase the implementation of this technology in different manufacturing industries, dramatic improvements for hydroformed part design and process development are imperative. The current design and development of tube hydroforming processes is plagued with long design and prototyping lead times of the component. The formability of hydroformed tubular parts is affected by various physical parameters such as material properties, tube and die geometry, boundary conditions and process loading paths. Finite element simulation is perceived by the industry to be a cost-effective process analysis tool and has the capability to provide a greater insight into the deformation mechanisms of the process and hence allow for greater product and process optimization. Recent advances in the non-linear metal forming simulation capabilities of finite element software have made simulation of many complex hydroforming processes much easier. Although finite element based simulation provides a better understanding of the process, trial-and-error based simulation and optimization becomes very costly for complex processes. Thus, powerful intelligent optimization methods are required for better design and understanding of the process. This work develops a better understanding of the forming process and its control parameters. An experimental study of ‘X ’ and ‘T’-branch type tube hydroforming was undertaken and finite element models of these forming processes were built and subsequently validated against the experimental results. Furthermore these forming processes were optimized using finite element simulations enhanced with numerical optimization algorithms and with an adaptive process control algorithm. These new tools enable fast and effective determination of loading paths optimized for successful hydroforming of complex tubular parts and replace trial-and-error approaches by a more efficient customized finite element analysis approach

Experimental process development and aerospace alloy formability studies for hydroforming

Dans le procédé d’hydroformage, la pression d’un fluide est utilisée pour déformer plastiquement un tube paroi mince à l’intérieur d’une matrice fermée afin de remplir la cavité de la matrice. L’hydroformage des tubes possède de nombreux avantages qui rendent ce procédé très intéressant pour plusieurs industries telles que l’automobile et l’aérospatiale. Mais, à cause de différents facteurs tels que la formabilité des matériaux, l’ordre et les séquences du chargement (force de compression axiale et pression interne pendant le procédé), la géométrie de l’outil et la friction, c’est un procédé de mise en forme assez complexe. Ainsi, la simulation par éléments finis combinée à des méthodes d’optimisation peuvent réduire significativement le coût de l’approche “Essai – Erreur” utilisée dans les méthodes conventionnelles de mise en forme. Dans ce mémoire, pour étudier les effets de différent paramètres tels que les conditions de friction, l’épaisseur du tube et la compression axiale sur la pièce finale, des essais d’hydroformage de tube ont été menés en utilisant une matrice de forme ronde à carrée. Les expériences ont été effectuées sur des tubes d’acier inoxydable 321 de 50.8 mm (2 in) de diamètre et deux différentes épaisseurs ; 0.9 mm et 1.2 mm. L’historique du chargement a été enregistré avec le système d’acquisition de la presse. Un système de mesure de déformation automatique, Argus, a été utilisé pour mesurer les déformations sur les tubes hydroformés. Les données collectées à partir des essais initiaux ont été utilisées pour comparer avec les simulations. Le procédé a été simulé et optimisé à partir des logiciels Ls-Dyna et Ls-Opt, respectivement. Les variations de déformations et d’épaisseurs mesurées à partir des expériences ont été comparées aux résultats de la simulation par éléments finis dans les zones critiques. La comparaison des résultats de la simulation et des expériences sont en bon accord indiquant que l’approche peut être utilisée pour prédire la forme finale et les variations d’épaisseurs de pièces hydroformées pour des applications aérospatiales

Near net shape manufacturing of metal : a review of approaches and their evolutions

In the last thirty years the concept of manufacturability has been applied to many different processes in numerous industries. This has resulted in the emergence of several different "Design for Manufacturing" methodologies which have in common the aim of reducing productions costs through the application of general manufacturing rules. Near net shape technologies have expanded these concepts, targeting mainly primary shaping process, such as casting or forging. The desired outcomes of manufacturability analysis for near-net-shape (NNS) processes are cost and lead/time reduction through minimization of process steps (in particular cutting and finishing operations) and raw material saving. Product quality improvement, variability reduction and component design functionality enhancement are also achievable through NNS optimization. Process parameters, product design and material selection are the changing variables in a manufacturing chain that interact in complex, non-linear ways. Consequently modeling and simulation play important roles in the investigation of alternative approaches. However defining the manufacturing capability of different processes is also a “moving target” because the various NNS technologies are constantly improving and evolving so there is challenge in accurately reflecting their requirements and capabilities. In the last decade, for example, CAD, CNC technologies and innovation in materials have impacted enormously on the development of NNS technologies. This paper reviews the different methods reported for NNS manufacturability assessment and examines how they can make an impact on cost, quality and process variability in the context of a specific production volume. The discussion identifies a lack of structured approaches, poor connection with process optimization methodologies and a lack of empirical models as gaps in the reported approaches