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

The development of a finite elements based springback compensation tool for sheet metal products

Springback is a major problem in the deep drawing process. When the tools are released after the forming stage, the product springs back due to the action of internal stresses. In many cases the shape deviation is too large and springback compensation is needed: the tools of the deep drawing process are changed so, that the product becomes geometrically accurate after springback. In this paper, two different ways of geometric optimization are presented, the smooth displacement adjustment (SDA) method and the surface controlled overbending (SCO) method. Both methods use results from a finite elements deep drawing simulation for the optimization of the tool shape. The methods are demonstrated on an industrial product. The results are satisfactory, but it is shown that both methods still need to be improved and that the FE simulation needs to become more reliable to allow industrial application

The potential use of service-oriented infrastructure framework to enable transparent vertical scalability of cloud computing infrastructure

Cloud computing technology has become familiar to most Internet users. Subsequently, there has been an increased growth in the use of cloud computing, including Infrastructure as a Service (IaaS). To ensure that IaaS can easily meet the growing demand, IaaS providers usually increase the capacity of their facilities in a vertical IaaS increase capability and the capacity for local IaaS amenities such as increasing the number of servers, storage and network bandwidth. However, at the same time, horizontal scalability is sometimes not enough and requires additional strategies to ensure that the large number of IaaS service requests can be met. Therefore, strategies requiring horizontal scalability are more complex than the vertical scalability strategies because they involve the interaction of more than one facility at different service centers. To reduce the complexity of the implementation of the horizontal scalability of the IaaS infrastructures, the use of a technology service oriented infrastructure is recommended to ensure that the interaction between two or more different service centers can be done more simply and easily even though it is likely to involve a wide range of communication technologies and different cloud computing management. This is because the service oriented infrastructure acts as a middle man that translates and processes interactions and protocols of different cloud computing infrastructures without the modification of the complex to ensure horizontal scalability can be run easily and smoothly. This paper presents the potential of using a service-oriented infrastructure framework to enable transparent vertical scalability of cloud computing infrastructures by adapting three projects in this research: SLA@SOI consortium, Open Cloud Computing Interface (OCCI), and OpenStack

Knowledge based cloud FE simulation of sheet metal forming processes

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques. This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions

A numerical approach to robust in-line control of roll forming processes

The quality of roll formed products is known to be highly sensitive and dependent on the process parameters and thus the unavoidable variations of these parameters during mass production. To maintain a constant high product quality, a new roll former with an adjustable final roll forming stand is developed at Deakin University enabling the continuous compensation for possible shape defects. In this work, a numerical approach to robust in-line control of the roll forming of a V-section profile is presented, combining the aspects of robust process design and in-line compensation methods. A numerical study is performed to determine the relationship between controllable process settings and uncontrollable variation of incoming material properties with respect to the common product defects longitudinal bow and springback. The computationally expensive non-linear FE simulations used in this study are subsequently replaced by metamod-els based on efficient Single Response Surfaces. Using these metamodels, the optimal setting for the adjustable stand is determined with robust optimization techniques and the effect on product quality analyzed. It is shown that the subsequent adjustment of the final roll stand position leads to a significantly improved product quality by preventing product defects and minimizing the deteriorating effects of scattering variables

Integrated Process Simulation and Die Design in Sheet Metal Forming

During the recent 10-15 years, Computer Aided Process Planning and Die Design evolved as one of the most important engineering tools in sheet metal forming, particularly in the automotive industry. This emerging role is strongly emphasized by the rapid development of Finite Element Modelling, as well. The purpose of this paper is to give a general overview about the recent achievements in this very important field of sheet metal forming and to introduce some special results in this development activity. Therefore, in this paper, an integrated process simulation and die design system developed at the University of Miskolc, Department of Mechanical Engineering will be analysed. The proposed integrated solutions have great practical importance to improve the global competitiveness of sheet metal forming in the very important segment of industry. The concept described in this paper may have specific value both for process planning and die design engineers

Recent Achievements in Numerical Simulation in Sheet Metal Forming Processes

Purpose of this paper: During the recent 10-15 years, Computer Aided Process Planning and Die Design evolved as one of the most important engineering tools in sheet metal forming, particularly in the automotive industry. This emerging role is strongly emphasized by the rapid development of Finite Element Modelling, as well. The purpose of this paper is to give a general overview about the recent achievements in this very important field of sheet metal forming and to introduce some special results in this development activity. Design/methodology/approach: Concerning the CAE activities in sheet metal forming, there are two main approaches: one of them may be regarded as knowledge based process planning, whilst the other as simulation based process planning. The author attempts to integrate these two separate developments in knowledge and simulation based approach by linking commercial CAD and FEM systems. Findings: Applying the above approach a more powerful and efficient process planning and die design solution can be achieved radically reducing the time and cost of product development cycle and improving product quality. Research limitations: Due to the different modelling approaches in CAD and FEM systems, the biggest challenge is to enhance the robustness of data exchange capabilities between various systems to provide an even more streamlined information flow. Practical implications: The proposed integrated solutions have great practical importance to improve the global competitiveness of sheet metal forming in the very important segment of industry. Originality/value: The concept described in this paper may have specific value both for process planning and die design engineers

A metamodel based optimisation algorithm for metal forming processes

Cost saving and product improvement have always been important goals in the metal\ud forming industry. To achieve these goals, metal forming processes need to be optimised. During\ud the last decades, simulation software based on the Finite Element Method (FEM) has significantly\ud contributed to designing feasible processes more easily. More recently, the possibility of\ud coupling FEM to mathematical optimisation algorithms is offering a very promising opportunity\ud to design optimal metal forming processes instead of only feasible ones. However, which\ud optimisation algorithm to use is still not clear.\ud In this paper, an optimisation algorithm based on metamodelling techniques is proposed\ud for optimising metal forming processes. The algorithm incorporates nonlinear FEM simulations\ud which can be very time consuming to execute. As an illustration of its capabilities, the\ud proposed algorithm is applied to optimise the internal pressure and axial feeding load paths\ud of a hydroforming process. The product formed by the optimised process outperforms products\ud produced by other, arbitrarily selected load paths. These results indicate the high potential of\ud the proposed algorithm for optimising metal forming processes using time consuming FEM\ud simulations

Solving optimisation problems in metal forming using Finite Element simulation and metamodelling techniques

During the last decades, Finite Element (FEM) simulations\ud of metal forming processes have become important\ud tools for designing feasible production processes. In more\ud recent years, several authors recognised the potential of\ud coupling FEM simulations to mathematical optimisation\ud algorithms to design optimal metal forming processes instead\ud of only feasible ones.\ud Within the current project, an optimisation strategy is being\ud developed, which is capable of optimising metal forming\ud processes in general using time consuming nonlinear\ud FEM simulations. The expression “optimisation strategy”\ud is used to emphasise that the focus is not solely on solving\ud optimisation problems by an optimisation algorithm, but\ud the way these optimisation problems in metal forming are\ud modelled is also investigated. This modelling comprises\ud the quantification of objective functions and constraints\ud and the selection of design variables.\ud This paper, however, is concerned with the choice for\ud and the implementation of an optimisation algorithm for\ud solving optimisation problems in metal forming. Several\ud groups of optimisation algorithms can be encountered in\ud metal forming literature: classical iterative, genetic and\ud approximate optimisation algorithms are already applied\ud in the field. We propose a metamodel based optimisation\ud algorithm belonging to the latter group, since approximate\ud algorithms are relatively efficient in case of time consuming\ud function evaluations such as the nonlinear FEM calculations\ud we are considering. Additionally, approximate optimisation\ud algorithms strive for a global optimum and do\ud not need sensitivities, which are quite difficult to obtain\ud for FEM simulations. A final advantage of approximate\ud optimisation algorithms is the process knowledge, which\ud can be gained by visualising metamodels.\ud In this paper, we propose a sequential approximate optimisation\ud algorithm, which incorporates both Response\ud Surface Methodology (RSM) and Design and Analysis\ud of Computer Experiments (DACE) metamodelling techniques.\ud RSM is based on fitting lower order polynomials\ud by least squares regression, whereas DACE uses Kriging\ud interpolation functions as metamodels. Most authors in\ud the field of metal forming use RSM, although this metamodelling\ud technique was originally developed for physical\ud experiments that are known to have a stochastic na-\ud ¤Faculty of Engineering Technology (Applied Mechanics group),\ud University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands,\ud email: [email protected]\ud ture due to measurement noise present. This measurement\ud noise is absent in case of deterministic computer experiments\ud such as FEM simulations. Hence, an interpolation\ud model fitted by DACE is thought to be more applicable in\ud combination with metal forming simulations. Nevertheless,\ud the proposed algorithm utilises both RSM and DACE\ud metamodelling techniques.\ud As a Design Of Experiments (DOE) strategy, a combination\ud of a maximin spacefilling Latin Hypercubes Design\ud and a full factorial design was implemented, which takes\ud into account explicit constraints. Additionally, the algorithm\ud incorporates cross validation as a metamodel validation\ud technique and uses a Sequential Quadratic Programming\ud algorithm for metamodel optimisation. To overcome\ud the problem of ending up in a local optimum, the\ud SQP algorithm is initialised from every DOE point, which\ud is very time efficient since evaluating the metamodels can\ud be done within a fraction of a second. The proposed algorithm\ud allows for sequential improvement of the metamodels\ud to obtain a more accurate optimum.\ud As an example case, the optimisation algorithm was applied\ud to obtain the optimised internal pressure and axial\ud feeding load paths to minimise wall thickness variations\ud in a simple hydroformed product. The results are satisfactory,\ud which shows the good applicability of metamodelling\ud techniques to optimise metal forming processes using\ud time consuming FEM simulations

Optimising towards robust metal forming processes

Product improvement and cost saving have always been important goals in the metal forming\ud industry. Numerical optimisation can help to achieve these goals, but optimisation with a deterministic\ud approach will often lead to critical process settings, such that the slightest variation in e.g. material behaviour\ud will result in violation of constraints. To avoid a high scrap ratio, process robustness must be considered in the\ud optimisation model. Optimising for robustness includes Robust Manufacturing (RM) techniques, Optimisation\ud Under Uncertainty (OUU) methods and Finite Element (FEM) simulations of the processes. In this paper,\ud we review RM and OUU. Subsequently, the combination of Statistical Process Control (SPC), robust and\ud reliability based optimisation methods, and FEM-based process simulation implemented in AutoForm-Sigma\ud is presented. An automotive deep drawing application demonstrates the potential of strategies that optimise\ud towards robust metal forming processes