Design Optimization of the Aeronautical Sheet Hydroforming Process Using the Taguchi Method

The aluminium alloy sheet forming processes forging in rubber pad and diaphragm presses (also known as hydroforming processes) are simple and economical processes adapted to aeronautical production. Typical defects of these processes are elastic recovery, necking, and wrinkling, and they present di culties in control mainly due to property variations of the sheet material that take place during the process. In order to make these processes robust and unresponsive to material variations, a multiobjective optimization methodology based on the Taguchi method is proposed in the present study. The design of experiments and process simulation are combined in the methodology, using the nonlinear finite element method. The properties of sheet material are considered noise factors of the hydroforming process, the objective being to find a combination of the control factors that causes minimal defects to noise factors. The methodology was applied to an AA2024-T3 aluminium alloy sheet of 1 mm thickness stamping process in a diaphragm press. The results allowed us to establish the optimal pressure values, friction coeficient between sheet and block, and friction coeficient between sheet and rubber to reduce the elastic recovery variations and the minimal thickness before noise facts

Uncertain design optimization of automobile structures: A survey

In real life, there are a lot of uncertainties in engineering structure design, and the potential uncertainties will have an important impact on the structural performance responses. Therefore, it is of great significance to consider the uncertainty in the initial stage of structural design to improve product performance. The consensus can be reached that the mechanical structure obtained by the reliability and robustness design optimization method considering uncertainty not only has low failure risk but also has highly stable performance. As a large mechanical system, the uncertainty design optimization of key vehicle structural performances is particularly important. This survey mainly discusses the current situation of the uncertain design optimization framework of automobile structures, and successively summarizes the uncertain design optimization of key automobile structures, uncertainty analysis methods, and multi-objective iterative optimization models. The uncertainty analysis method in the design optimization framework needs to consider the existing limited knowledge and limited test data. The importance of the interval model as a non-probabilistic model in the uncertainty analysis and optimization process is discussed. However, it should be noted that the interval model ignores the actual uncertainty distribution rule, which makes the design scheme still have some limitations. With the further improvement of design requirements, the efficiency, accuracy, and calculation cost of the entire design optimization framework of automobile structures need to be further improved iteratively. This survey will provide useful theoretical guidance for engineers and researchers in the automotive engineering field at the early stage of product development

A geometrical robust design using the Taguchi method: application to a fatigue analysis of a right angle bracket

The majority of components used in aeronautic, automotive or transport industries are subjected to fatigue loads. Those elements should be designed considering the fatigue life. In this paper the geometrical design of a right angle bracket has been simulated by FEM, considering the type of material, the applied load and the geometry of the pieces. The geometry of the right angle bracket has been optimized using the Taguchi’s robust optimization method. As far as the authors know, there are no publications dealing with the use of the Taguchi’s robust optimization method applied to the geometric design of industrial pieces having as output variable the Findley fatigue coefficient. This paper presents a method for determining the critical design parameters to extend the product life of components subject to fatigue loading. This pioneer idea is applicable to any other design or physical or mechanical application so it can be widely used as new methodology to fatigue robust design of mechanical parts and components.La gran parte de los componentes usados en la industria aeronáutica, automovilista o del transporte están sometidos a cargas de fatiga. Esos componentes deberían ser diseñados considerando la vida a fatiga. En este artículo el diseño de una escuadra ha sido simulad por MEF teniendo en cuenta el tipo de material, cargas aplicadas y la geometría. La geometría de la escuadra se ha optimizado usando el método de optimización robusta de Taguchi. Hasta donde los autores han comprobado no hay ninguna publicación que emplee el método de optimización robusta de Taguchi aplicándolo al diseño geométrico de elementos industriales tomando como variable de salida el coeficiente de fatiga de Findley. Este artículo presenta un método para determinar los parámetros críticos en el diseño que afectan a la vida a fatiga del componente. Esta idea es pionera y aplicable a otros diseños o aplicaciones mecánicas que pueden emplear esta metodología para el diseño robusto a la fatiga de partes y componentes mecánicos

Optimization and Influence of GMAW Parameters for Weld Geometrical and Mechanical Properties Using the Taguchi Method and Variance Analysis

Gas metal arc welding is one of the arc fusion processes that is widely used in industry due to its high efficiency. The correct selection of the input parameters has direct influence on the weld quality and, with the control of those parameters, it is possible to reduce the amount of weld material, improve its properties and then increase the productivity of the process. This study intends to take a group of weld parameters and submit them to the optimization by the Taguchi Method and check the influence of those through a Variance Analysis (ANOVA). An L9 orthogonal array gathered three parameters (weld voltage, weld speed and weld torch angle) into three levels, then, with all combinations set and performed, the macrography and the transversal tensile strength test provided, respectively, the geometrical and the mechanical properties. The signal-to-noise ratios enable the optimization and the ANOVA provided the influence of the input parameters on the response parameters. The weld speed appeared as the most influent parameter for the weld geometry, contributing 63.54% to reinforcement, 66.36% to width and 66.94% to penetration, and the weld torch angle the most influent to the ultimate transversal tensile strength (41.39%). The optimum levels to the reinforcement are 22.4 [V], 400 [mm/min] and 30 [°], to the width 22.4 [V], 300 [mm/min], 0 [°], to the penetration 23.3 [V], 400 [mm/min], 0 [°] and, lastly, to the ultimate transversal tensile strength 24.1 [V], 200 [mm/min], 15 [°]. The Taguchi method showed to be suitable for this kind of problem and giving an efficient experiment design and good results. Keywords: Taguchi method, Optimization, GMA

A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

An effective hybrid algorithm is proposed for solving multiobjective optimization engineering problems with inequality constraints. The weighted sum technique and BFGS quasi-Newton’s method are combined to determine a descent search direction for solving multiobjective optimization problems. To improve the computational efficiency and maintain rapid convergence, a cautious BFGS iterative format is utilized to approximate the Hessian matrices of the objective functions instead of evaluating them exactly. The effectiveness of the proposed algorithm is demonstrated through a comparison study, which is based on numerical examples. Meanwhile, we propose an effective multiobjective optimization strategy based on the algorithm in conjunction with the surrogate model method. This proposed strategy has been applied to the crashworthiness design of the primary energy absorption device’s crash box structure and front rail under low-speed frontal collision. The optimal results demonstrate that the proposed methodology is promising in solving multiobjective optimization problems in engineering practice

Design for Vehicle Structural Crashworthiness Via Crash Mode Matching.

Vehicle crashworthiness is an important design attribute which designers strive to improve. However, design for structural crashworthiness is a difficult task. A vehicle structure must have strength to shield the passenger compartment, as well as compliance to cushion the impact energy. The best known analysis method for crashworthiness performance prediction is nonlinear finite element (FE). FE analysis of detailed vehicle models requires enormous computational resources thereby hindering the success of general-purpose optimization approaches. An approach which is more of an art than a formal procedure is that of crash mode matching. Qualitatively, the crash mode is the observed gross-motion of the structure and its time history of deformation in various zones. Crash mode matching involves adjusting the design variables of the structure in order to achieve a desirable structural deformation history, which an experienced designer can typically do while requiring only a few trial FE runs. This dissertation to develops an algorithmic methodology by formalizing this crash mode matching approach. A quantitative representation of the crash mode is introduced as a matrix of time series of the structural deformation history, with dimensions of the matrix being the structural location and type of deformation. A comparison metric is then introduced for the degree of matching between crash modes as the integral of the error between the time series. An automated algorithm for crash mode matching heuristically directs stochastic sampling of the design space by adjusting the mean and standard deviation of normal distributions on the design variables. Adjustment of the mean and standard deviation is performed via Fuzzy logic rules that are defined by the algorithm user in analogy to the type of decisions that an experienced designer would make. Stochastic search allows for global convergence properties, as well as accounting for different expert designers sometimes having different opinions on how to modify a design. Implementation of the proposed framework is applied to two real-life case studies involving front half of a vehicle, as well as full vehicle models. The studies show success in attaining high performance designs, while requiring a modest number of FE runs, hence reasonable computational resources.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60666/1/khamza_1.pd

Analysis of a Carbon Fiber Reinforced Polymer Impact Attenuator for a Formula SAE Vehicle Using Finite Element Analysis

The Hashin failure criteria and damage evolution model for laminated fiber reinforced polymers are explored. A series of tensile coupon finite element analyses are run to characterize the variables in the physical model as well as modeling techniques for using an explicit dynamic solver for a quasi-static problem. An attempt to validate the model on an axial tube crush is presented. It was found that fiber buckling was not occurring at the impactor-tube interface. Results and speculation as to why the failure initiation is incorrect are discussed. Lessons learned from the tube crush are applied successfully to the quasi-static Formula SAE nosecone crush test. The model is validated by experimental data and the impact metrics between the test and model are within 5%. Future work and possible optimization techniques are discussed

Multi-objective crashworthiness design optimization of a rollover protective structure by an improved constraint-handling technique

This study proposes a multi-objective optimization (MOO) strategy with an improved constraint-handling technique to improve the crashworthiness of an excavator rollover protective structure (ROPS). First, the experimental test under the ISO 12117 criteria is conducted and the developed numerical model is verified. Then, the amounts of energy absorption and the cross-sectional forces of components in the ROPS are analyzed. The main energy absorbing and load carrying components are identified. Finally, the thicknesses of the identified components are considered as the design variables. A multi-objective crashworthiness optimization process aims at improving the safety distance and reducing the total mass is designed by the finite element analysis-based surrogate model technique and a modified MOO algorithm. The proposed algorithm modifies the objective function values of an individual with its constraint violations and the true objective function values, of which adaptive penalty weights fed back from the constraint violations are used to keep the balance. Compared with the existing methods, it is found that the optimal solutions obtained by the proposed algorithm show superiority on convergence rate and diversity of distribution. The optimal results show that the safety distance is 27.42% higher while the total mass is 7.06% lower than those of the baseline design when it meets the requirements of ISO 12117. This study provides an alternative crashworthiness design route for the ROPS of the construction machines

Application of Surrogate Based Optimisation in the Design of Automotive Body Structures

The rapid development of automotive industry requires manufacturers to continuously reduce the development cost and time and to enhance the product quality. Thus, modern automotive design pays more attention to using CAE analysis based optimisation techniques to drive the entire design flow. This thesis focuses on the optimisation design to improve the automotive crashworthiness and fatigue performances, aiming to enhance the optimisation efficiency, accuracy, reliability, and robustness etc. The detailed contents are as follows: (1) To excavate the potential of crash energy absorbers, the concept of functionally graded structure was introduced and multiobjective designs were implemented to this novel type of structures. First, note that the severe deformation takes place in the tubal corners, multi-cell tubes with a lateral thickness gradient were proposed to better enhance the crashworthiness. The results of crashworthiness analyses and optimisation showed that these functionally graded multi-cell tubes are preferable to a uniform multi-cell tube. Then, functionally graded foam filled tubes with different gradient patterns were analyzed and optimized subject to lateral impact and the results demonstrated that these structures can still behave better than uniform foam filled structures under lateral loading, which will broaden the application scope of functionally graded structures. Finally, dual functionally graded structures, i.e. functionally graded foam filled tubes with functionally graded thickness walls, were proposed and different combinations of gradients were compared. The results indicated that placing more material to tubal corners and the maximum density to the outmost layer are beneficial to achieve the best performance. (2) To make full use of training data, multiple ensembles of surrogate models were proposed to maximize the fatigue life of a truck cab, while the panel thicknesses were taken as design variables and the structural mass the constraint. Meanwhile, particle swarm optimisation was integrated with sequential quadratic programming to avoid the premature convergence. The results illustrated that the hybrid particle swarm optimisation and ensembles of surrogates enable to attain a more competent solution for fatigue optimisation. (3) As the conventional surrogate based optimisation largely depends on the number of initial sample data, sequential surrogate modeling was proposed to practical applications in automotive industry. (a) To maximize the fatigue life of spot-welded joints, an expected improvement based sequential surrogate modeling method was utilized. The results showed that by using this method the performance can be significantly improved with only a relatively small number of finite element analyses. (c) A multiojective sequential surrogate modeling method was proposed to address a multiobjective optimisation of a foam-filled double cylindrical structure. By adding the sequential points and updating the Kriging model adaptively, more accurate Pareto solutions are generated. (4) While various uncertainties are inevitably present in real-life optimisations, conventional deterministic optimisations could probably lead to the violation of constraints and the instability of performances. Therefore, nondeterministic optimisation methods were introduced to solve the automotive design problems. (a) A multiobjective reliability-based optimisation for design of a door was investigated. Based on analysis and design responses surface models, the structural mass was minimized and the vertical sag stiffness was maximized subjected to the probabilistic constraint. The results revealed that the Pareto frontier is divided into the sensitive region and insensitive region with respect to uncertainties, and the decision maker is recommended to select a solution from the insensitive region. Furthermore, the reduction of uncertainties can help improve the reliability but will increase the manufacturing cost, and the tradeoff between the reliability target and performance should be made. (b) A multiobjective uncertain optimisation of the foam-filled double cylindrical structure was conducted by considering randomness in the foam density and wall thicknesses. Multiobjective particle swarm optimisation and Monte Carlo simulation were integrated into the optimisation. The results proved that while the performances of the objectives are sacrificed slightly, the nondeterministic optimisation can enhance the robustness of the objectives and maintain the reliability of the constraint. (c) A multiobjective robust optimisation of the truck cab was performed by considering the uncertainty in material properties. The general version of dual response surface model, namely dual surrogate model, was proposed to approximate the means and standard deviations of the performances. Then, the multiobjective particle optimisation was used to generate the well-distributed Pareto frontier. Finally, a hybrid multi-criteria decision making model was proposed to select the best compromise solution considering both the fatigue performance and its robustness. During this PhD study, the following ideas are considered innovative: (1) Surrogate modeling and multiobjective optimisation were integrated to address the design problems of novel functionally graded structures, aiming to develop more advanced automotive energy absorbers. (2) The ensembles of surrogates and hybrid particle swarm optimisation were proposed for the design of a truck cab, which could make full use of training points and has a strong searching capacity. (3) Sequential surrogate modeling methods were introduced to several optimisation problems in the automotive industry so that the optimisations are less dependent on the number of initial training points and both the efficiency and accuracy are improved. (4) The surrogate based optimisation method was implemented to address various uncertainties in real life applications. Furthermore, a hybrid multi-criteria decision making model was proposed to make the best compromise between the performance and robustness