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

Temporal Shape Changes and Future Trends in European Automotive Design

Evolution produces genuine novelty in morphology through the selection of competing designs as phenotypes. When applied to human creativity, the evolutionary paradigm can provide insight into the ways that our technology and its design are modified through time. The shape of European utilitarian cars in the past 60 years was analyzed in order to determine whether changes occur in a gradual fashion or through saltation, clarifying which are the more conserved and more variable parts of the designs. We also attempted to predict the future appearances of the cars within the next decade, discussing all results within the framework of relevant evolutionary-like equivalences. Here, we analyzed the modification in the shape of European utilitarian cars in the past 60 years by three-dimensional geometric morphometrics to test whether these changes occurred in a gradual or more saltatory fashion. The geometric morphometric shape analysis showed that even though car brands have always been preserving distinct shapes, all followed a gradual pattern of evolution which is now converging toward a more similar fusiform and compact asset. This process was described using Darwinian evolution as a metaphor to quantify and interpret changes over time and the societal pressures promoting them.This research was funded by project RITFIM (ref. CTM2010-16274, of the Spanish national RTD program. J. Aguzzi is a postdoctoral Fellow of the Spanish Ramón y Cajal Program (Spanish Ministry of Economy and Competitiveness).We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).Peer reviewe

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

Automatic and efficient driving strategies while approaching a traffic light

Vehicle-infrastructure communication opens up new ways to improve traffic flow efficiency at signalized intersections. In this study, we assume that equipped vehicles can obtain information about switching times of relevant traffic lights in advance. This information is used to improve traffic flow by the strategies 'early braking', 'anticipative start', and 'flying start'. The strategies can be implemented in driver-information mode, or in automatic mode by an Adaptive Cruise Controller (ACC). Quality criteria include cycle-averaged capacity, driving comfort, fuel consumption, travel time, and the number of stops. By means of simulation, we investigate the isolated strategies and the complex interactions between the strategies and between equipped and non-equipped vehicles. As universal approach to assess equipment level effects we propose relative performance indexes and found, at a maximum speed of 50 km/h, improvements of about 15% for the number of stops and about 4% for the other criteria. All figures double when increasing the maximum speed to 70 km/h.Comment: Submitted to ITSC - 17th International IEEE Conference on Intelligent Transportation System

DEVELOPMENT OF A DESIGN METHOD TO REDUCE CHANGE PROPAGATION EFFECTS

ABSTRACT This dissertation presents a design method to reduce engineering changes caused due to change propagation effect. The method helps designers to systematically plan a verification, validation, and test (VV&T) plan. The rationale behind such a method is founded on a well-accepted principle that a robust validation plan can reduce design changes. However, such method has not yet been developed in mechanical engineering domain, so a method from software engineering has been adopted and extended to address the limitations in the existing design evaluation tools. Tools extensively used in industry, such as FMEA, and in academia have been reviewed to determine if they can identify different propagation pathways including variant, behavior, organization, and geometric pathways. As a result, it is found that variant and organizational pathways are not identified in any of these tools -- propagation in these pathways have caused major product failure in commercial vehicle and automatic fire sprinkler manufacturing industries. A seven-step VV&T method is proposed to address the aforementioned gap in which each step is tailored to suit mechanical engineering needs. The major contribution is developing the construct to identify variant and organization pathways and a prescriptive method. It has been validated in a leading commercial vehicle manufacturer, one of the passenger car manufacturing giants, and an automatic fire sprinkler manufacturer. The results from these three companies indicate the proposed VV&T method enables designers to identify variant and organizational pathways and evaluate them, which in turn can reduce design changes due to propagation effects. Objective evidence obtained from the fire sprinkler manufacturing company supports this claim. \u27If we know what assembly combination to test with, testing is not a problem...and if it can prevent a failure of this magnitude --I think this method can --it can be extremely beneficial...\u27 - Project engineer, commercial vehicle manufacture

Design and Analysis of a Double Lead Screw Household Trash Compactor Using a Static Simulation

Waste is a serious problem, especially household waste. Therefore, an idea was developed to overcome the existing household waste problem in the form of a trash compactor. This research proposes on designing the trash compactor using a double lead screw mechanism. The trash compactor uses an electric motor for its driving force and a set of gearbox for speed reduction. The trash compactor was designed, modelled, and simulated by using Autodesk Inventor Professional 2022. The simulation is based on a static simulation. The results show that the design of lead screw, frame, and ram size has overcome the 20000N of trash compactor capacity are according to the material strength and displacement for each components with safety factor more than 1.4. The trash compactor has a total capacity of 245.76 liter

Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models

A full-scale crash test is conventionally used for vehicle crashworthiness analysis. However, this approach is expensive and time-consuming. Vehicle crash reconstructions using different numerical modelling approaches can predict vehicle behavior and reduce the need for multiple full-scale crash tests, thus research on the crash reconstruction has received a great attention in the last few decades. Among modelling approaches, lumped parameters models (LPM) and finite element models (FEM) are commonly used in the vehicle crash reconstruction. This thesis focuses on developing and improving the LPM for vehicle frontal crash analysis. The study aims at reconstructing crash scenarios for vehicle-to-barrier (VTB), vehicleoccupant (V-Occ), and vehicle-to-vehicle (VTV), respectively. In this study, a single mass-spring-damper (MSD) is used to simulate a vehicle to-barrier or a wall. A double MSD is used to model the response of the chassis and passenger compartment in a frontal crash, a vehicle-occupant, and a vehicle-tovehicle, respectively. A curve fitting, state-space, and genetic algorithm are used to estimate parameters of the model for reconstructing the vehicle crash kinematics. Further, the piecewise LPM is developed to mimic the crash characteristics for VTB, VO, and VTV crash scenarios, and its predictive capability is compared with the explicit FEM. Within the framework, the advantages of the proposed methods are explained in detail, and suggested solutions are presented to address the limitations in the study.publishedVersio