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The vehicle structure is a highly complex system as it is subject to different requirements of many engineering disciplines. Multidisciplinary optimization (MDO) is a simulation-based approach for capturing this complexity and achieving the best possible compromise by integrating all relevant CAE-based disciplines. However, to enable operative application of MDO even under consideration of crash, various adjustments to reduce the high numerical resource requirements and to integrate all disciplines in a target way must be carried out. They can be grouped as follows: The use of efficient optimization strategies, the identification of relevant load cases and sensitive variables as well as the reduction of CAE calculation time of costly crash load cases by so-called finite element (FE) submodels. By assembling these components in a clever way, a novel, adaptively controllable MDO process based on metamodels is developed. There are essentially three special features presented within the scope of this paper: First, a module named global sensitivity matrix which helps with targeted planning and implementation of a MDO by structuring the multitude of variables and disciplines. Second, a local, heuristic and thus on all metamodel types computable prediction uncertainty measure that is further used in the definition of the optimization problem. And third, a module called adaptive complexity control which progressively reduces the complexity and dimensionality of the optimization problem. The reduction of resource requirements and the increase in the quality of results are significant, compared to the standard MDO procedure. This statement is confirmed by providing results for a FE full vehicle example in six load cases (five crash load cases and one frequency analysis).

Aim of this work is to improve the bond between a strain sensor and a device on which the strain shall be determined. As strain sensor, a CMOS-integrated chip featuring piezoresistive sensor elements was used which is capable of wireless energy and data transmission. The sensor chip was mounted on a standardized tensile test specimen of stainless steel by a bonding process using reactive multilayer systems (RMS). RMS provide a well-defined amount of heat within a very short reaction time of a few milliseconds and are placed in-between two bonding partners. RMS were combined with layers of solder which melt during the bonding process. Epoxy adhesive films were used as a reference bonding process. Under mechanical tensile loading, the sensor bonded with RMS shows a linear strain sensitivity in the whole range of tested forces whereas the adhesive-bonded sensor has slightly nonlinear behavior for low forces. Compared to the adhesive-bonded chips, the sensitivity of the reactively bonded chips is increased by a factor of about 2.5. This indicates a stronger mechanical coupling by reactive bonding as compared to adhesive bonding.

In this paper we present a new procedure using the graph and heuristic based topology optimization in order to find layouts for three-dimensional frame structures under crash loads. A three-dimensional graph describes the geometry and is used to derive a finite element shell model. The model of the frame structure consists of different profiles with continuous cross-sections. The ends of the profiles are currently rigidly connected. Each cross-section is defined by an individual two-dimensional graph. After performing a simulation its results are used by competing heuristics to propose new topologies for the frame structure. Most of these heuristics are derived from expert knowledge. Over several iterations, the goal is to improve the structures mechanical behavior. Typical objectives are the minimization of the structural intrusion in a crash scenario or the minimization of the maximal contact force between structural components. The presented method includes topology optimization by heuristics and shape optimization respectively sizing by mathematical optimization algorithms. The new flexible syntax for three- and two-dimensional graphs, the optimization process and the currently used heuristics are described. The performance is demonstrated for two examples, each optimized twice with opposing objectives.

The majority of parts in modern car bodies is manufactured from sheet metal. Rarely these parts are fully stressed due to design space restrictions and complex requirements. The usage of tailor rolled blanks (TRB) enables the reduction of sheet thickness in areas less loaded and thus reduces part weight. Technically most sheet metal parts are potentially suited for the application of TRB. Economic circumstances like the additional flexible rolling process and technology-specific nesting constraints limit the application to a subset of parts. The search for the best candidate parts taking mass and cost into account is currently challenging. This article presents an optimization strategy for the selection of the parts in a vehicle structure that are best suited for the application of TRB. As a first step, a priori preferencing is performed to select parts based on engineering rules. Using a reduced number of candidate parts, high quality metamodels are trained to perform multiobjective optimizations of all possible combinations of remaining parts, revealing the most efficient part selection under consideration of mass and cost.

In a market-driven economy, the availability of an evaluation of the manufacturability cost and economic efficiency right on from the early design phase is a competitive advantage. For this reason, a methodology is proposed in this work which enhances the planning capabilities and efficiency by automatic provision of steel constructions with manufacturing plans, cost calculation and evaluation in the early phase. To achieve this, the design process of steel structures is enhanced with aspects from the cost-domain and the manufacturing-domain. Furthermore, digital tools and methods of modern software engineering are applied to classical engineering design in order to handle increased product complexity and save design time in shortened product life-cycles. As a solution approach, so-called graph-based design languages are used and evaluated by means of steel construction examples.

The simulation of load application elements requires the modeling of the contact point and a nonlinear analysis. This contact analysis is still time-consuming despite of powerful computers. A reduction of this contact by a simple load model would result in enormous time savings. The Hertzian contact theory provides an analytical approach to the contact problem. However, an isotropic material behavior is assumed, which is problematic especially with fiber reinforced structures. Nevertheless, a suitable load model can be developed for a simplified model of a bolt joint. The edge effects occurring at the edge of the hole are determined using an approximation function (parameterized polynomial approach). The anisotropic material behavior is represented by alternative models or it can also be integrated into the calculation by an extension of Hertzian theory. The different approaches are compared in respect of accuracy, complexity and computing time. For reference and verification of the results, a contact model is created using the FEM software HyperMesh and Optistruct from Altair. Besides the contact model can be used as an aid for creating the load model. Finally, a method is presented, which reduces a contact analysis to a purely linear static structural analysis and thus enables a significantly reduced computing time. The corresponding load model also gives a good representation of reality.

The design process of fiber-reinforced plastics (FRP) is a challenging task, especially concerning passenger vehicles in crashworthiness applications where manufacturing limitations and requirements regarding passive safety have to be considered. Numerical optimization can be a helpful tool during the design process, but most available methods are not applicable because analytical sensitivities are not available in crash simulations. The Graph and Heuristic based Topology Optimization (GHT) can be utilized to optimize the topology of cross-sections of crashworthiness structures while fulfilling a wide range of manufacturing constraints, but it has to be extended for composites. Since the topology changes during optimization runs, the stress state changes as well. This demands high predictive capabilities on the material model. This paper presents the necessary adjustments to describe composite profile structures within the GHT method. A commercial material model for LS-Dyna is parameterized and used for the calculation process.

Under public health emergencies, particularly an early epidemic, it is fundamental that genetic and other healthcare data is shared across borders in both a timely and accurate manner before the outbreak of a global pandemic. However, although the COVID-19 pandemic has created a tidal wave of data, most patient data is siloed, not easily accessible, and due to low sample size, largely not actionable. Based on the precision medicine platform Shivom, a novel and secure data sharing and data analytics marketplace, we developed a versatile pandemic preparedness platform that allows healthcare professionals to rapidly share and analyze genetic data. The platform solves several problems of the global medical and research community, such as siloed data, cross-border data sharing, lack of state-of-the-art analytic tools, GDPR-compliance, and ease-of-use. The platform serves as a central marketplace of ‘discoverability’. The platform combines patient genomic & omics data sets, a marketplace for AI & bioinformatics algorithms, new diagnostic tools, and data-sharing capabilities to advance virus epidemiology and biomarker discovery. The bioinformatics marketplace contains some preinstalled COVID-19 pipelines to analyze virus- and host genomes without the need for bioinformatics expertise. The platform will be the quickest way to rapidly gain insight into the association between virus-host interactions and COVID-19 in various populations which can have a significant impact on managing the current pandemic and potential future disease outbreaks.

Essential quality features of pressure sensors are, among other accuracy-related factors, measurement range, operating temperature, and long-term stability. In this work, these features are optimized for a capacitive pressure sensor with a measurement range of 10 bars. The sensor consists of a metal membrane, which is connected to a PCB and a digital capacitive readout. To optimize the performance, different methods for the joining process are studied. Transient liquid phase bonding (TLP bonding), reactive joining, silver sintering, and electric resistance welding are compared by measurements of the characteristic curves and long-term measurements at maximum pressure. A scanning electron microscope (SEM) with energy-dispersive X-ray spectroscopy (EDX) analysis was used to examine the quality of the joints. The evaluation of the characteristic curves shows the smallest measurement errors for TLP bonding and sintering. For welding and sintering, no statistically significant long-term drift was measured. In terms of equipment costs, reactive joining and sintering are most favorable. With low material costs and short process times, electric resistance welding offers ideal conditions for mass production.

This paper presents a new approach for optimizing shell structures considering their mid surface design including cut-outs. Therefore we introduced a manufacturing constraint to the 3D topology optimization based on the density method in order to receive an optimized structure without undercuts and with a constant wall thickness, so that these structures can be manufactured by deep drawing in one step. It is shown that introducing cut-outs while increasing the shell thickness can improve the performance of shell structures considering their stiffness at a constant mass.

Die Form bzw. Gestalt von Gebrauchsgegenstanden ist oft ein Kompromiss aus Asthetik und mechanischen Anforderungen. Asthetik und Mechanik muss aber nicht zwingend konkurrieren, was viele Strukturen in der Natur beweisen. Diese Formen entwickeln sich basierend auf den vorliegenden Kraften.

In diesem Kapitel werden die mathematischen Grundlagen fur die Optimierung behandelt. Diese Grundlagen sind das Rustzeug, um die Arbeitsweise der in Kap. 4 vorgestellten Optimierungsverfahren verstehen zu konnen.

Die Topologie eines Bauteils, d. h. die Lage und Anordnung von Strukturelementen, kann das Strukturverhalten entscheidend beeinflussen. Eine Topologieoptimierung muss deshalb in einem sehr fruhen Stadium des Entwurfsprozesses erfolgen. Die meisten heutigen Topologieoptimierungsverfahren geben Design-Vorschlage basierend auf wenigen Vorgaben wie zulassiger Entwurfsraum, Lagerung und Belastung. In der Regel werden einfache Optimierungsfunktionale, wie das Gewicht und die mittlere Nachgiebigkeit (vgl. Abschn. 2.4) berucksichtigt. Diese Design-Vorschlage mussen dann in eine Konstruktion umgesetzt werden. In diesem Kapitel werden zunachst diese Verfahren behandelt. Die direkte Berucksichtigung aller in der Formoptimierung ublichen Optimierungsfunktionale ist nur mit der parametrischen Beschreibung der Bauteilrander moglich. Dies fuhrt zu wesentlich aufwendigeren Verfahren, welche danach beschrieben werden.

Die effiziente Anwendung von Verfahren der Strukturoptimierung erfordert die Kenntnis des eigenen Handlungsrahmens im Produktentwicklungsprozess. Um diesen abzuschatzen, sind Grundkenntnisse der Konstruktionsprinzipen, der Verfahren der Strukturanalyse und des Produktentwicklungsprozesses selbst notwendig. Werden die Rahmenbedingungen nicht berucksichtigt, lasst sich ein Optimierungsergebnis nicht in ein Produkt umsetzen. Dieses Kapitel umreist das notwendige Basiswissen zur Abschatzung des eigenen Handlungsrahmens.

In diesem Kapitel werden zum einen Optimierungen fur ausgewahlte Einsatzgebiete genauer beschrieben. Allen diesen Optimierungen ist gemein, dass die Aufbereitung der Simulationsmodelle im Vergleich zu den bisher in diesem Buch vorgestellten Anwendungen wesentlich aufwandiger ist. Sie zeigen aber typische Aufwande industrieller Bauteiloptimierungen. Zum anderen werden Themenbereiche der weitergehenden Forschungen zur Optimierung mechanischer Strukturen besprochen. Daraus sind Trends der nachsten Jahre ableitbar.

In this work investigations on TLP-bonding with a binary alloy consisting of Ag and Sn are presented. This joining technology allows stress-reduced mounting with a temperature-stable metallic interconnection. To analyze the influence of the process parameters a bonding-press that is capable of high heating rates up to 25 K/s was built and methods of DoE were used. The bonding pressure showed the most significant impact, while heating rate, time and temperature had a minor influence. The influence on the microporosity is demonstrated by SEM micrographs of ion-milled cross sections.

The feasibility of printing structures made of high-alloy TRIP steel by Electron Beam Melting (EBM) is demonstrated in this study. Three different structure geometries are discussed. During the melting process, approximately 1.4 wt.% manganese evaporate independent of the printed structure. The structures differ in the arrangement of cell walls and the local material distribution in the in-plane array. To evaluate the strength of the structures, quasi-static compression tests were carried out in out-of-plane and in-plane direction. It is shown, that a homogenous distribution of cell walls over the structure cross section and stiffened cell walls with short distances between the cell wall nodes contribute to a high compressive strength.

Stability is an important issue in topology optimization, since results of the optimization are often framework structures. If some trusses of these structures are subjected to compression, they maybe buckle and the structure fails.

In the simulation of crashworthiness structures different kinds of nonlinearities like large deformations, contact and material nonlinearities appear. In order to optimize the topology of crashworthiness profile structures, the Graph and Heuristic Based Topology Optimization (GHT) was introduced. This method uses heuristics that are derived from expert knowledge to change the topology through adding or removing walls from the profile.

In this paper the Topological Derivative for crash loaded structures is derived with the adjoint sensitivity analysis. The main idea of the adjoint sensitivity analysis is to circumvent the direct calculation of the sensitivity of the displacement field. Instead, the adjoint equilibrium equation has to be solved. In this approach, material derivation and partial integration in the time domain are applied to the Topological Derivative. This ensures, that the inertial effects are kept, as they are important for a reliable crash simulation. The result is a backward integration scheme for the adjoint state.

Highly conductive fillers have a strong influence on improving the poor out of plane thermal conductivity of carbon fiber reinforced composites. The objective of this study has been to investigate the role of the diamond powder (DP) in enhancing the out-of-plane thermal conductivity of the woven composites. Samples of the standard modulus T300 carbon fiber composite with 44% and 55% fiber volume fraction and the high modulus YS90A carbon fiber composite with 50% volume fraction were fabricated with their matrices comprising of neat epoxy and different loading of diamond powder within epoxy resin. Steady state thermal conductivity measurements were carried out and it was found from the measurements that the out of plane thermal conductivity of the standard modulus composite increased by a factor of 2.3 with 14% volume fraction of diamond powder in the composite while the out of plane thermal conductivity of the high modulus composite increased by a factor of 2.8 with 12% volume fraction of diamond powder in the composite. Finite Element Modeling (FEM) with the incorporation of microstructural characteristics is presented and good consistency between the measurements and FEM results were observed.

Unlocking the value of epigenetic data for actionable insights will drive aging research, precision medicine, and ultimately population health. Fundamental questions should be addressed by integrative personal omics profiling with epigenomics at its center, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from an individual to reveal dynamic molecular changes in health and disease. With the evolution of better technologies and digital capacity, enormous amounts of omics data will be produced and stored in the digital space and researchers will need artificial intelligence (AI) to be able to keep track of it. AI is already transforming the world of medicine and will help healthcare providers make faster and more accurate diagnoses. Based on epigenetic data, deep learning algorithms will predict the risk of a disease in time to prevent it and will help scientists understand how interindividual epigenetic variability leads to disease. However, ensuring security and privacy in transmitting and storing personal epigenetic profiles will require building a new and open multiomics data ecosystem. Blockchain, an open-source technology that uses a distributed database for secure transactions, has the potential to address many of the challenges related to security and privacy with personal health information. Blockchain technology enables integrating data from a distributed network of participants in the healthcare value chain on a global scale. We are in the initial stages of a revolution in precision medicine enabled by advanced technologies such as epigenomics, AI, and blockchain. The pioneering effort by early adopters from the research space is critical for putting these technologies within reach of the broader healthcare ecosystem.

Tailor Rolled Blanks (TRB) are an established lightweight application for highly stressed structural parts in automotive industry. By varying the rolling gap, parts with load adapted thickness profiles and continuous transitions are manufactured. The rolling process itself is subjected to several manufacturing constraints which have to be considered in the numerical optimization. It has been shown that the thickness run parametrization has significant impact on the optimization result and the associated computational cost.

Today, the product development process is characterized by increasing diversity. A trend towards customer-tailored products can be observed. This trend demands new processes for product development and manufacturing. Increasing product individuality up to lot size one can be faced with methods, which automate the design process and avoid redundant manual work. As the number of tools involved in the product development process is ever-increasing, one goal is to eliminate the distribution of knowledge into several software tools and begin with one central and consistent data model from where all the software tools used can be triggered automatically. This goal can be addressed by using graph-based design languages [1], which themselves are based on the Unified Modeling Language (UML). On the manufacturing side, one technology can be seen in the additive manufacturing process. For finding a good structure, a combination of additive manufacturing and topology optimization can be advantageous because of the ability of additive manufacturing to create almost arbitrary geometry. Using the example of a lightweight quadrocopter, we propose a graph-based design language which integrates topology optimization and can cover different aspects of the multi-disciplinary product development process: requirements, abstract product functions, design constraints (i.e. equations) and costs among others. The topology optimization triggered by the design language can take into account different product configurations (i.e. packaging configurations) and accordingly makes design proposals (i.e. structural proposals for the frame of the quadrocopter). The executable nature of the graph-based design language reduces the design time significantly.

Efficiently and systematically extracting knowledge from design of experiments studies in the field of vehicle crash simulation is increasingly challenging due to the number and size of the simulation models and the complexity of the occurring mechanisms. As an alternative to common statistical postprocessing, this paper proposes the usage of data-mining methods from the field of knowledge discovery in databases to condensate important effects and give insight into their origins.

This paper deals with a topology optimization scheme for crash loaded structures. The presented approach combines the numerical calculation of Topological Derivatives with a level-set method. In a microscale investigation on a planar shell with a hole under perpendicular stresses a meta-model for the Topological Derivatives is derived. This meta-model provides the sensitivities for the optimization of the macroscale mechanical problem. The iterative volume reduction with a level-set method, a linear mapping scheme and filtering allows the crash simulation with smooth boundaries. An academic example illustrates the applicability of the optimization scheme.

The structural optimization of large crashworthy systems like a vehicle body in a crash loaded case is a time consuming and costly process. The computation time can be reduced by dividing the large system (main model) into small systems called submodels. These submodels can be effectively used in the optimization to shorten the response time of the simulation. The generation of submodels by hand is challenging and requires a lot of effort and knowledge to create and validate them. This paper presents a workflow to automatically generate and validate the submodels using various mathematical functions.

Topology optimization results based on the homogenization or density method highly depend on the discretization of the design space. The smallest possible dimension of the optimized structure is one element edge length. If the mesh can be refined, smaller design features can be represented and thereby the performance of the optimized structures can be improved. But the mesh refinement is limited by the computational cost of the Finite Element Analysis (FEA).

A promising approach in reducing weight is using thin shell structures with supporting ribs instead of thick shell structures, in order to only use the material where it is needed. Nevertheless, the optimization of these structures remains a big challenge, especially in crash load cases, because of material nonlinearities, contact and large displacements.

In this work, the dynamics of the bonding process using reactive multilayer systems (RMS) is studied by use of acceleration sensors. A piezoelectric accelerometer with a wide dynamic range was selected to measure the vertical acceleration during the reaction process using a Ni/Al RMS foil. This sensor was integrated into a die bonding tool. Then, bonding experiments were performed with dummy plates and substrates by igniting the RMS and measuring the sensor signal simultaneously. It was found that a strong sensor signal occurs at the time when the reaction of the RMS is completed. By additional cross sectional analysis of the bonding area, the correlation between signal height and total volume contraction has been investigated. The obtained results demonstrate that this experimental setup can be used to find materials and process conditions for bonding with significantly reduced shock-wave, lowering the risk of damage to the assembled device.

This contribution deals with the mathematical description and algorithmization of expert knowledge so that this expert knowledge can be used within automatic optimization procedures with a focus on the topology optimization of crashworthiness structures. Up to now, a purely mathematical topology optimization of crashworthiness structures is not possible. The reasons for that are the nonlinearities coming from the large displacements and rotations, contact phenomena and complex material behaviour (e.g. plasticity, failure and strain rate dependency). One approach to overcome these problems by using heuristics based on expert knowledge and an application example are shown in this contribution. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)

For the efficient development of new structural concepts, it is necessary to perform an optimization of the mechanical properties of profile cross-sections taking into account all relevant load cases. Especially for crash load cases, there currently exists no established method for the conceptual design and topology optimization. The main problems are the complicated deformation conditions in the crash, the huge number of design variables, the existence of simulative and physical bifurcation points and the costly determination of sensitivity information. For the application area of developing profile cross-sections, the method presented in this paper attempts to overcome these shortfalls. It has been developed for the combined topology, shape and sizing optimization taking into account all relevant crash load cases. For this a flexible description of the structure's profile cross-section via mathematical graphs is used. Modifications of the structure's topology are performed with heuristics (rules), which are based on expert knowledge, whereas the automatically generated shape und sizing parameters of the structure are optimized with mathematic optimization algorithms.