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Additive manufacturing (AM) of components using material extrusion (MEX) offers the potential for the integration of functions through the use of multi-material design, such as sensors, actuators, energy storage, and electrical connections. However, there is a significant gap in the availability of electrical composite properties, which is essential for informed design of electrical functional structures in the product development process. This study addresses this gap by systematically evaluating the resistivity (DC, direct current) of 14 commercially available filaments as unprocessed filament feedstock, extruded fibers, and fabricated MEX-structures. The analysis of the MEX-structures considers the influence of anisotropic electrical properties induced by the selective material deposition inherent to MEX. The results demonstrate that composites containing fillers with a high aspect ratio, such as carbon nanotubes (CNT) and graphene, significantly enhance conductivity and improve the reproducibility of MEX structures. Notably, the extrusion of filaments into MEX structures generally leads to an increase in resistivity; however, composites with CNT or graphene exhibit less reduction in conductivity and lower variability compared to those containing only carbon black (CB) or graphite. These findings underscore the importance of filler selection and composition in optimizing the electrical performance of MEX structures.

Additive manufacturing provides new possibilities in product design compared to traditional manufacturing processes. Particularly additive material extrusion offers the freedom to combine multiple materials in a single component without additional steps. However, combining multiple materials often leads to reduced adhesion, which can hinder the creation of high-strength designs. This issue can be largely mitigated using the geometric freedom of additive manufacturing to produce interlocking structures. This publication investigates the use of lattice structures as interlocking bonds in multi-material applications. The aim is to aid the design of suitable lattice structures by collecting geometric freedoms of lattices, application requirements, and manufacturing constraints, for this information to be used in suitable designs in the future. Initially, the general design freedoms of lattice structures are compiled and explained. Subsequently, these design freedoms are narrowed down based on the specific requirements for interlocking bonds and the limitations imposed by geometry and material combinations during manufacturing. The publication concludes with design recommendations that can be used as the basis for interlock bonds. Suitable lattice designs should aim for high interconnectivity, interconnected porosity, and a high number of similar strut structures, all the while maintaining low dimensions in the interface direction.

The integration of continuous fibers into additively manufactured plastic components greatly enhances their mechanical properties. Nonetheless, a challenge lies in the limited adaptability of these properties to specific loading scenarios. To address this issue, present research aims to exploit the hybridization of additively manufactured composites, combining diverse fiber materials within a single component. Specifically, fabricating continuous fiber-reinforced hybrid composites through material extrusion enables high flexibility in adapting local fiber materials. To further the understanding of the effects of hybrid fiber reinforcement in additively manufactured plastic parts, this study investigates how distinct stacking sequences in hybrid and non-hybrid PA6-based composite components, reinforced with carbon and glass fibers, affect deformation and failure behavior. Mechanical properties are assessed using tensile, flexural, and impact tests, in conjunction with an analysis of cost and weight efficiency. The results unveil a distinct correlation between stacking sequences and the mechanical properties of additively manufactured composites. While the hybrid samples predominantly exhibit slight negative hybrid effects, a positive hybrid effect of 96.4 % is achieved in terms of impact toughness with an optimized stacking sequence. With the exception of impact strength, specimens reinforced solely with carbon fibers frequently demonstrate a remarkable ratio between mechanical properties, weight, and cost, whereas hybrid composites exhibit a balanced compromise across all tested mechanical properties. Building upon the obtained results, three comprehensive design approaches are introduced and applied, demonstrating the new design possibilities arising from the combination of hybrid composites and additive manufacturing. The developed design approaches and material combinations can be used for a wide range of lightweight construction applications.

Recent advancements in fiber reinforced additive manufacturing leverage the piezoresistivity of continuous carbon fibers. This effect enables the fabrication of structural components with inherent piezoresistive properties suitable for load measurement or structural monitoring. These are achieved without necessitating additional manufacturing or assembly procedures. However, there remain unexplored variables within the domain of continuous fiber-reinforced additive manufacturing. Crucially, the roles of fiber curvature radii and sensing fiber bundle counts have yet to be comprehensively addressed. Additionally, the compression-sensitive nature of printed carbon fiber-reinforced specimens remains a largely unexplored research area. To address these gaps, this study presents experimental analyses on tensile and three-point flexural specimens incorporating sensing carbon fiber strands. All specimens were fabricated with three distinct curvature radii. For the tensile specimens, the number of layers was also varied. Sensing fiber bundles were embedded on both tensile and compression sides of the flexural specimens. Mechanical testing revealed a linear-elastic behavior in the specimens. It was observed that carbon fibers supported the majority of the load, leading to brittle fractures. The resistance measurements showed a dependence on both the number of sensing layers and the radius of curvature, and exhibited a slight decreasing trend in the cyclic tests. Compared with the sensors subjected to tensile stress, the sensors embedded on the compression side showed a lower gauge factor.

The complexity offered by additive material extrusion (MEX) presents new opportunities for novel design, especially with regard to multi-material components. However, this design freedom has heretofore only been scarcely used thus stifling innovation. One reason for this development is the complicated nature of adhesion at the interface of multi-material parts. Hence, this publication aims to investigate the process-dependent influences in multi-material MEX by conducting tensile tests on ABS-PLA multi-material specimens. By implementing a distance gauge into the tool change procedure, positional fluctuations of the nozzle will be eliminated and the effects of extrusion temperature, line placement, and over- or under-extrusion on composite strength can be determined more precisely. In addition, thermal imaging is conducted to give an informed estimate of the effects of build chamber or build plate temperature on diffusion at the material interface. The results show a clear influence of extrusion temperature and over-extrusion on composite strength, while the effect of line placement is determined to only be minor. The build chamber temperature is predicted to have no meaningful effect on composite strength. Overall, the results suggest that deviations in printer calibration, by as little as 0.04 mm, can have a significant influence on composite strength.

Continuous fiber-reinforced material extrusion is an emerging additive manufacturing process that builds components layer by layer by extruding a continuous fiber-reinforced thermoplastic strand. This novel manufacturing process combines the benefits of additive manufacturing with the mechanical properties and lightweight potential of composite materials, making it a promising approach for creating high-strength end products. The field of design for additive manufacturing has developed to provide suitable methods and tools for such emerging processes. However, continuous fiber-reinforced material extrusion, as a relatively new technology, has not been extensively explored in this context. Designing components for this process requires considering both restrictive and opportunistic aspects, such as extreme anisotropy and opportunities for functional integration. Existing process models and methods do not adequately address these specific needs. To bridge this gap, a tailored methodology for designing continuous fiber-reinforced material extrusion is proposed, building on established process models. This includes developing process-specific methods and integrating them into the process model, such as a process selection analysis to assess the suitability of the method and a decision model for selecting the process for highly stressed components. Additionally, a detailed design process tailored to continuous fiber-reinforced material extrusion is presented. The application of the developed process model is demonstrated through a case study.

Battery cells experience volume changes due to intercalation and ageing processes, which may pose a challenge when integrating cells into a battery module. This study presents an innovative, numerical model, which spatially resolved predicts the time-dependent, overall mechanical behaviour of battery modules caused by volume changes in built-in cells. An already self-developed battery module model, which statically describes the three-dimensional (3D), mechanical behaviour in a 0D simulation environment, is extended by the time dimension for dynamic modelling. The existing model abilities and features are maintained, such as the inclusion of multiple size scales from the cell to module level as well as the automatized model building process for the investigation of different module designs in regard to the number and arrangement of foam pads and multiple other design parameters. The validation of the predication abilities against those of complex, commercial software solutions, which use Finite Element Analysis (FEA) in a 3D model environment, have shown good agreement regarding sensitivity, robustness and numerical stability, revealing the impact and interdependencies of model parameters as well as the numerical limits of the model. In this study, the potential of the novel model regarding computational time and resources is underlined, making it a useful and effective tool for fast optimization studies.

The additive manufacturing of components using material extrusion (MEX) enables the integration of several materials into one component, including functional structures such as electrically conductive structures. This study investigated the influence of the selected additive MEX process on the resistivity of MEX structures. Specimens were produced from filaments and granules of an electrically conductive PLA and filled with carbon nanotubes and carbon black. Specimens were produced with a full-factorial variation of the input variables: extrusion temperature, deposition speed, and production process. The resistivity of the specimens was determined by four-wire measurement. Analysis of the obtained data showed that only the extrusion temperature had a significant influence on the resistivity of the MEX specimens. Furthermore, the impact of the nozzle diameter was evaluated by comparing the results of this study with those of a previous study, with an otherwise equal experimental setup. The nozzle diameter had a significant influence on resistivity and a larger nozzle diameter reduced the mean variance by an order of magnitude. The resistivity was lower for most process parameter sets. As the manufacturing process had no significant influence on the resistivity of MEX structures, it can be selected based on other criteria, e.g., the cost of feedstock.

Additive manufacturing of components using the material extrusion (MEX) of thermoplastics enables the integration of multiple materials into a single part. This can include functional structures, such as electrically conductive ones. The resulting functional structure properties depend on the process parameters along the entire manufacturing chain. The aim of this investigation is to determine the influence of process parameters in filament production and additive manufacturing on resistivity. Filament is produced from a commercially available composite of polylactide (PLA) with carbon nanotubes (CNT) and carbon black (CB), while the temperature profile and screw speed were varied. MEX specimens were produced using a full-factorial variation in extrusion temperature, layer height and deposition speed from the most and least conductive in-house-produced filament and the commercially available filament from the same composite. The results show that the temperature profile during filament production influences the resistivity. The commercially available filament has a lower conductivity than the in-house-produced filament, even though the starting feedstock is the same. The process parameters during filament production are the main factors influencing the resistivity of an additively manufactured structure. The MEX process parameters have a minimal influence on the resistivity of the used PLA/CNT/CB composite.

Nowadays considering trends such as digitalization, automated driving as well as electric mobility in products in automotive development processes is a major challenge, which has led to an enormous increase in the number of product functions of technical systems. However, the recognized processes in automotive development are strongly component-oriented and such processes partially support the development of product functions. In order to meet future trends and ensure long term customer satisfaction, a transfer from component-oriented to function-oriented development is necessary. Accordingly, a holistic concept can be useful that enables the integration of customer feedback into the early phase of product development in the context of function-orientation. However, the customer feedback evaluation and their mapping with technical subsystems have been considered mainly in the context of component-oriented development. In this contribution, a method is proposed, which is generated in the context of a product model of product generation engineering. Product Generation Engineering enables the structuring of the development process of a product generation and supports function-oriented development. The Product Model provides customer- oriented development of mechatronic products. The proposed method is achieved in the sense of model-based systems engineering and validated by the exemplarily application of a case study of a specific vehicle. Both the past and current product generations of the specific vehicle are taken into account in the development of the subsequent product generation.

The current work experimentally determined how the initial resistance and gauge factor in additively manufactured piezoresistive sensors are affected by the material, design, and process parameters. This was achieved through the tensile testing of sensors manufactured with different infill angles, layer heights, and sensor thicknesses using two conductive polymer composites. Linear regression models were then used to analyze which of the input parameters had significant effects on the sensor properties and which interaction effects existed. The findings demonstrated that the initial resistance in both materials was strongly dependent on the sensor geometry, decreasing as the cross-sectional area was increased. The resistance was also significantly influenced by the layer height and the infill angle, with the best variants achieving a resistance that was, on average, 22.3% to 66.5% lower than less-favorable combinations, depending on the material. The gauge factor was most significantly affected by the infill angle and, depending on the material, by the layer height. Of particular interest was the finding that increasing in the infill angle resulted in an increase in the sensitivity that outweighed the associated increase in the initial resistance, thereby improving the gauge factor by 30.7% to 114.6%, depending on the material.

ABSTRACT: Disruptive innovations (DI) have the potential to fundamentally change markets and their power relations: Specifically, established companies are confronted with the threat of being forced out of the market by DI. At the same time, companies also have the opportunity to control the market’s development by developing DI themselves. This raises the question of how a proactive management of DI can be systematized. Here, the approach of innovation portfolio management (IPM) provides support by identifying, evaluating, and selecting a company’s most promising product ideas. The management of DI is challenging due to their characteristics, such as diversity, high uncertainty, especially with regard to customer acceptance, difficult comparability with existing products, potential for cannibalization, and substitution of existing products. For this reason, new approaches to managing DI are required in literature and practice. This paper presents a novel methodology to support—especially established—companies in proactively generating DI via existing processes in their IPM. For this purpose, the methodology supports the early identification of the product idea’s disruptive potential so that these can be handled appropriately in the further course of the process, such as the evaluation of all product ideas in the company during IPM. Thus, the risk can be minimized that promising DI are not rejected due to unsuitable procedures, but are brought to market maturity. The methodology contributes to the literature by showing how DI can be pursued embedded in existing corporate structures and also in competition with other product ideas in the company—in existing approaches, DI is primarily considered singularly and detached from the existing corporate context. The methodology is validated by the example of the digital camera, which disrupted the photo industry, as well as through interviews in a practical context.

Recent research efforts have highlighted the potential of hybrid composites in the context of additive manufacturing. The use of hybrid composites can lead to an enhanced adaptability of the mechanical properties to the specific loading case. Furthermore, the hybridization of multiple fiber materials can result in positive hybrid effects such as increased stiffness or strength. In contrast to the literature, where only the interply and intrayarn approach has been experimentally validated, this study presents a new intraply approach, which is experimentally and numerically investigated. Three different types of tensile specimens were tested. The non-hybrid tensile specimens were reinforced with contour-based fiber strands of carbon and glass. In addition, hybrid tensile specimens were manufactured using an intraply approach with alternating carbon and glass fiber strands in a layer plane. In addition to experimental testing, a finite element model was developed to better understand the failure modes of the hybrid and non-hybrid specimens. The failure was estimated using the Hashin and Tsai–Wu failure criteria. The specimens showed similar strengths but greatly different stiffnesses based on the experimental results. The hybrid specimens demonstrated a significant positive hybrid effect in terms of stiffness. Using FEA, the failure load and fracture locations of the specimens were determined with good accuracy. Microstructural investigations of the fracture surfaces showed notable evidence of delamination between the different fiber strands of the hybrid specimens. In addition to delamination, strong debonding was particularly evident in all specimen types.

Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations. The required parameter calibration of the material models is becoming increasingly challenging with regard to the growing variety of available materials. Besides the classical iterative optimization-based parameter identification method, novel machine learning-based methods represent promising alternatives, especially in terms of efficiency. However, the machine learning algorithms, architectures, and settings significantly affect the resulting accuracy. This work presents a comparative study of different machine learning algorithms based on virtual datasets with varying settings for the direct inverse material parameter identification method. Multilayer perceptrons, convolutional neural networks, and Bayesian neural networks are compared; and their resulting prediction accuracies are investigated. Furthermore, advantages in material parameter identification by uncertainty quantification using the Bayesian probabilistic approach are examined and discussed. The results show increased prediction quality when using convolutional neural networks instead of multilayer perceptrons. The assessment of the aleatoric and epistemic uncertainties when using Bayesian neural networks also demonstrated advantages in evaluating the reliability of the predicted material parameters and their influences on the subsequent finite element simulations.

Recently, the use of prismatic cells in electric vehicles has increased significantly. Unlike the cylindrical or pouch format, the prismatic cell format has not been sufficiently investigated. In this study, quasi-static mechanical tests are performed on prismatic cells. The tests include a cylindrical and a hemispherical impactor that mechanically load the cells in all three spatial directions. In both in-plane directions, a cell stack consisting of three cells is tested to capture the influence and loading of the outer cells of a cell stack. It is found out that, in the in-plane tests, short-circuiting occurs first in the outer cells and subsequently in the middle cell, which is targeted by the impactor. This result can also be supported by computed tomography scans. The results illustrate that, when evaluating the crash safety of battery cells, several cells should always be tested in order to capture the different loading of the cells.

An existing challenge in the use of continuous fiber reinforcements in additively manufactured parts is the limited availability of suitable fiber materials. This leads to a reduced adaptability of the mechanical properties to the load case. The increased design freedom of additive manufacturing allows the flexible deposition of fiber strands at defined positions, so that even different fiber materials can be easily combined in a printed part. In this work, therefore, an approach is taken to combine carbon and aramid fibers in printed composite parts to investigate their effects on mechanical properties. For this purpose, tensile, flexural and impact tests were performed on printed composite parts made of carbon and aramid fibers in a nylon matrix with five different mixing ratios. The tests showed that the use of hybrid composites for additive manufacturing is a reasonable approach to adapt the mechanical properties to the loading case at hand. The experiments showed that increasing the aramid fiber content resulted in an increase in impact strength, but a decrease in tensile and flexural strength and a decrease in stiffness. Microstructural investigations of the fracture surfaces showed that debonding and delamination were the main failure mechanisms. Finally, Rule of Hybrid Mixture equations were applied to predict the mechanical properties at different mixture ratios. This resulted in predicted values that differed from the experimentally determined values by an average of 5.6%.

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.

Nowadays, product development times are constantly decreasing, while the requirements for the products themselves increased significantly in the last decade. Hence, manufacturers use Computer-Aided Design (CAD) and Finite-Element (FE) Methods to develop better products in shorter times. Shape optimization offers great potential to improve many high-fidelity, numerical problems such as the crash performance of cars. Still, the proper selection of optimization algorithms provides a great potential to increase the speed of the optimization time. This article reviews the optimization performance of two different algorithms and frameworks for the structural behavior of a b-pillar. A b-pillar is the structural component between a car’s front and rear door, loaded under static and crash requirements. Furthermore, the validation of the algorithm includes a feasibility constraint. Recently, an optimization routine was implemented and validated for a Non-dominated Sorting Genetic Algorithm (NSGA-II) implementation. Different multi-objective optimization algorithms are reviewed and methodically ranked in a comparative study by given criteria. In this case, the Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS) framework is chosen and implemented into the existing Institut für Konstruktionstechnik Optimizes Shapes (IKOS) framework. Specifically, the article compares the NSGA-II and GOMORS directly for a linear, non-linear, and feasibility optimization scenario. The results show that the GOMORS outperforms the NSGA-II vastly regarding the number of function calls and Pareto-efficient results without the feasibility constraint. The problem is reformulated to an unconstrained, three-objective optimization problem to analyze the influence of the constraint. The constrained and unconstrained approaches show equal performance for the given scenarios. Accordingly, the authors provide a clear recommendation towards the surrogate-based GOMORS for costly and multi-objective evaluations. Furthermore, the algorithm can handle the feasibility constraint properly when formulated as an objective function and as a constraint.

This study is about a method for evaluating specific product complexity. In this context, an efficient and scalable method for the development of a specific complexity assessment of highly complex products is presented. Furthermore, existing evaluation methods are analysed according to effort and benefit, thus showing the research gap and the need for the method to be developed. The procedure for the development of an indicator for the specific evaluation of product complexity is presented in five steps and an exemplary complexity indicator for lithium ion battery cells is developed. This index is then applied, and the complexity of commercial battery cells from the application is evaluated. Based on these evaluations, final potentials of the method are shown and a recommendation for a reduction in product complexity is provided. The developed method for complexity assessment is scalable in its effort and offers implementation into existing complexity management. The method allows quick adaptation or extension and, thus, well-founded decision making. By standardizing the evaluation and taking objectively measurable complexity characteristic values as a basis, a holistic and objective evaluation tool is shown, which can thus become a decisive success factor for manufacturers of complex products.

Today, vehicle development is already in a process of substantial transformation. Mobility trends can be derived from global megatrends and have a significant influence on the requirements of the developed vehicles. The sociological, technological, economic, ecological, and political developments can be determined by using the scenario technique. The results are recorded in the form of differently shaped scenarios; however, they are mainly document-based. In order to ensure a holistic approach in the sense of model-based systems engineering and to be able to trace the interrelationships of the fast-changing trends and requirements, it is necessary to implement future scenarios in the system model. For this purpose, a method is proposed that enables the consideration of future scenarios in model-based vehicle development. The procedure of the method is presented, and the location of the future scenarios within the system architectures is named. The method is applied and the resulting system views are derived based on the application example of an autonomous people mover. With the help of the described method, it is possible to show the effects of a change of scenario (e.g., best-case and worst-case) and the connections with the highest level of requirements: stakeholder needs.

The mechanical integrity of a lithium ion battery cell can be evaluated using finite element (FE) simulation techniques. In this study, different FE modelling approaches including heterogeneous, homogeneous, hybrid and sandwich methods are presented and analysed. The basic capabilities of the FE-methods and their suitability to simulate a real mechanical safety test procedures on battery cells are investigated by performing a simulation of a spherical indentation test on a sample pouch cell. For each modelling approach, one battery cell model was created. In order to observe the system behaviour, relevant parametric studies involving coefficient of friction and failure strain of separator were performed. This studied showed that these parameters can influence the maximum force and the point of failure of the cell. Furthermore, the influence of an anisotropic separator on the results was also investigated. The advantages and disadvantages of each modelling approach are discussed and a simplified approach with a partial cell modelling is suggested to further reduce the simulation time and complexity.

Additive manufacturing (AM), widely known as 3D-printing, builds parts by adding material in a layer-by-layer process. This tool-less procedure enables the manufacturing of porous sound absorbers with defined geometric features, however, the connection of the acoustic behavior and the material’s micro-scale structure is only known for special cases. To bridge this gap, the work presented here employs machine-learning techniques that compute acoustic material parameters (Biot parameters) from the material’s micro-scale geometry. For this purpose, a set of test specimens is used that have been developed in earlier studies. The test specimens resemble generic absorbers by a regular lattice structure based on a bar design and allow a variety of parameter variations, such as bar width, or bar height. A set of 50 test specimens is manufactured by material extrusion (MEX) with a nozzle diameter of 0.2 mm and a targeted under extrusion to represent finer structures. For the training of the machine learning models, the Biot parameters are inversely identified from the manufactured specimen. Therefore, laboratory measurements of the flow resistivity and absorption coefficient are used. The resulting data is used for training two different machine learning models, an artificial neural network and a k-nearest neighbor approach. It can be shown that both models are able to predict the Biot parameters from the specimen’s micro-scale with reasonable accuracy. Moreover, the detour via the Biot parameters allows the application of the process for application cases that lie beyond the scope of the initial database, for example, the material behavior for other sound fields or frequency ranges can be predicted. This makes the process particularly useful for material design and takes a step forward in the direction of tailoring materials specific to their application.

The design and development processes are full of decisions. Ranging from simple and straightforward to complex and elaborated. These decisions are taken by individuals that constantly rely on their intuition and heuristics to support their decision-making processes. Although heuristics tend to be very helpful, in many cases, they can lead to cognitive biases. This article postulates a method to recognize some of these biases and to apply dedicated strategies to diminish their effects. To do so, the study reviews different decision models in engineering design and consolidates them into one; here, called ABC decision model—ABC stands for Allaying Biased Choices. This model consists of four phases describing four different decision types. Subsequently, four matching strategy sets are prescribed to target some of the most prone biases on those phases. Then, to demonstrate the application opportunities of this method, the ABC decision model is applied to the process of Strategic Release Planning (SRP). Finally, to show the theory in real-world conditions, the results of a pilot industrial application are presented. This article offers promising opportunities for allaying biased choices in design and development processes.

Additive manufacturing, especially material extrusion (MEX), has received a lot of attention recently. The reasons for this are the numerous advantages compared to conventional manufacturing processes, which result in various new possibilities for product development and -design. By applying material layer by layer, parts with complex, load-path optimized geometries can be manufactured at neutral costs. To expand the application fields of MEX, high-strength and simultaneously lightweight materials are required which fulfill the requirements of highly resilient technical parts. For instance, the embedding of continuous carbon and flax fibers in a polymer matrix offers great potential for this. To achieve the highest possible variability with regard to the material combinations while ensuring simple and economical production, the fiber–matrix bonding should be carried out in one process step together with the actual parts manufacture. This paper deals with the adaptation and improvement of the 3D printer on the one hand and the characterization of 3D printed test specimens based on carbon and flax fibers on the other hand. For this purpose, the print head development for in-situ processing of contin uous fiber-reinforced parts with improved mechanical properties is described. It was determined that compared to neat polylactic acid (PLA), the continuous fiber-reinforced test specimens achieve up to 430% higher tensile strength and 890% higher tensile modulus for the carbon fiber reinforcement and an increase of up to 325% in tensile strength and 570% in tensile modulus for the flax fibers. Similar improvements in performance were achieved in the bending tests.

Innovative vehicle concepts have been developed in the past years in the automotive sector, including alternative drive systems such as hybrid and battery electric vehicles, so as to meet the environmental targets and cope with the increasingly stringent emissions regulations. The preferred hybridizing technology is lithium-ion battery, thanks to its high energy density. The optimal integration of battery packs in the vehicle is a challenging task when designing e-mobility concepts. Therefore, this work proposes a conceptual design procedure aimed at optimizing the sizing of hybrid and battery electric vehicles. Particularly, the influence of the cell type, physical disposition and arrangement of the electrical devices is accounted for within a conversion design framework. The optimization is focused on the trade-off between the battery pack capacity and weight. After introducing the main features of electric traction systems and their challenges compared to conventional ones, the relevant design properties of electric vehicles are analyzed. A detailed strategy, encompassing the selection of battery format and technology, battery pack design and final assessment of the proposed set-up, is presented and implemented in an exemplary application, assuming an existing commercial vehicle as the reference starting layout. Prismatic, cylindrical and pouch cells are configured aiming at achieving installed battery energy as close as possible to the reference one, while meeting the original installation space constraint. The best resulting configuration, which also guarantees similar peak power performance of the reference battery-pack, allows reducing the mass of the storage system down to 70% of its starting value.

To be able to use finite element (FE) simulations in structural component development, experimental investigations for the characterization of the material properties are required to subsequently calibrate suitable material cards. In contrast to the commonly used computational and time-consuming method of parameter identification (PI) by using analytical and numerical optimizations with internal or commercial software, a more time-efficient method based on machine learning (ML) is presented. This method is applied to simulate the material behavior of additively manufactured specimens made of acrylonitrile butadiene styrene (ABS) under uniaxial stress in a structural simulation. By using feedforward artificial neural networks (FFANN) for the ML-based direct inverse PI process, various investigations were carried out on the influence of sampling strategies, data quantity and data preparation on the prediction accuracy of the NN. Furthermore, the results of hyperparameter (HP) search methods are presented and discussed and their influence on the prediction quality of the FFANN are critically evaluated. The investigations show that the NN-based method is applicable to the present use case and results in material parameters that lead to a lower error between experimental and calculated force-displacement curves than the commonly used optimization-based method.

Direct additive manufacturing (AM) of sensors has in recent years become possible, but still remains a largely unexplored area. This work proposes a novel resistive sensor design that utilizes the geometric freedom offered by AM, especially by material extrusion, to enable a customizable and amplified response to force and deformation. This is achieved by using a multi-material design made of an elastomer and an electrically conductive polymer that enables a physical shortening of the conductive path under compressive load through a specific definition of shape. A number of different variants of this novel sensor design are tested, measuring their mechanical and electrical behavior under compression. The results of these tests confirm a strong resistive response to mechanical loading. Furthermore, the results provide insight into the influencing factors of the design, i.e., the gap size between the conductive pathing and the stiffness of the sense element support structure are found to be primary influencing factors governing sensor behavior.

One possibility in order to manufacture products with very few restrictions in design freedom is additive manufacturing. For advanced acoustic design measures like Acoustic Black Holes (ABH), the layer-wise material deposition allows the continuous alignment of the mechanical impedance by different filling patterns and degrees of filling. In order to explore the full design potential, mechanical models are indispensable. In dependency on process parameters, the resulting homogenized material parameters vary. In previous investigations, especially for ABH structures, a dependency of the material parameters on the structure’s thickness can be observed. In this contribution, beams of different thicknesses are investigated experimentally and numerically in order to identify the material parameters in dependency on the frequency and the thickness. The focused material is polyactic acid (PLA). A parameter fitting is conducted by use of a 3D finite element model and it’s reduced version in a Krylov subspace. The results yield homogenized material parameters for the PLA stack as a function of frequency and thickness. An increasing Young’s modulus with increasing frequency and increasing thickness is observed. This observed effect has considerable influence and has not been considered so far. With the received parameters, more reliable results can be obtained.

Material composition complexity offered by material extrusion additive manufacturing offers new opportunities for function-driven part design. Nevertheless, since influencing factors on the interface strength between different materials are not well understood, this complexity is only used infrequently, in part, in design thereby restraining innovation. This paper proposes a systematical approach for identification and quantification of relevant adhesion phenomena that influence interface strength. For this reason, suited test specimen, which utilize the geometric freedom offered by additive manufacturing, are developed for roll peeling tests and peeling resistance of several combinations of rigid and flexible materials is determined. The results show that material choice especially regarding polarity as well as mechanical interlocking in regards to surface roughness and design features have high influence on the interface strength of multi-material parts manufactured by material extrusion. These results are explained through the relevant adhesion mechanisms that determine the interface strength in additively manufactured parts. Finally, criteria that predominantly affect interface strength are deduced and design recommendations for creating functional parts with ill-fitting material combinations are formulated.

Multi-material additive manufacturing offers new design freedom for functional integration and opens new possibilities in innovative part design, for instance, a local integration of electrically conductive structures or heat radiant surfaces. Detailed experimental investigations on materials with three different fillers (carbon black (CB), carbon nanotubes (CNT) and nano copper wires) were conducted to identify process-specific influencing factors on electrical conductivity and resistive heating. In this regard, raster angle orientation, extrusion temperature, speed and flow rate were investigated. A variation of the raster angle (0°, ±45°, and 90°) shows the highest influence on resistivity. An angle of 0° had the lowest electrical resistance and the highest temperature increase due to resistive heating. The material filled with nano copper wires showed the highest electrical conductivity followed by the CNT filled material and materials filled with CB. Both current⁻voltage characteristics and voltage-dependent heat distribution of the surface temperature were determined by using a thermographic camera. The highest temperature increase was achieved by the CNT filled material. The materials filled with CB and nano copper wires showed increased electrical resistance depending on temperature. Based on the experiments, solution principles and design rules for additively manufactured electrically conductive structures are derived.

Three-dimensional printing used to be a rapid prototyping process, but nowadays it is establishing as an additive manufacturing (AM) process. One of these AM techniques is selective laser sintering (SLS), which most often involves partial melting of the particles and therefore belongs to the category of powder bed fusion processes. Much progress has been made in this field by research on process parameters like laser power, hatch distance, and scanning speed while still lacking a fundamental understanding of the powder deposition and its influence on parts. A critical issue for economic manufacturing is the building time of parts with good mechanical properties, which can be reduced by lower surface roughness due to less or missing post processing. Therefore, the influence of three blade shapes on powder bed surface roughness has been evaluated for PA12 powder with three different grain size distributions by using advanced X-ray micro computed tomography (XMT) and a confocal laser scanning microscope (LSM). Along with those methods, new techniques for powder characterization were tested and compared. Lowest roughness has been achieved with a flat blade, based on a higher compression due to a larger contact zone between blade and powder bed. Furthermore, an anisotropic effect of the mechanical properties resulting from different building directions has been detected which can be explained by varying amounts of solid contact paths through the powder bed depending on powder application direction. In addition, an optimal combination of process parameters with an even compression of the powder bed leads to low surface roughness, complementing the advantages of additive manufacturing.

Multi-material additive manufacturing (AM) offers new design opportunities for functional integration and opens new possibilities in innovative part design, for example, regarding the integration of damping or conductive structures. However, there are no standardized test methods, and thus test specimens that provide information about the bonding quality of two materials printed together. As a result, a consideration of these new design potentials in conceptual design is hardly possible. As material extrusion (ME) allows easily combination of multiple polymeric materials in one part, it is chosen as an AM technique for this contribution. Based on a literature review of commonly used standards for polymer testing, novel test specimens are developed for the characterization of the bonding quality of two ME standard materials printed together. The proposed specimen geometries are manufactured without a variation of process parameters. The load types investigated in the course of this study were selected as examples and are tensile, lap-shear, and compression-shear. The conducted tests show that the proposed test specimens enable a quantification of the bonding quality in the material transition. Moreover, by analyzing the fracture pattern of the interface zone, influencing factors that probably affect the interface strength are identified, which can be further used for its optimization.

The development of a passenger car is a multidisciplinary task. The vehicle has to fulfill demands out of different attributes like vehicle dynamics, driveability, acoustics, thermal and heat management, safety, durability, crash and economics. One main problem is the variability of mechanical quantities responsible for the performance and customer perception of a car. To overcome this, the extension of the conventional deterministic oriented development process to a process which includes stochastic quantities is necessary. In this paper the current deterministic approach is described briefly. It is not possible to perform the extension in a single step. In a first extended version of the process the variability of material parameters is included. In further steps the formulation and solution of stochastic optimization problems for sub-problems is necessary. Finally the complete approach should fully integrate the variability of stochastic quantities. [ABSTRACT FROM AUTHOR]

The continuously decreasing life cycle of modern products leads to new challenges for product development. Additive manufacturing (AM) processes are able to support faster development by rapid production of samples and prototypes. However, the material properties of components produced by common (plastic-) 3D-printers are often insufficient for functional prototyping. A well-established way to improve the properties of plastics is the embedding of reinforcing fibers. Thus, this paper shows a method for fiber-reinforced 3D-printing. Through this combination, several restrictions of conventional composite production can be eased and additional freedoms of design are gained. To support the design of such parts, an adapted design methodology for fiber-reinforced 3D-printing is developed. [ABSTRACT FROM AUTHOR]