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Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other’s weaknesses.

In the context of tolerance management, the consideration of manufacturing and assembly processes is becoming increasingly important. The main drivers for this are, above all, short development times and high-quality requirements, leading to tight tolerances. To overcome the resulting challenges, many publications address the process-oriented tolerance management. However, since multiple terms and definitions for describing activities that link tolerance management with the production process exist, it is hard to obtain a comprehensive overview on the topic. Therefore, this paper presents a review of existing approaches. The aim is to identify similarities and differences of existing approaches and present them with the help of a classification. For this purpose, among others, work from the areas of process-oriented tolerance management, stream of variation, state space modeling, and variation propagation in multistation manufacturing and assembly systems is considered. Based on the definition of the summarizing term “process-oriented tolerance and variation management”, a classification of this thematic area will be introduced.

The human's upright standing is a complex control process that is not yet fully understood. Postural control models can provide insights into the body's internal control processes of balance behavior. Using physiologically plausible models can also help explaining pathophysiological motion behavior. In this paper, we introduce a neuromusculoskeletal postural control model using sensor feedback consisting of somatosensory, vestibular and visual information. The sagittal plane model was restricted to effectively six degrees of freedom and consisted of nine muscles per leg. Physiologically plausible neural delays were considered for balance control. We applied forward dynamic simulations and a single shooting approach to generate healthy reactive balance behavior during quiet and perturbed upright standing. Control parameters were optimized to minimize muscle effort. We showed that our model is capable of fulfilling the applied tasks successfully. We observed joint angles and ranges of motion in physiologically plausible ranges and comparable to experimental data. This model represents the starting point for subsequent simulations of pathophysiological postural control behavior.

Service loads repeatedly stress components on a regular basis and lead to fatigue damage in the material. In the case of components made of short fibre-reinforced thermoplastics, which are also crash-relevant in addition to only bearing service loads, however, a significant deterioration in mechanical properties can be observed after fatigue damage has been introduced. This is where the approach presented in this paper comes in: in order to enable a realistic simulation of such components in their used conditions, the material data are assigned depending on previously determined damage. The approach, which combines the domains of highly dynamic and cyclic experiments as well as different types of numerical simulations, is tested for its performance in the present paper. For this purpose, component tests are carried out on cross-rib beams, which serve to validate the method. The novelty and uniqueness of this paper lies in the linking of fatigue life and crashworthiness considerations for short fibre-reinforced thermoplastics, which, in this case, is raised to a new level by considering the component level for the first time.

Bearing life calculation is a well-researched and standardized topic for rotating operation conditions. However, there is still no validated and standardized calculation for oscillating operation, only different calculation approaches. Due to the increasing number of oscillating rolling bearings, for example, in wind turbines, industrial robots, or 3D printers, it is becoming more and more important to validate one of these approaches or to formulate a new one. In order to achieve this goal, the damage mechanisms for oscillating operating conditions must first be analyzed in more detail by means of experimental investigations. The open question is whether fatigue is the relevant damage mechanism or whether wear damage, such as fretting corrosion or false brinelling, dominates. The present work therefore shows under which oscillation angle and frequency fatigue occur in oil-lubricated cylindrical roller bearings.

Digital transformation is omnipresent in our daily lives and its impact is noticeable through new technologies, like smart devices, AI-Chatbots or the changing work environment. This digitalization also takes place in product development, with the integration of many technologies, such as Industry 4.0, digital twins or data-driven methods, to improve the quality of new products and to save time and costs during the development process. Therefore, the use of data-driven methods reusing existing data has great potential. However, data from product design are very diverse and strongly depend on the respective development phase. One of the first few product representations are sketches and drawings, which represent the product in a simplified and condensed way. But, to reuse the data, the existing sketches must be found with an automated approach, allowing the contained information to be utilized. One approach to solve this problem is presented in this paper, with the detection of principle sketches in the early phase of the development process. The aim is to recognize the symbols in these sketches automatically with object detection models. Therefore, existing approaches were analyzed and a new procedure developed, which uses synthetic training data generation. In the next step, a total of six different data generation types were analyzed and tested using six different one- and two-stage detection models. The entire procedure was then evaluated on two unknown test datasets, one focusing on different gearbox variants and a second dataset derived from CAD assemblies. In the last sections the findings are discussed and a procedure with high detection accuracy is determined.

Abstract Understanding of the human body’s internal processes to maintain balance is fundamental to simulate postural control behaviour. The body uses multiple sensory systems’ information to obtain a reliable estimate about the current body state. This information is used to control the reactive behaviour to maintain balance. To predict a certain motion behaviour with knowledge of the muscle forces, forward dynamic simulations of biomechanical human models can be utilized. We aim to use predictive postural control simulations to give therapy recommendations to patients suffering from postural disorders in the future. It is important to know which types of modelling approaches already exist to apply such predictive forward dynamic simulations. Current literature provides different models that aim to simulate human postural control. We conducted a systematic literature research to identify the different approaches of postural control models. The different approaches are discussed regarding their applied biomechanical models, sensory representation, sensory integration, and control methods in standing and gait simulations. We searched on Scopus, Web of Science and PubMed using a search string, scanned 1253 records, and found 102 studies to be eligible for inclusion. The included studies use different ways for sensory representation and integration, although underlying neural processes still remain unclear. We found that for postural control optimal control methods like linear quadratic regulators and model predictive control methods are used less, when models’ level of details is increasing, and nonlinearities become more important. Considering musculoskeletal models, reflex-based and PD controllers are mainly applied and show promising results, as they aim to create human-like motion behaviour considering physiological processes.

The use of system models within model-based systems engineering (MBSE) is essential for improved communication or system documentation. Previous publications have investigated further reuse of these system models, for example, transforming them directly into discipline-specific models for reuse. The authors refer to this as the term “Utilization” of system models. It aims the compensation of modelling efforts and a further integration of linked models within MBSE. Motivated by a lack of common understanding of this term, a systematic literature review of the state of the art is presented. With this systematic overview, a definition and classification system for different use cases and system life cycle stages are created. These are key results to support engineers and researchers in adopting existing or discovering new utilization approaches. This supports the mission of advanced systems engineering and aims the identification of new research directions coming along with SysML v2 and the advanced systems engineering methods.

The calculation of moments of area is one of the most fundamental aspects of engineering mechanics for calculating the properties of beams or for the determination of invariants in different kind of geometries. While a variety of shapes, such as circles, rectangles, ellipses, or their combinations, can be described symbolically, such symbolic expressions are missing for freeform cross-sections. In particular, periodic B-spline cross-sections are suitable for an alternative beam cross-section, e.g., for the representation of topology optimization results. In this work, therefore, a symbolic description of the moments of area of various parametric representations of such B-splines is computed. The expressions found are then compared with alternative computational methods and checked for validity. The resulting equations show a simple method that can be used for the fast conceptual computation of such moments of area of periodic B-splines.

The load-adaptive behavior of the muscles in the human musculoskeletal system offers great potential for minimizing resource and energy requirements in many technical systems, especially in drive technology and robotics. However, the lack of knowledge about suitable technical linear actuators that can reproduce the load-adaptive behavior of biological muscles in technology is a major reason for the lack of successful implementation of this biological principle. In this paper, therefore, the different types of linear actuators are investigated. The focus is particularly on artificial muscles and rope pulls. The study is based on literature, on the one hand, and on two physical demonstrators in the form of articulated robots, on the other hand. The studies show that ropes are currently the best way to imitate the load-adaptive behavior of the biological model in technology. This is especially illustrated in the context of this paper by the discussion of different advantages and disadvantages of the technical linear actuators, where ropes, among other things, have a good mechanical and control behavior, which is very advantageous for use in an adaptive system. Finally, the next steps for future research are outlined to conclude how ropes can be used as linear actuators to transfer load-adaptive lightweight design into technical applications.

Working at and above head height with a power tool represents a common activity in craft and assembly applications. To assist and protect the user from overload and injuries in these situations, the development and use of application-specific support systems, such as exoskeletons and power tools, have greatly increased in recent years. Thus, the integration of aspects of the user-centered product development of support systems in the early phases of product development process has high potentials. A common approach to integrate the user early in the product development process is the use of musculoskeletal human models, which allow the evaluation of effects on the human body. This could also be applicable in the mentioned use case to enable the evaluation of the interactions for the user. Therefore, a co-simulation model for virtual modelling and simulating human–machine interactions is presented. The co-simulation model is made up of a musculoskeletal human model and the models of the technical systems (exoskeleton and power tool). By applying the co-simulation model, the impact of technical systems on the human body can be taken into account to derive design alternatives for the technical system due to the requirements of the user. The paper describes the design of the co-simulation model and particularly, the interaction of the submodels. The evaluation of the co-simulation model is carried out with the help of a subject study for the selected use case working at and above head height. The results show plausible results for the muscle loads considering the support by an exoskeleton. Furthermore, the comparison of simulated results to measured muscle activations via surface electromyography shows a good agreement. Thus, the co-simulation model passes the test for functionality and seems to be applicable for the derivation of design alternatives of technical systems regarding the user needs. In future, the co-simulation model will be further validated with a higher number of subjects and to implement design alterations in the technical systems.

Given strict emission targets and legal requirements, especially in the automotive industry, environmentally friendly and simultaneously versatile applicable production technologies are gaining importance. In this regard, the use of mechanical joining processes, such as clinching, enable assembly sheet metals to achieve strength properties similar to those of established thermal joining technologies. However, to guarantee a high reliability of the generated joint connection, the selection of a best-fitting joining technology as well as the meaningful description of individual joint properties is essential. In the context of clinching, few contributions have to date investigated the metamodel-based estimation and optimization of joint characteristics, such as neck or interlock thickness, by applying machine learning and genetic algorithms. Therefore, several regression models have been trained on varying databases and amounts of input parameters. However, if product engineers can only provide limited data for a new joining task, such as incomplete information on applied joining tool dimensions, previously trained metamodels often reach their limits. This often results in a significant loss of prediction quality and leads to increasing uncertainties and inaccuracies within the metamodel-based design of a clinch joint connection. Motivated by this, the presented contribution investigates different machine learning algorithms regarding their ability to achieve a satisfying estimation accuracy on limited input data applying a statistically based feature selection method. Through this, it is possible to identify which regression models are suitable to predict clinch joint characteristics considering only a minimum set of required input features. Thus, in addition to the opportunity to decrease the training effort as well as the model complexity, the subsequent formulation of design equations can pave the way to a more versatile application and reuse of pretrained metamodels on varying tool configurations for a given clinch joining task.

Simulation models are a valuable tool for exoskeleton development, especially for system optimization and evaluation. It allows an assessment of the performance and effectiveness of exoskeletons even at an early stage of their development without physical realization. Due to the closed physical interaction between the exoskeleton and the user, accurate modeling of the human–exoskeleton interaction in defined scenarios is essential for exoskeleton simulations. This paper presents a novel approach to simulate exoskeleton motion in response to human motion and the interaction forces at the physical interfaces between the human and the exoskeleton. Our approach uses a multibody model of a shoulder exoskeleton in MATLAB R2021b and imports human motion via virtual markers from a digital human model to simulate human–exoskeleton interaction. To validate the human-motion-based approach, simulated exoskeleton motion and interaction forces are compared with experimental data from a previous lab study. The results demonstrate the feasibility of our approach to simulate human–exoskeleton interaction based on human motion. In addition, the approach is used to optimize the support profile of an exoskeleton, indicating its potential to assist exoskeleton development prior to physical prototyping.

Fatigue life testing is a complex and costly matter, especially in the case of fibre-reinforced thermoplastics, where other parameters in addition to force alone must be taken into account. The number of tests required therefore increases significantly, especially if the influence of different fibre orientations is to be taken into account. It is therefore important to gain the greatest possible amount of knowledge from the limited number of available tests. In order to achieve this, this study aims to utilise adaptive sampling, which is used in numerous areas of computational engineering, for the design of experiments on fatigue life testing. Artificial neural networks (ANNs) are therefore trained on data for the short-fibre-reinforced material PBT GF30, and their areas of greatest model uncertainty are queried. This was undertaken with ANNs from various numbers of hidden layers, which were analysed for their performance. The ideal case turned out to be four hidden layers, for which a squared error as small as 1 × 10−3 was recorded. Locally resolved, the ANN was used to identify the region of greatest uncertainty for samples of vertical orientation and small numbers of cycles. With information such as this, additional data can be obtained in such uncertain regions in order to improve the model prediction—almost halving the recorded error to only 0.55 × 10−3. In this way, a model of comparable value can be found with less experimental effort, or a model of better quality can be set up with the same experimental effort.

Digital Twins offer vast potential, yet many companies, particularly small and medium-sized enterprises, hesitate to implement them. This hesitation stems partly from the challenges posed by the interdisciplinary nature of creating Digital Twins. To address these challenges, this paper explores systematic approaches for the development and creation of Digital Twins, drawing on relevant methods and approaches presented in the literature. Conducting a systematic literature review, we delve into the development of Digital Twins while also considering analogous concepts, such as Cyber-Physical Systems and Product-Service Systems. The compiled literature is categorised into three main sections: holistic approaches, architecture, and models. Each category encompasses various subcategories, all of which are detailed in this paper. Through this comprehensive review, we discuss the findings and identify research gaps, shedding light on the current state of knowledge in the field of Digital Twin development. This paper aims to provide valuable insights for practitioners and researchers alike, guiding them in navigating the complexities associated with the implementation of Digital Twins.

Abstract Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated (EHL) contacts. In this contribution, we demonstrate that machine learning (ML) and artificial intelligence (AI) approaches (support vector machines, Gaussian process regressions, and artificial neural networks) can predict relevant film parameters more efficiently and with higher accuracy and flexibility compared to sophisticated EHL simulations and analytically solvable proximity equations, respectively. For this purpose, we use data from EHL simulations based upon the full-system finite element (FE) solution and a Latin hypercube sampling. We verify that the original input data are required to train ML approaches to achieve coefficients of determination above 0.99. It is revealed that the architecture of artificial neural networks (neurons per layer and number of hidden layers) and activation functions influence the prediction accuracy. The impact of the number of training data is exemplified, and recommendations for a minimum database size are given. We ultimately demonstrate that artificial neural networks can predict the locally-resolved film thickness values over the contact domain 25-times faster than FE-based EHL simulations (R 2 values above 0.999). We assume that this will boost the use of ML approaches to predict EHL parameters and traction losses in multibody system dynamics simulations.

Abstract Tetrahedral amorphous carbon (ta‐C) coatings have the potential to protect biomedical implants from wear and increase their service life. This study elucidates the biocompatibility, mechanical properties, adhesion, and wear resistance of ta‐C coatings fabricated by physical vapor deposition on cobalt‐chromium‐molybdenum (CoCr) and titanium (Ti64) alloys as well as ultrahigh molecular weight polyethylene (UHMWPE). Satisfactory cytocompatibility is verified using contact angle and surface tension measurements as well as indirect and direct cell testing. Scratch testing demonstrates excellent adhesion to the substrates and as confirmed by nanoindentation, the coatings represent an up to 13‐fold and 182‐fold increase in hardness on the hard and soft materials. In metal pin‐on‐UHMWPE disk sliding experiments under simulated body fluid lubrication, the wear rates of the disk are reduced by 48% (against CoCr) and 73% (against Ti64) while the pin wear rates are reduced by factors of 20 (CoCr) and 116 (Ti64) compared to uncoated pairings. From optical and laser scanning microscopy, Raman measurements, and particle analyses, it is shown that the underlying substrates remain well protected. Nonetheless, focused ion beam scanning electron microscopy revealed coating process‐related and thermally driven subductions as well as tribologically induced near‐surface fatigue, which can potentially constitute critical wear mechanisms.

The knowledge of the operating conditions in rolling bearings in technical applications offers many advantages, for example, to ensure a safe operation and to save resources and costs with the help of condition monitoring and predictive maintenance procedures. In many cases, it is difficult to implement sensors to measure the operating conditions of the rolling bearing, for reasons such as inaccessibility of the mounting position or non-compliance with installation space neutrality, which influences the sensor on the measuring point. Printed sensors using a digital deposition process, which can be used in very narrow design spaces, offer advantages in this respect. So far, these sensors have not been established in rolling bearings, so there is potential for technical application. This paper discusses the fundamental advantages and disadvantages as well as the challenges of the application, and it demonstrates the feasibility under isolated boundary conditions by applying a printed strain gauge sensor to the outer ring of a cylindrical roller bearing NU210 in an experimental setup to measure the strain under load. In this setup, the outer ring is deformed by 2 mm under an increasing radial load using a hydraulic press, and the strain is measured. Both a commercial reference sensor and a FE-simulation are used to validate the measurement. The results show that an implementation using printed sensors as a strain gauge works successfully. The resulting challenges, such as measuring strain gradients and printing on curved surfaces, are finally evaluated, and an outlook for further work is given.

It is undeniable that mechanical properties, such as the stiffness or residual strength of fibre-reinforced thermoplastics, are adversely affected by fatigue damage caused by cyclic loading. In order to quantify and predict this damage influence, a calculation approach was developed in the past for the subgroup of short-fibre-reinforced thermoplastics. In order to test and expand the applicability of this approach to the field of long-fibre-reinforced thermoplastics, the decrease in mechanical properties is investigated experimentally in this paper using PP LGF30, propylene reinforced with long glass fibres, as an example. The paper describes both the fatigue behaviour and the residual strength of the material after fatigue damage. A decrease in the residual strength of up to about 35% could be recorded. The paper also presents a modelling approach that predicts the orientation-dependent fatigue strength of the material, and furthermore allows for the calculation of its residual strength as a function of fatigue damage. The novelty of the contribution lies in the continuous modelling of fatigue behaviour for arbitrary oriented samples of long-fibre-reinforced thermoplastics and also in the prediction of its residual strength depending on previously induced fatigue damage.

Exoskeletons, orthoses, exosuits, assisting robots and such devices referred to as wearable assistive devices are devices designed to augment or protect the human body by applying and transmitting force. Due to the problems concerning cost- and time-consuming user tests, in addition to the possibility to test different configurations of a device, the avoidance of a prototype and many more advantages, digital human models become more and more popular for evaluating the effects of wearable assistive devices on humans. The key indicator for the efficiency of assistance is the interface between device and human, consisting mainly of the soft biological tissue. However, the soft biological tissue is mostly missing in digital human models due to their rigid body dynamics. Therefore, this systematic review aims to identify interaction modelling approaches between wearable assistive devices and digital human models and especially to study how the soft biological tissue is considered in the simulation. The review revealed four interaction modelling approaches, which differ in their accuracy to recreate the occurring interactions in reality. Furthermore, within these approaches there are some incorporating the appearing relative motion between device and human body due to the soft biological tissue in the simulation. The influence of the soft biological tissue on the force transmission due to energy absorption on the other side is not considered in any publication yet. Therefore, the development of an approach to integrate the viscoelastic behaviour of soft biological tissue in the digital human models could improve the design of the wearable assistive devices and thus increase its efficiency and efficacy.

Since musculoskeletal disorders are one of the most common work-related diseases for assemblers and machine operators, it is crucial to find new ways to alleviate the physical load on workers. Support systems such as exoskeletons or handheld power tools are promising technology to reduce the physical load on the humans. The development of such systems requires consideration of the interactions between human and technical systems. The physical relief effect of the exoskeleton can be demonstrated in experimental studies or by simulation with the digital human model (DHM). For the digital development of these support systems, an application-oriented representation of the workload is necessary. To facilitate digital development, an application-oriented workload model (ApOL model) of an overhead working task is presented. The ApOL model determines the load (forces, torques) onto the DHM during an overhead screw-in task using a cordless screwdriver, based on experimental data. The ApOL model is verified by comparing the simulated results to the calculated values from a mathematical model, using experimental data from three participants. The comparison demonstrates successful verification, with a maximum relative mean-absolute-error (rMAE) of the relevant load components at 11.4%. The presented ApOL model can be utilized to assess the impact of cordless screwdriver design on the human workload and facilitate a strain-based design approach for support systems e.g., exoskeletons.

In order to improve the tribological performance of PVD–MoS2 coatings, which are frequently used as a solid lubricant for operating in challenging environments, e.g., in a vacuum, they can be modified with nitrogen. This work evaluates the tribological behavior and a possible compaction occurring during the initial tribological load in the rolling contact for pure and nitrogen-modified PVD–MoS2 coatings in a vacuum. Short-running tests (1000 cycles) of coated steel discs paired with uncoated steel discs made from 100Cr6 (1.3505, AISI 52100) were conducted on a two-disc tribometer. The slide-to-roll ratio of 10.5% was kept constant, while the load was varied in two steps from 1.1 GPa to 1.6 GPa. Subsequently, a comparison was made between the worn and the pristine coatings by means of nanoindentation and an optical analysis of the wear track. The formation of a load-bearing solid lubrication was achieved for both MoS2-variants. The main differences affected the material transfer and wear mechanisms. The worn coatings reached a similar wear coefficient of 4 × 10−6 mm3N−1m−1 and a possible compaction of the coatings was found, indicated through an increased indentation hardness (for MoS2 1158% and MoS2:N 96% at a 1.1 GPa load). The assumed tribological mechanism changed with nitrogen modification, but scales with increasing load. The nitrogen-modified MoS2 coating showed less compaction than pure MoS2, while the frictional behavior was improved by a 17% reduction of the coefficient of friction.

This work aims to evaluate the current state of research on the use of artificial intelligence, deep learning, digitalization, and Data Mining in product development, mainly in the mechanical and mechatronic domain. These methods, collectively referred to as “digital engineering”, have the potential to disrupt the way products are developed and improve the efficiency of the product development process. However, their integration into current product development processes is not yet widespread. This work presents a novel consolidated view of the current state of research on digital engineering in product development by a literature review. This includes discussing the methods of digital engineering, introducing a product development process, and presenting results classified by their individual area of application. The work concludes with an evaluation of the literature analysis results and a discussion of future research potentials.

Biomechanical simulation allows for in silico estimations of biomechanical parameters such as muscle, joint and ligament forces. Experimental kinematic measurements are a prerequisite for musculoskeletal simulations using the inverse kinematics approach. Marker-based optical motion capture systems are frequently used to collect this motion data. As an alternative, IMU-based motion capture systems can be used. These systems allow flexible motion collection without nearly any restriction regarding the environment. However, one limitation with these systems is that there is no universal way to transfer IMU data from arbitrary full-body IMU measurement systems into musculoskeletal simulation software such as OpenSim. Thus, the objective of this study was to enable the transfer of collected motion data, stored as a BVH file, to OpenSim 4.4 to visualize and analyse the motion using musculoskeletal models. By using the concept of virtual markers, the motion saved in the BVH file is transferred to a musculoskeletal model. An experimental study with three participants was conducted to verify our method’s performance. Results show that the present method is capable of (1) transferring body dimensions saved in the BVH file to a generic musculoskeletal model and (2) correctly transferring the motion data saved in the BVH file to a musculoskeletal model in OpenSim 4.4.

Substantial efforts have been made to integrate manufacturing- and design-relevant knowledge into product development processes. A common approach is to provide the relevant knowledge to the design engineers using a knowledge-based system (KBS) that, in turn, becomes the engineering assistance system. Keeping the knowledge up to date is a critical issue, making knowledge acquisition a bottleneck of developing and maintaining KBS. This article presents a robust metamodel optimization and performance estimation architecture for developing and maintaining a KBS useful for design-for-manufacturing from the context of sheet-bulk metal forming. It is shown that the presented KBS or engineering assistance system helps achieve performing design-for-manufacturing, integrating both design and manufacturing knowledge. Using the presented approach helps overcome the bottleneck of knowledge acquisition and knowledge update through its self-learning component based on data mining and knowledge discovery.

The individualization of patient-specific ankle joint orthoses is becoming increasingly important and can be ideally realized by means of additive manufacturing. However, currently, there are no functional additively manufactured fiber-reinforced products that are used in the field of orthopedic treatment. In this paper, an approach as to how additively manufactured orthopedic products can be designed and produced quickly and flexibly in the future is presented. This is demonstrated using the example of a solid ankle–foot orthosis. For this purpose, test results on PETG-CF15, which were determined in a previous work, were integrated into a material map for an FEA simulation. Therewith, the question can be answered as to whether production parameters that were determined at the test specimen level can also be adapted to real, usable components. Furthermore, gait recordings were used as loading conditions to obtain exact results for the final product. In order to perfectly adapt the design of the splint to the user, a 3D scan of a foot was performed to obtain a perfect design space for topology optimization. This resulted in a patient-specific and stiffness-optimized product. Subsequently, it was demonstrated that the orthosis could be manufactured using fused layer modelling. Finally, a comparison between the conventional design and the consideration of AM-specific properties was made. On this basis, it can be stated that the wearing comfort of the patient-specific design is very good, but the tightening of the splint still needs to be improved.

The field of application of data-driven product development is diverse and ranges from requirements through the early phases to the detailed design of the product. The goal is to consistently analyze data to support and improve individual steps in the development process. In the context of this work, the focus is on the design and detailing phase, represented by the virtual testing of products through Finite Element (FE) simulations. However, due to the heterogeneous data of a simulation model, automatic use is a big challenge. A method is therefore presented that utilizes the entire stock of calculated simulations to predict the plausibility of new simulations. Correspondingly, a large amount of data is utilized to support less experienced users of FE software in the application. Thus, obvious errors in the simulation should be detected immediately with this procedure and unnecessary iterations are therefore avoided. Previous solutions were only able to perform a general plausibility classification, whereas the approach presented in this paper is intended to predict specific error sources in FE simulations.

Purpose Here we aimed to compare two machining strategies regarding the marginal strength of CAD/CAM materials using a hoop-strength test in model sphero-cylindrical dental crowns, coupled with finite element analysis. Materials and Methods Five CAD/CAM materials indicated for single posterior crowns were selected, including a lithium disilicate (IPS e.max® CAD), a lithium (di)silicate (Suprinity® PC), a polymer-infiltrated ceramic scaffold (Enamic®), and two indirect resin composites (Grandio® Blocs and Lava™ Ultimate). A sphero-cylindrical model crown was built on CAD Software onto a geometrical abutment and machined using a Cerec MC XL system according to the two available protocols: rough-fast and fine-slow. Specimens were fractured using a novel hoop-strength test and analyzed using the finite element method to obtain the inner marginal strength. Data were evaluated using Weibull statistics. Results Machining strategy did not affect the marginal strength of any restorative material tested here. Ceramic materials showed a higher density of chippings in the outer margin, but this did not reduce inner marginal strength. IPS e.max® CAD showed the statistically highest marginal strength, and Enamic® and Lava™ Ultimate were the lowest. Grandio® Blocs showed higher performance than Suprinity® PC. Conclusions The rough-fast machining strategy available in Cerec MC XL does not degrade the marginal strength of the evaluated CAD/CAD materials when compared to its fine-fast machining strategy. Depending on the material, resin composites have the potential to perform better than some glass-ceramic materials.

The transformation of virtual product development to Digital Engineering (DE) requires the successful integration of Digital Engineering or data-driven methods into existing product development processes. Those methods allow for the analysis and usage of existing data. However, missing knowledge about these methods, as well as their performance or limitations, is a major burden for their application, especially in small and medium-sized enterprises. In order to close this gap, this paper proposes the AI4PD ontology, linking product development processes (PD) and Digital Engineering methods (AI). This knowledge representation gives companies an overview of the available methods to support them in selecting a suitable solution for their problems. The representation of AI4PD is performed in Protégé using the W3C standard OWL syntax. The opportunities of AI4PD are shown by a use case of identifying a DE-Method for predicting manufacturing possibilities based on test data and CAD files. Furthermore, after possible problems in existing product development processes are identified, AI4PD covers the necessary knowledge for a successful method of identification and integration to transform virtual product development to Digital Engineering.

Only by using accurate material data can precise simulation results be achieved. This principle also and especially applies in the field of crash simulation. However, in the simulation of short-fibre reinforced thermoplastics, material parameters are usually used that originate from the material testing of as-new samples. In order to get closer to the condition on the roads, where not only new vehicles are driving, the influence of service loads on the crashworthiness has to be investigated. This paper reports on studies of PBT GF30, a polybutylene terephthalate reinforced with 30% glass fibres, in which fatigue damage was induced in the material by cyclic loading. The residual strength was then determined in a high-speed experiment and compared with the strength of virgin samples. In order to enable the usability of the findings in the simulation, a modified failure criterion was implemented that takes the previous fatigue damage into account. The prediction quality of the simulation model was compared with the experimental findings and it can be concluded that there is good agreement.

A digital twin describes the virtual representation of a real process. This twin is constantly updated with real data and can thus control and adapt the real model. Designing suitable digital twins for path planning of autonomous robots or drones is often challenging due to the large number of different dynamic environments and multi-task and agent systems. However, common path algorithms are often limited to two tasks and to finding shortest paths. In real applications, not only a short path but also the width of the passage with a path as centered as possible are crucial, since robotic systems are not ideal and require recalibration frequently. In this work, so-called homotopic shrinking is used to generate the digital twin, which can be used to extract all possible path proposals including their passage widths for 2D and 3D environments and multiple tasks and robots. The erosion of the environment is controlled by constraints such that the task stations, the robot or drone positions, and the topology of the environment are considered. Such a deterministic path algorithm can flexibly respond to changing environmental conditions and consider multiple tasks simultaneously for path generation. A distinctive feature of these paths is the central orientation to the non-passable areas, which can have significant benefits for worker and patient safety. The method is tested on 2D and 3D maps with different tasks, obstacles, and multiple robots. For example, the robust generation of the digital twin for a maze and also the dynamic adaptation in case of sudden changes in the environment is covered. This variety of use cases and the comparison with alternative methods result in significant advantages, such as high robustness, consideration of multiple targets, and high safety distances to obstacles and areas that cannot be traversed. Finally, it was shown that the environment for the digital twin can be reduced to reasonable paths by constrained shrinking, both for real 2D maps and for complex virtual 2D and 3D maps.

The increasing use of low-viscosity lubricants in order to reduce the friction in machine elements such as rolling bearings is leading to increased operation in the mixed or boundary lubrication regime. The associated wear can lead to an earlier failure of tribological systems. In this context, a detailed wear simulation offers great potential with regard to the design of machine elements as well as the calculation of lifetimes. This contribution presents an approach for the numerical wear simulation of lubricated rolling/sliding-contacts. Therefore, a finite element method-based simulation model was developed which is able to deal with non-Gaussian surfaces and contacts subject to boundary and mixed lubrication. Using the example of an axial cylindrical roller bearing considering realistic geometry, locally varying velocities, and two load cases, the wear modeling results of the mixed and the boundary lubrication regime were illustrated. The wear coefficient required for Archard’s wear model was determined experimentally by means of a two-disc tribometer.

Repetitive overhead work with a heavy load increases the risk for work-related shoulder disorders. Occupational exoskeletons supporting arm elevation are potential solutions to reduce that risk by lowering the physical strains on the shoulder. Many studies have reported a reduction in shoulder stress in various overhead tasks by using such exoskeletons. However, the support demand can vary in each phase of motion as well as in each individual task. This paper presents a laboratory study with five participants to evaluate the influence of the support level of an active shoulder exoskeleton in different motion phases (e.g., arm lifting, screw-in, and arm lowering of two overhead tasks) to identify the potential optimization of support at each phase. Results show that the support level of the exoskeleton should be adapted to the motion phase of the two chosen tasks. A higher support force is desired for the screw phase compared to the arm lifting and lowering phases, and the support level needs to be reduced immediately for arm lowering after the screw phase. The time for switching the support levels can be recognized by the electric current of the screwdriver.

The trend towards lightweight design, driven by increasingly stringent emission targets, poses challenges to conventional joining processes due to the different mechanical properties of the joining partners used to manufacture multi-material systems. For this reason, new versatile joining processes are in demand for joining dissimilar materials. In this regard, pin joining with cold extruded pin structures is a relatively new, two-stage joining process for joining materials such as high-strength steel and aluminium as well as steel and fibre-reinforced plastic to multi-material systems, without the need for auxiliary elements. Due to the novelty of the process, there are currently only a few studies on the robustness of this joining process available. Thus, limited statements on the stability of the joining process considering uncertain process conditions, such as varying material properties or friction values, can be provided. Motivated by this, the presented work investigates the influence of different uncertain process parameters on the pin extrusion as well as on the joining process itself, carrying out a systematic robustness analysis. Therefore, the methodical approach covers the complete process chain of pin joining, including the load-bearing capacity of the joint by means of numerical simulation and data-driven methods. Thereby, a deeper understanding of the pin joining process is generated and the versatility of the novel joining process is increased. Additionally, the provision of manufacturing recommendations for the forming of pin joints leads to a significant decrease in the failure probability caused by ploughing or buckling effects.

In rolling or gear contacts, truncation of the contact ellipse can occur, for example, when an undercut extends into the contact area. For an elastic calculation approach, the edge constitutes a mathematical singularity, which is revealed by a theoretically infinitely high pressure peak. However, when elastic–plastic material behavior is taken into account, the pressure peak is limited by local hardening and yielding of the material, leading to plastic deformations. As a result, those calculations are rather challenging and the results partly unexpected due to the discontinuity contained in the geometry. Nevertheless, to the authors’ knowledge, hardly any published studies exist on elastic–plastic simulations of truncated contact ellipses. Therefore, a numerical study concerning the contact of a rigid ball with an elastic–plastic plane is presented. Due to an undercut in the plane, a quarter of the theoretical Hertzian contact ellipse is cut off. The aim of the study is to investigate the influence of the undercut angle on the pressure distribution and the elastic and plastic deformation at the edge. The use of FEM shows that the undercut angle has a significant effect on the characteristics of the contact. The results obtained using FEM are then used as a reference for comparison with a semi-analytical method (SAM). It is shown that the SAM, based on the half-space, provides comparable results only for very small undercut angles.

Additive manufacturing offers a high degree of design freedom. When Design for Additive Manufacturing is conducted properly, lightweight potential can be exploited. This contribution introduces a novel design approach for the widespread fused layer modelling (FLM) technology when using orthotropic Fibre Reinforced Polymer filament. Its objective is to obtain stiff and strong load-path optimized FLM structures in a structured and algorithmic way. The approach therefore encompasses (1) build orientation optimization to consider weaker bonding between layers than intralayer; (2) topology optimization with orthotropic material properties to obtain favourable overall geometry and inner structure; (3) direct build path generation from optimized material orientation and alternatives to the direct generation and (4) simulation. The approach is demonstrated using a lift arm under multiple load cases and further demonstrator parts to show its general applicability. Lightweight potential of individual optimization steps and the influence of modifications contrasting general non-FLM-specific optimization are studied and discussed.

Along with the ever-increasing customer demands, early consideration of variation in terms of robust design is important to avoid expensive iterations in the product development. However, existing methods are detached from the development process and can therefore only be applied at a late stage or only with comprehensive expert knowledge. Especially in the concept stage, where the geometry of a product is not yet defined and the optimisation potential is high, effective solution proposals for systematic consideration of variation are lacking. Therefore, this paper describes a new integrated approach facilitating robust and tolerance design in the concept stage. The novelty of the approach using ontologies and graph-based visualisation is the close linkage of product development and tolerance knowledge, which allows automation and helps to avoid time- and cost-intensive iteration loops. As a result, a robust and tolerance-compliant concept design, an initial qualitative tolerance specification and instructions for the further tolerancing process are already available at the end of the concept stage. The applicability and the benefits of the approach are illustrated by representative case studies and a user study allowing a critical comparison between the conventional, mostly subjective procedure and the presented approach.

Additively manufactured parts play an increasingly important role in structural applications. Fused Layer Modeling (FLM) has gained popularity due to its cost-efficiency and broad choice of materials, among them, short fibre reinforced filaments with high specific stiffness and strength. To design functional FLM parts, adequate material models for simulations are crucial, as these allow for reliable simulation within virtual product development. In this contribution, a new approach to derive FLM material models for short fibre reinforced parts is presented; it is based on simultaneous fitting of the nine orthotropic constants of a linear elastic material model using six specifically conceived tensile specimen geometries with varying build direction and different extrusion path patterns. The approach is applied to a 15 wt.% short carbon-fibre reinforced PETG filament with own experiments, conducted on a Zwick HTM 5020 servo-hydraulic high-speed testing machine. For validation, the displacement behavior of a geometrically more intricate demonstrator part, printed upright, under bending is predicted using simulation and compared to experimental data. The workflow proves stable and functional in calibration and validation. Open research questions are outlined.

In a three-dimensional transient simulation of the elastic–plastic rolling contact, transient effects can be observed at the beginning of the rolling until a stationary state is reached after rolling for a length of several times the contact radius. In most cases, the steady-state regime is in focus of scientific investigations, whereas the transient effects are hardly considered. In the present work, those transient effects at the beginning of a frictionless rolling contact of a rigid sphere on an elastic–plastic plane are studied in detail. The analysis is limited to isotropic strain hardening. In particular, the changes of the contact pressure during rolling, as well as the plastic strain state and plastic deformations remaining after rolling are investigated. This is intended to get to the bottom of existing explanatory approaches from literature, which are based on the change in conformity. Beyond that, a more profound explanation of the transient effects is developed by identifying existing correlations.

The computer aided internal optimisation (CAIO) method produces an optimised fibre layout for parts made from fibre-reinforced plastics (FRP), starting from an initial shell geometry and a given load case. Its main principle is iterative reduction of shear stresses by aligning fibre main axes with principal normal stress trajectories. Previous contributions, ranging from CAIO’s introduction over testing to extensions towards multi-layer FRP laminates, highlighted its lightweight design potential. For its application to laminate design approaches, alterations have been proposed; however, questions remain open. These questions include which convergence criteria to use, how to handle ambiguous principle normal stress trajectories, influence of using multi-layer CAIO optimisation instead of the initial single-layer CAIO and how dire consequences of slightly deviating fibre orientations from the optimised trajectories are. These challenges are discussed in depth and guidelines are given. This paper is an enhanced version of a distinguished contribution at the first symposium ‘Lightweight Design in Product Development’, Zurich (June 14–15, 2018).

In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use of Gaussian processes for the prediction of the resulting coating characteristics to enhance the design of amorphous carbon coatings. In this regard, by using Gaussian process regression (GPR) models, a visualization of the process map of available coating design is created. The training of the GPR models is based on the experimental results of a centrally composed full factorial 23 experimental design for the deposition of a-C:H coatings on medical UHMWPE. In addition, different supervised machine learning (ML) models, such as Polynomial Regression (PR), Support Vector Machines (SVM) and Neural Networks (NN) are trained. All models are then used to predict the resulting indentation hardness of a complete statistical experimental design using the Box–Behnken design. The results are finally compared, with the GPR being of superior performance. The performance of the overall approach, in terms of quality and quantity of predictions as well as in terms of usage in visualization, is demonstrated using an initial dataset of 10 characterized amorphous carbon coatings on UHMWPE.

Rolling bearings have to meet the highest requirements in terms of guidance accuracy, energy efficiency, and dynamics. An important factor influencing these performance criteria is the cage, which has different effects on the bearing dynamics depending on the cage’s geometry and bearing load. Dynamics simulations can be used to calculate cage dynamics, which exhibit high agreement with the real cage motion, but are time-consuming and complex. In this paper, machine learning algorithms were used for the first time to predict physical cage related performance criteria in an angular contact ball bearing. The time-efficient prediction of the machine learning algorithms enables an estimation of the dynamic behavior of a cage for a given load condition of the bearing within a short time. To create a database for machine learning, a simulation study consisting of 2000 calculations was performed to calculate the dynamics of different cages in a ball bearing for several load conditions. Performance criteria for assessing the cage dynamics and frictional behavior of the bearing were derived from the calculation results. These performance criteria were predicted by machine learning algorithms considering bearing load and cage geometry. The predictions for a total of 10 target variables reached a coefficient of determination of R2≈0.94 for the randomly selected test data sets, demonstrating high accuracy of the models.

Within the domain of tribology, enterprises and research institutions are constantly working on new concepts, materials, lubricants, or surface technologies for a wide range of applications. This is also reflected in the continuously growing number of publications, which in turn serve as guidance and benchmark for researchers and developers. Due to the lack of suited data and knowledge bases, knowledge acquisition and aggregation is still a manual process involving the time-consuming review of literature. Therefore, semantic annotation and natural language processing (NLP) techniques can decrease this manual effort by providing a semi-automatic support in knowledge acquisition. The generation of knowledge graphs as a structured information format from textual sources promises improved reuse and retrieval of information acquired from scientific literature. Motivated by this, the contribution introduces a novel semantic annotation pipeline for generating knowledge in the domain of tribology. The pipeline is built on Bidirectional Encoder Representations from Transformers (BERT)—a state-of-the-art language model—and involves classic NLP tasks like information extraction, named entity recognition and question answering. Within this contribution, the three modules of the pipeline for document extraction, annotation, and analysis are introduced. Based on a comparison with a manual annotation of publications on tribological model testing, satisfactory performance is verified.

Gear pairs made of the material pairing metal-polymer provide advantages, such as a reduced weight, beneficial damping properties and the possibility to be operated in dry running conditions. However, the service life of the pairing is limited due to wear. The properties of the metallic gearing have a significant influence on the wear behavior of the material pairing. From previous investigations, the influence of the surface topography and the flank hardness of the metal pinion is known. With regard to resource saving and efficient manufacturing of the metal pinion, cold forging offers benefits. Through cold forging, metallic gears for the material pairing can be produced ready-to-use in a process suitable for serial production. In order to enable manufacturing by extrusion, the application of gear radii is necessary. The gear radii significantly affect the extrusion process and the achievable gear properties. However, the influence of gear radii on wear within the metal-polymer material pairing has not yet been investigated. Within this contribution, the influence of the gear radii on the contact behavior as well as the resulting local load and wear of the tooth flank is determined. For this purpose, wear tests with aluminum (AlMgSi1) and steel (16MnCr5) gears with different gear radii within the pairing with polyamide (PA66) gears were performed. It has been shown that the local wear of the tooth flank can be attributed to the local load and that adjusted gear radii lead to a varying load and wear of the metal and polymer gears. Based on the findings, functional relationships regarding the choice of gear radii and the wear behavior are derived which can be applied in the design of cold forged gears.

Mass customization aims to meet individual requirements and, therefore, is one way to attract and retain customers—a key challenge in the design industry. The increase in design automation has offered new opportunities to design customized products at high speed in a way that is cost equivalent to mass production. Design automation is built upon the reuse of product and process knowledge. Ontologies have proven to be a feasible, highly aggregated knowledge representation in engineering design. While product and process knowledge from other lifecycle phases are represented in multiple approaches, the design process of the product as well as the adaption process of product variants is missing, causing breakpoints or additional iterations in design automation. Therefore, suitable knowledge representation tailored to design automation is still missing. Accordingly, this contribution proposes a novel knowledge representation approach to enable design automation for mass customization. Methodically, this novel approach uses semantic enrichment of CAD environments to automatically deduce information about a design task, design rationale, and design process represented by a formal ontology. The integration of the design process significantly differentiates the approach from previous ones. The feasibility of the approach is demonstrated by a bike crank customization process.

Diamond-like carbon coatings may decrease implant wear, therefore, they are helping to reduce aseptic loosening and increase service life of total knee arthroplasties (TKAs). This two-part study addresses the development of such coatings for ultrahigh molecular weight polyethylene (UHMWPE) tibial inlays as well as cobalt-chromium-molybdenum (CoCr) and titanium (Ti64) alloy femoral components. While the deposition of a pure (a-C:H) and tungsten-doped hydrogen-containing amorphous carbon coating (a-C:H:W) as well as the detailed characterization of mechanical and adhesion properties were the subject of Part I, the tribological behavior is studied in Part II. Pin-on-disk tests are performed under artificial synovial fluid lubrication. Numerical elastohydrodynamic lubrication modeling is used to show the representability of contact conditions for TKAs and to assess the influence of coatings on lubrication conditions. The wear behavior is characterized by means of light and laser scanning microscopy, Raman spectroscopy, scanning electron microscopy and particle analyses. Although the coating leads to an increase in friction due to the considerably higher roughness, especially the UHMWPE wear is significantly reduced up to a factor of 49% (CoCr) and 77% (Ti64). Thereby, the coating shows continuous wear and no sudden failure or spallation of larger wear particles. This demonstrated the great potential of amorphous carbon coatings for knee replacements.

Diamond-like carbon (DLC) coatings have the potential to reduce implant wear and thus to contribute to avoiding premature failure and increase service life of total knee replacements (TKAs). This two-part study addresses the development of such coatings for ultrahigh molecular weight polyethylene (UHMWPE) tibial inlays as well as cobalt–chromium–molybdenum (CoCr) and titanium (Ti64) alloy femoral components. While a detailed characterization of the tribological behavior is the subject of part II, part I focusses on the deposition of pure (a-C:H) and tungsten-doped hydrogen-containing amorphous carbon coatings (a-C:H:W) and the detailed characterization of their chemical, cytological, mechanical and adhesion behavior. The coatings are fabricated by physical vapor deposition (PVD) and display typical DLC morphology and composition, as verified by focused ion beam scanning electron microscopy and Raman spectroscopy. Their roughness is higher than that of the plain substrates. Initial screening with contact angle and surface tension as well as in vitro testing by indirect and direct application indicate favorable cytocompatibility. The DLC coatings feature excellent mechanical properties with a substantial enhancement of indentation hardness and elastic modulus ratios. The adhesion of the coatings as determined in modified scratch tests can be considered as sufficient for the use in TKAs.

In the context of targeted improvements in energy efficiency, secondary rolling bearing contacts are gaining relevance. As such, the elastohydrodynamically lubricated (EHL) roller face/rib contact of tapered roller bearings significantly affects power losses. Consequently, this contribution aimed at numerical optimization of the pairing’s macro-geometric parameters. The latter were sampled by a statistical design of experiments (DoE) and the tribological behavior was predicted by means of EHL contact simulations. For each of the geometric pairings considered, a database was generated. Key target variables such as pressure, lubricant gap and friction were approximated by a meta-model of optimal prognosis (MOP) and optimization was carried out using an evolutionary algorithm (EA). It was shown that the tribological behavior was mainly determined by the basic geometric pairing and the radii while eccentricity was of subordinate role. Furthermore, there was a trade-off between high load carrying capacity and low frictional losses. Thereby, spherical or toroidal geometries on the roller end face featuring a large radius paired with a tapered rib geometry were found to be advantageous in terms of low friction. For larger lubricant film heights and load carrying capacity, spherical or toroidal roller on toroidal rib geometries with medium radii were favorable.

The development of hybrid trusses made of carbon-fiber-reinforced plastic struts and aluminum knots is currently not standardized, and there is no overall method for the design, although it has been proven that mass reduction is feasible. This paper introduces a new method for computer-aided engineering based design of hybrid trusses using carbon-fiber-reinforced plastic struts and metal nodes based on a modular system. The method includes all design steps from topology optimization to computer-aided design model generation and offers support to the engineer. The method is discussed in theory. A case study is done with a beam-shaped truss. It shows that if the bisection optimization method is combined with further constraints, it is suitable for selecting the optimum struts from a modular system for the truss. The developed approach is a suitable method for designing hybrid trusses. The basis of the method has been developed and will be further detailed and extended.

Due to rising demands regarding the functionality and load-bearing capacity of functional components such as synchronizer rings in gear systems, conventional forming operations are reaching their limits with respect to formability and efficiency. One way to meet these challenges is the application of the innovative process class of sheet-bulk metal forming (SBMF). By applying bulk forming operations to sheet metal, the advantages of both process classes can be combined, thus realizing an optimized part weight and an adapted load-bearing capacity. Different approaches to manufacturing relevant part geometries were presented and evaluated regarding the process properties and applicability. In this contribution, a self-learning engineering workbench was used to provide geometry-based data regarding a novel component geometry with circumferential involute gearing manufactured in an SBMF process combination of deep drawing and upsetting. Within the comprehensive investigations, the mechanical and geometrical properties of the part were analyzed. Moreover, the manufactured components were compared regarding the increased fatigue strength in cyclic load tests. With the gained experimental and numerical data, the workbench was used for the first time to generate the desired component as a CAD model, as well as to derive design guidelines referring to the investigated properties and fatigue behavior.