The acoustic behavior of technical products—referred to as Noise, Vibration, and Harshness (NVH)—forms an important criterion in customers’ purchasing decisions, e.g. in the automotive sector. Thus, optimizing the NVH behavior is essential during product development. NVH is influenced by dynamic excitations and the transfer of sound from the location of excitation to a receiver such as the driver. As the electrification of the mobility sector increases, gear mesh excitations contribute significantly to the noise of a vehicle, which was previously dominated by the combustion engine. The time-varying stiffness of the gear mesh is the main excitation in drivetrains and therefore needs to be optimized during product development. Elastic multi-body simulation (eMBS) has become an established method for modeling NVH characteristics of drivetrains. In order to evaluate the effect of gear mesh designs on a drivetrain’s excitations and therefore its NVH behavior, the time-varying stiffness of the gear mesh has to be incorporated into eMBS models. Today, this is either done by applying quasi-static transmission errors onto a model in frequency domain thereby neglecting the influence of the system’s oscillations back on the gear mesh excitations, e.g. by axial misalignment of the meshing shafts. Alternatively, analytical stiffness calculations are included in eMBS models in time-domain. However, these approaches are not yet validated. Also, incorporating the interaction between a flexible gear wheel and the gear mesh stiffness is paramount for NVH models of gears. Within previous works, a method for incorporating externally calculated gear mesh stiffnesses into eMBS has been proposed to overcome these challenges. The goal of this paper is to compare, validate and enhance the existing eMBS modeling methods of gear mesh stiffnesses with respect to their ability to model the dynamic behavior of gear boxes, defined by natural frequencies and critical operating points. A two-step approach consisting of quasi-static and dynamic validations is conducted. An improvement in accuracy between simulation and measurement of 10 dB is achieved compared to analytical eMBS methods.

Rising consumer demand and stricter regulations regarding sustainability are leading to an increasing consideration of sustainability aspects during product development. To evaluate environmental impacts, life cycle assessments (LCAs) are established as the most comprehensive method. However, the extensive data-need for setting up the life cycle inventory (LCI) model limits their widespread application. Moreover, LCAs are normally conducted post-development, offering no support in decision making during the development process. An approach is needed that provides product-specific information during product development to be used in LCAs to allow the execution of LCAs already during product development. Model-based systems engineering (MBSE) promises to meet this need by providing a framework for seamless interdisciplinary product development. A central element in many MBSE approaches is a SysML system model that describes the architecture and behavior of the product and provides all relevant information for needed domain-specific models. In this contribution, a novel approach is presented utilizing a SysML system model to automatically execute an impact assessment within an LCA tool. For this, the minimum data-need for setting up LCI models in established LCA tools is analyzed, and a framework for modeling life cycles in SysML system models is presented. To allow an automated execution of an LCA, an interface between a SysML tool and an LCA tool is implemented. In the example of a laser-structured metal part, this contribution shows that SysML system models are suited to provide the needed product-specific data for setting up LCI models and that they can be utilized to execute LCAs already during product development.

The modern, ever shorter development process of powertrains incorporates many disciplines of mechanical engineering. Among these, the dynamic and acoustic behavior—which is often referred to as noise, vibration, harshness (NVH)—is becoming increasingly important for the consumer’s purchase decision. However, NVH is often only considered in the last steps of the product development as both experimental analysis methods and high-fidelity modeling approaches are time-consuming. In contrast, in early phases of the development process, fast iteration cycles are crucial. Thus, the engineer needs appropriate NVH models for each phase of the development process in order to evaluate design and concept decisions with respect to their NVH implications.This publication shows a methodological guide on how to apply suitable NVH models forward directed in different stages of the development process and thus efficiently implement optimizations. In the early, highly iterative phases of the development process, analytical determination of the excitation behavior as well as simplified finite element simulations are carried out in order to estimate the vibration behavior of the concept. For the first elaborated geometries, frequency response functions can then be determined and thus the influence of e.g. ribbings to NVH behavior can be evaluated. In the later phases, where advanced simulation models have to be set up, a fully parameterized eMBS model of the drivetrain enables an analysis of its operating behavior, from which further optimization potential of the system can be derived. The proposed method is demonstrated on the example of a tractor transaxle development process.

The acoustic and structural dynamic behavior of technical products—often referred to as Noise, Vibration, and Harshness (NVH)—is an important criterion in customers’ purchasing decisions. Therefore, NVH optimization is a crucial goal of the development process. NVH is influenced by dynamic excitations, caused by time-varying forces e.g. in gears or electric machines, as well as the transfer path of sound from the location of excitation to a receiver such as a driver’s ear and the sound perception. The behavior of the transfer path is determined by amplification or isolation of structure-borne and airborne sound between the location of the excitation and the location of the receiver. As a result, eigenfrequencies and damping in the transfer path form crucial structural dynamic properties.Within jointed structures, friction between the surfaces of lap joints are the most dominant influencing factor on the transfer path’s damping. In order to increase the efficiency of NVH optimization in the development process, methods of virtual product development have become an established tool. Especially finite element method (FEM) and elastic multi-body simulation (eMBS) have allowed to numerically evaluate the structural dynamic behavior of technical products before manufacturing physical prototypes. While numerous methods for modeling jointed structures exist in FEM and eMBS environments, the choice of suitable eMBS models of joints remains challenging today. This is because the frictional behavior between the jointed partners in higher frequencies is largely influenced by the deformation of the joint surface. However, the deformation of the jointed parts is simplified in eMBS models compared to quasi-continuous FEM models. Accordingly, the choice of a suitable spatial discretization of eMBS joint models is a key factor in the model quality of eMBS models. Thus, this work proposes a modeling guideline for eMBS models of jointed structures based on the deformation of the structure using the criterion of rigidness. The method is demonstrated on an eMBS model of the academic Brake Reuß beam, which consists of two beam elements jointed with three screws in an overlap lap joint. Good agreement between eMBS simulation and measurement with an error of 1 dB is achieved using the proposed modeling method.

Modal testing is used to experimentally determine the dynamic behavior of mechanical structures. The planning of the positions for exciting the structure, the so-called driving points, is essential for efficient experimental modal testing. Driving points are identified either by an initial assumption of possible excitation points and the experimental evaluation of their quality, or with the help of numerical models and indicators for optimal driving point selection. However, for practical applications, several driving points are usually required to excite all modes in the frequency range of interest, which is not covered in state-of-the-art indicators for driving point selection. This paper therefore presents a method for driving point identification with consideration of multiple driving points. Additionally, a criterion was developed that considers the excitability of modes, risks of double hits, and the excitation orientation simultaneously in order to increase the accuracy of the driving point identification. This criterion enables the evaluation of the excitation quality of each surface node and any mode combination using an automated selection method for determining the optimum set of driving points. The presented method assumes that natural frequencies and eigenvectors from preliminary numerical models are available for planning. By applying the method on application examples, the reduced effort required for a full modal analysis is demonstrated.