Julius Moritz Berges

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.

Developing increasingly complex multidisciplinary products in short development cycles is one major challenge in today’s product development. Model-based Systems Engineering (MBSE) approaches are well suited to address this challenge. With MBSE, products are virtually represented in central system models. For the efficient verification of customer requirements and to avoid exhaustive physical testing with prototypes, virtual domain models (e.g. FE-models) are integrated into the system model. To perform a virtual test, domain models need to be executed in a sequence, so-called workflows.Current workflows link several product system levels in one workflow and are often only valid for one specific system architecture. As the number of requirements and system complexity increases, these workflows become also more complex. The effort for creating new comprehensible workflows is currently high and the reusability cannot be ensured. To solve these deficits, a method for the systematic formalization of reusable workflows in system models as well as their structured integration is presented. Behavior diagrams in the modelling language SysML are used to control the execution order of the domain models of different purposes and fidelities. Modular sub-workflows are developed for each system level. These sub-workflows can be reused and combined modularly to form larger workflows. The approach shows a high potential to easily build and organize workflows in reusable libraries thereby supporting automated virtual testing in product development. To demonstrate the approach, workflows for bearing lifetime calculation and shaft fatigue testing of a wind turbine drive train as well as their integration into the SysML system model are presented.

The acoustic and structural dynamic properties of vehicles—often referred to as Noise, Vibration, Harshness (NVH)—form a crucial criterion during product development. To reduce iterations with physical prototypes, NVH simulation models are well established. In early development phases, many parameters of NVH models, such as material and contact properties, are either assumed based on empirical values or have to be measured. In both cases, the value of these parameters is uncertain. Thus, the output of NVH system simulation models such as structure borne or air borne sound is also uncertain and must be quantified. However, applying state-of-the-art uncertainty analysis methods to NVH simulation models considering all uncertain input parameters is inefficient due to their high computation time. Therefore, this paper presents a method of coupled sensitivity (SA) and uncertainty analysis (UA), which enables the efficient uncertainty calculation for NVH simulations. In this method, firstly the most influential parameters are determined using a SA to reduce the number of input parameters. Depending on the number of parameters and the computation time of the NVH simulation model, either the Morris SA or an EFAST SA is chosen. Finally, a fuzzy UA is performed, which quantifies the uncertainty of the output of the NVH simulation and provides its possible ranges. The procedure is applied to the NVH model for predicting air borne sound of an electric drive with 53 uncertain input parameters.

To handle increasing complexity in product development, model-based systems engineering (MBSE) approaches are well suited, in which the technical system is represented in a system model. To efficiently test requirements, domain models are integrated into the system model. For each purpose (e.g., battery lifetime calculation), there are typically several models at several fidelity levels. Since the model signatures (i.e., necessary inputs for the models and their outputs) differ depending on the fidelity level, not all models can be used in any development phase. In addition, due to the different model signatures, not all models can be combined arbitrarily to model networks. Currently, valid model networks in system models must be determined in a time-consuming, manual process. Therefore, this paper presents an approach that automates this task via the implementation of an algorithm that analyzes a system model and the model signatures and automatically returns all valid model networks. When input parameters, models or their signatures change, the algorithm updates automatically, and the user receives the valid model network without any manual effort. The approach is demonstrated with the running example of battery system development.

The application of laser-structured metal surfaces to combine plastics and metals is a promising option to enable low-cost lightweight and resource-efficient multi-material joints. The mechanical properties (especially strength), as well as production time and costs, depend on the microstructure of the metal surface (e.g. the number, distance and shape of cavities). Thus, in order to design optimal joints, the properties from the mechanical, as well as production and cost domain, must be considered simultaneously during product development. Therefore, in this paper, a model-based optimization approach is presented with the goal of identifying an optimum between the strength of the joint and laser manufacturing costs. Parameterized models for the strength estimation, as well as calculation of laser manufacturing time and costs, are developed. The models are linked in an optimization workflow and the optimum positioning and shape of the cavities on the joining zone is determined using a genetic optimization algorithm. The results show that, compared to a benchmark, the manufacturing costs can be reduced by 82 % for the same strength using the proposed model-based optimization approach.

Laser-based plastic–metal joints have high potential to enable cost-efficient lightweight structures in multi-material design. By an appropriate load-optimized positioning of the microstructure on the joining zone, cost- and strength-optimized joints can be realized. However, there are no design methods and models to efficiently develop these tailored microstructures. Currently, time-consuming experiments are necessary to find the optimum microstructure concepts. These experiments must be repeated when requirements change, e.g., dimensions of the components. To provide a simple and efficient design tool, this paper presents an automated numerical method for the development of cost- and strength-optimized microstructure concepts for laser-based joining zones. The basis for the approach is a new numerical model which generates concepts for microstructures automatically based only on the stress tensor in the joining zone. A new finite element cohesive zone model (CZM) was developed to estimate the joint strength. The CZM parameters were efficiently derived from a finite element model of a single cavity. To determine the costs, a new model is presented that calculates the production time and the cost for any given microstructure. The models were interconnected in a combined optimization procedure and a genetic algorithm was used to determine cost- and strength-optimized microstructure concepts. The approach was applied to a demonstration example where the laser costs were reduced by up to 67% compared with benchmarks with surface-covering parallel linear cavities. The approach shows high potential for the efficient design of cost- and strength-optimal laser-based hybrid joints since it is fully based on simulation models and iterative experiments in the design stage are eliminated.

Plastic-metal joints with a laser-structured metal surface have a high potential to reduce cost and weight compared to conventional joining technologies. However, their application is currently inhibited due to the absence of simulation methods and models for mechanical design. Thus, this paper presents a model-based approach for the strength estimation of laser-based plastic-metal joints. The approach aims to provide a methodology for the efficient creation of surrogate models, which can capture the influence of the microstructure parameters on the joint strength. A parametrization rule for the shape of the microstructure is developed using microsection analysis. Then, a parameterized finite element (FE) model of the joining zone on micro level is developed. Different statistical plans and model fits are tested, and the predicted strength of the FE model and the surrogate models are compared against experiments for different microstructure geometries. The joint strength is predicted by the FE model with a 3.7% error. Surrogate modelling using half-factorial experimental design and linear regression shows the best accuracy (6.2% error). This surrogate model can be efficiently created as only 16 samples are required. Furthermore, the surrogate model is provided as an equation, offering the designer a convenient tool to estimate parameter sensitivities.

Locally load-optimized fiber-based composites, the so-called tailored textiles (TT), offer the potential to reduce weight and cost compared to conventional fiber-reinforced plastics (FRP). However, the design of TT has a higher complexity compared to FRP. Current approaches, focusing on solving this complexity for multiple objectives (cost, weight, stiffness), require great effort and calculation time, which makes them unsuitable for serial applications. Therefore, in this paper, an approach for the efficient creation of simplified TT concept designs is presented. By combining simplified models for structural design and cost estimation, the most promising concepts, regarding the cost, weight, and stiffness of TT parts, can be identified. By performing a parameter study, the cost, weight, and stiffness optima of a sample part compared to a conventional FRP component can be determined. The cost and weight were reduced by 30% for the same stiffness. Applying this approach at an early stage of product development reduces the initial complexity of the subsequent detailed engineering design, e.g., by applying methods from the state of the art.