Analysing hierarchical design processes is difficult due to the technical and organizational dependencies spanning over multiple levels. The V-Model of Systems Engineering considers multiple levels. It is, however, not quantitative. We propose a model for simulating hierarchical product design processes based on the V-Model. It includes, first, a product model which structures physical product properties in a hierarchical dependency graph; second, an organizational model which formalizes the assignment of stakeholder responsibility; third, a process model which describes the top-down and bottom-up flow of design information; fourth, an actor model which simulates the combination of product, organization and process by using computational agents. The quantitative model is applied to a simple design problem with three stakeholders and three separate areas of responsibility. The results show the following phenomena observed in real-world product design: design iterations occur naturally as a consequence of the designers’ individual behaviour; inconsistencies in designs emerge and are resolved. The simple design problem is used to compare point-based and interval-based requirement decomposition quantitatively. It is shown that development time can be reduced significantly by using interval-based requirements if requirements are always broken down immediately.

The design of gear boxes is a complex challenge characterized by conflicting requirements and seemingly circular dependencies. Existing tools support engineers but focus on a single predefined design, often leading to costly iterative processes and non-optimal solutions. Solution Space Engineering (SSE) alleviates this by generating multiple designs represented by solution spaces. For this, a particular model structure is needed, and thus restructuring existing models, e.g., from industry standards. The application of solution spaces to a two-stage gear box is presented.

Additive manufacturing (AM) became a key technology in the development of innovative products. Advancements have been made to improve economic feasibility. However, ecological sustainability is still an open issue of AM. To improve sustainability, it is crucial to track, visualize, and evaluate emissions along the lifecycle. This paper presents a novel Digital Twin based approach enabling prediction of the product carbon footprint (PCF) and prescriptive measures to improve sustainability. By improving part and process design, a significant PCF reduction was achieved.

This paper presents a method for the lightweight design of robotic links subject to dynamic loads and requirements on the overall system stiffness. It includes (1) a decomposition scheme to enable separate component optimization and (2) an approach based on topology optimization for optimal load path design of screw connections. The approach reduces computing cost and mass of designs with screw connections.

Lab-on-a-Chip (LOC) products for point-of-care diagnostics have gained significant attention. However, there is a lack of systematic approaches for LOC product development. To address this, we introduce an Attribute Dependency Graph exemplary for a magnetophoretic LOC system for pathogen detection. This model organizes dependencies between the design variables and crucial quantities of interest, such as detectability, cost per test, and test duration. The obtained model helps to manage design complexity and can be adapted to other LOC approaches.

Design education prepares novice designers to solve complex and challenging problems requiring diverse skill sets and an interdisciplinary approach. Hackathons, for example, offer a hands-on, collaborative learning approach in a limited time frame to gain practical experience and develop problem-solving skills quickly. They enable collaboration, prototyping and testing among interdisciplinary teams. Typically, hackathons strongly focus on the solution, assuming that this will support learning. However, building the best product and achieving a strong learning effect may not be related. This paper presents the results of an empirical study that examines the relationship between product quality, learning effect and effort spent in an academic 2-week hackathon. Thirty teams identified user problems in this course and developed hardware and mechatronic products. This study collected the following data: (1) effort spent during the hackathon through task tracking, (2) learning effect through self-assessment by the participants and (3) product quality after the hackathon by an external jury. The study found that the team effort spent has a statistically significant but moderate correlation with product quality. The correlation between product quality and learning effect is statistically insignificant, suggesting that for this setting, there is no relevant association.

Designing vibrating systems is challenging due to component interaction. One approach to reduce the resulting complexity is top-down design where requirements on components are formulated such that the overall system achieves the design goal. Previous work showed how to derive quantitative and solution-neutral requirements on components of a vibrating system, expressed as permissible ranges of impedance. This work adapts the methodology to a practical use case and provides a concrete technical solution: A hammer drill that can cause white finger syndromes to users is equipped with an appropriate vibration absorber. The hammer drill is represented by a lumped mass model and validated using experimental data of a reference design. Solution-neutral and quantitative component requirements on the overall dynamics of the vibration absorber expressed by impedance are derived. They provide a clear target for the component design. A vibration absorber in form of a Tuned Mass Damper (TMD) is designed accordingly. The final design is validated experimentally and shown to reduce the vibration by 47%.

Tuned mass dampers may be used to improve vibrational behavior of structures. However, they require space to move. This paper presents an approach to incorporate tuned mass dampers into a lightweight-optimized structure without extra space requirement. It is based on (1) topology optimization (TopOpt) with unit cells and (2) vibration reduction with multiple tuned mass dampers (m-TMD) within the unit cells. The topology optimization is performed with a physics-informed penalty factor, unique to the chosen unit cell. Subsequently, the weight optimal density distribution is realized by populating the design domain with unit cells of ten different densities. To reduce the induced vibration, m-TMDs are placed inside the cavities of the unit cells in the grey scale regions. The effectiveness of the approach is demonstrated for the design of a 2-segment robot arm. The resulting unit cell robotic arm (UC-Arm) is 3.6% lighter than the reference model, maintains the same static performance, and shows a 60% smaller dynamic displacement in the observed frequency range. No extra space is required for the motion of the m-TMD.

Optimization-driven design offers advantages over traditional experience-based mechanical design. As an example, topology optimization can be a powerful tool to generate body shapes for Additive Manufacturing (AM). This is helpful, when (1) load paths are non-intuitive due to complex design domains or boundary conditions, or (2) the design process is to be automated to minimize effort associated with experience-based design. However, practically relevant boundary conditions are often difficult to put into a formal mathematical language to, for example, either feed it into a topology optimization algorithm, or provide precise quantitative criteria for CAE-supported manual design. This paper presents a survey of three industry use cases and identifies three types of requirements: the first can be directly cast into parts of an optimization problem statement (∼ 40%), the second is considered indirectly by adapting the optimization problem without explicit reference to the requirement (∼ 20%), and the third is only assessed after the design is finalized (∼ 40%). For categories 2 and 3 we propose directions of improvement to support formulating complex design tasks as unambiguous design problems.

ABSTRACT

Solution spaces are sets of designs that meet all quantitative requirements of a given design problem, aiding requirement management. In previous works, ways of calculating subsets of the complete solution space as hyper-boxes, corresponding to a collection of permissible intervals for design variables, were developed. These intervals can be used to formulate independent component requirements with built-in tolerance. However, these works did not take physical feasibility into account, which has two disadvantages: first, solution spaces may be useless, when the included designs cannot be realized. Second, bad designs that are not physically feasible unnecessarily restrict the design space that can be used for requirement formulation.

In this paper, we present the new concept of a requirement space that is defined as the largest set of designs that (1) allows for decomposition (e.g., into intervals when it is box-shaped), (2) maximizes the useful design space (good and physically feasible), and (3) excludes the non-acceptable design space (bad and physically feasible). A small example from robot design illustrates that requirement spaces can be significantly larger than solution spaces and thus improve requirement decomposition.

Complex systems typically consist of many components and are subject to many requirements. Approaches like the V-Model support complex systems design by providing guidelines on how to break down large systems into smaller pieces. Models built with SysML provide documentation on an abstract level. However, neither incoporates detailed information on components that may be relevant for design decisions. In robot-like systems, e.g., the choice of transmissions will depend on the system dynamics of the robot. This is modeled in the time domain, where detailed time-dependent component interaction is considered. The design perspective, however, is best represented in the property domain. Here, requirements on static component properties are formulated.

This paper presents a generic approach that connects the property and time domains to enable early-stage design decisions. The approach is applied to a 1-link robot system with a simple demonstrator transmission model, including properties that are typically not considered in the early design phase, like a nonlinear stiffness characteristic with backlash.

Digital Twins are one of the most trending topics. However, there are still open issues in the development of Digital Twins. One of these impediments is formulating a clear and valid value proposition of a Digital Twin. Therefore, this paper presents a novel business modelling approach for Digital Twins. Based on a literature review and an academic case study, different business modelling approaches and techniques were systematically compared, selected and adjusted in order to develop a new framework suitable for describing the value of Digital Twins. It consists of 10 steps – (1) describe a basic development concept of the DT, (2) identify the customer segments, needs and pain points, (3) derive a value proposition (4) identify revenue streams and values created, (5) identify key activities, (6) resources needed, and (7) necessary partners. (8) Adapt the organization to fit the business model and (9) evaluate the cost structure of the project. In the end, document and communicate the new business model (10). Each step is supported by auxiliary methods, tools and procedures. The approach was applied to a scientific case study. In an initial evaluation the overall applicability and usefulness of the approach were confirmed.

The continuous integration and verification of components is essential in distributed design processes. Identifying the optimal integration and verification frequency, however, can be challenging due to the complexity of product development. Especially the effect of human decision-making in partially isolated development scenarios is difficult to consider. Thus, we performed an experimental study based on the following three steps: first, an extension of the existing parameter design framework, which is used to conduct experiments under laboratory conditions, in which human subjects solve quantitative surrogate design tasks. Second, a series of experiments in which 32 subjects divided into groups of two solved 229 parameter design tasks with a varying integration and verification frequency. And, third, a statistical analysis of the results with respect to development time, coupling strength and process costs. According to our results, development time can be reduced by up to 71%, if the integration and verification frequency is doubled. If process costs are also considered, the optimal frequency can be subject to a conflict of goals between reducing development time and minimising process cost.

Complexity in product design increases with little understanding of cause and effect. As a consequence, the impact of design decisions (or changes) on the product is difficult to predict and control. This article presents a model of cause and effect for design decisions that avoid circular dependencies: the so-called attribute dependency graph (ADG) models complex system behaviour and properties, and increases transparency by carefully distinguishing between what is realised and what is required. An ADG is a polyhierarchy, with design variables (directly controllable) at the bottom, quantities of interest (not directly controllable) on the top, and intermediate attributes. The dependencies represent causality in a simple sense: assigning values to design variables, representing the cause, will determine the values of the dependent attributes, representing the effect. ADGs do not account for what is required, but for what effects emerge by design activity. A set of rules makes them independent of designers’ views. They provide the structure for so-called INUS conditions, that is, insufficient but necessary parts of unnecessary but sufficient conditions that can be used for requirement development. The modelling approach is applied to one simple synthetic and then to two real-world design problems, the design of a water hose box and a passenger vehicle.

Process models are among the principal artefacts used for managing design projects. However, the selection of effective modelling approaches can be difficult for design project managers, given that a plethora of tools exists for various modelling purposes. In addition to date no systematic approach for the assessment and selection of process modelling approaches is available to practitioners. This paper presents the development of criteria for benchmarking and selecting different process modelling tools. The results are based on three elements. (1) In a four-hour workshop undertaken by the Design Process SIG of the Design Society, bringing together around 20 international researchers and practitioners in design process modelling, an initial set of 58 criteria were brainstormed and consolidated during the workshop and in follow-up meetings. (2) The consolidated criteria were then compared with literature. The finalised criteria list was then validated by external experts in industry (3). The resulting list of 12 criteria provides a sound basis for practitioners to support a systematic selection of process modelling approaches. Further, it lays the foundation of a benchmarking tool, which is subject to future work.

Increasing product complexity and individual customer requirements make the design of optimal product families difficult. Numerical optimization supports optimal design but must deal with the following challenges: many design variables, non-linear or discrete dependencies, and many possibilities of assigning shared components to products. Existing approaches use simplifications to alleviate those challenges. However, for use in industrial practice, they often use irrelevant commonality metrics, do not rely on the actual design variables of the product, or are unable to treat discrete variables. We present a two-level approach: (1) a genetic algorithm (GA) to find the best commonality scheme (i.e., assignment scheme of shared components to products) and (2) a particle swarm optimization (PSO) to optimize the design variables for one specific commonality scheme. It measures total cost, comprising manufacturing costs, economies of scales and complexity costs. The approach was applied to a product family consisting of five water hose boxes, each of them being subject to individual technical requirements. The results are discussed in the context of the product family design process.

In the development of mechatronic systems, it is important to differentiate the products to be developed from those of the competitors, but also from the own product generations already available on the market. However, there are uncertainties regarding the correct features to be differentiated from the customer and user perspective and how these features should be designed. Nevertheless, despite the high impact, decisions must be taken early in the development process. Within this publication, a method to support in this respect was derived based on the findings of the Model of PGE - Product Generation Engineering and the Kano-Model. Therefore, experienceable product features and reference system elements as characteristics of these are evaluated according to the Kano-Model and thus made comparable with regard to the customers and user satisfaction. The objective is to select the product features to be differentiated and the corresponding reference system elements in such a way that a desired level of customer and user satisfaction is achieved. In order to evaluate the method, it was applied in a real development project. It was found that the application of the method led to a reduction of the existing uncertainty.