Generative Design (GD) is a design approach that uses algorithms to generate designs. This paper investigates the role of optimisation algorithms in GD process. We study how Pareto Fronts – a classical optimization algorithm output – help designers to browse the variety associated with a design problem. Thanks to the “splitting condition” from design theory, we show that valuable Pareto Fronts for designers are those that allow the exploration of a variety of design parameters without modifying substantially the performance of the designed solution. We call “Splitting Pareto Front” the Pareto Fronts that display this property and investigate how to generate them. We compare, on an electrical battery design problem, two optimization algorithms – NSGA-II and MAP-Elites – based on the design parameters variety they generate. Our results show that MAP-Elites generates Pareto Fronts that are more splitting than those generated by NSGA-II. We then discuss this result in term of the design process: which algorithm is best suited for which design task? We conclude with the importance for future research on Generative Design Algorithms (GDA) to study jointly the functioning of GDA and their expected contribution to the design process.

Autonomous vehicles for the last mile are a promising use case for advancing autonomous driving in real-world traffic. For this purpose, traditional car manufacturers and newcomer companies develop a new vehicle concept: the autonomous shuttle. During the development, components from the automation domain, such as the sensors, must be placed and integrated into the vehicle body. The trade-offs between the functional performance of the perception and the exterior design must be evaluated early in the design process. For this purpose, a model of the vehicle exterior is needed. In this contribution, we present a method for parametric modeling of the vehicle exterior of autonomous shuttles. We define 17 input parameters and use computer-aided design to create a virtual model of the body and the wheelhouses. In the results, we validate our method by ensuring that existing shuttles can be modeled with our approach and also analyze the limitations. The model supports decision-making in the early design phase by enabling quick iterations between sensor placement and exterior design.

Gaze sharing is an emerging technology which can enhance human communication and collaboration. As such, it is expected to play an important role in future educational practices. To date, its application was not explored in the context of design education. Additionally, it was mainly implemented in non-immersive environments, which are limited in their potential for engaging learners. Acknowledging the growing interest in immersive learning, as well as the promise made by gaze sharing technology, we strive to develop an immersive gaze sharing environment, to support design education. As a first step, we have implemented and tested an immersive gaze sharing system for supporting design learners. The system was then tested by focusing on a scenario of architectural design, with the aim of collecting valuable user feedback regarding its usability and potential. Our initial user study informs developers of such systems regarding potential issues that may be encountered during deployment in a real-world setting and proposes concrete ways to address these. These insights can help to pave the way for integrating gaze sharing system into design education practices in the near future.

As an initial step towards a better understanding of cognitive load in computer-aided design (CAD), the herein presented study investigated cognitive load imposed on 24 mechanical engineers during two CAD modelling tasks of intentionally different complexity levels. The cognitive load has been rarely studied in the CAD context, which resulted in the lack of understanding if and how the EEG-based indicators available from the literature reflect the changes in cognitive load imposed on engineering designers in CAD activities. Therefore, cognitive load was measured and analysed using three EEG-based indicators to explore insights that might be obtained from them. The initial analysis revealed different cognitive load results from the employed indicators for the same EEG data. In addition, the study implies that the cognitive load results obtained through the used indicators are only partially coherent with the CAD modelling task complexity. Hence, the results imply that the chosen EEG-based indicator matters when measuring and analysing cognitive load in CAD modelling tasks and that its adjustment for CAD context might be needed.

Model-Based Definition provides several benefits for communicating between engineering and other downstream stakeholders. Particularly, semantic PMI information included in 3D models benefits both CAM programming and inspection phases. However, the efficiency of generating CAM- and CAI codes automatically according to the semantic PMI information varies due to the compatibility of different systems. This paper focuses on the experiment that we made on inspecting three different parts with the Coordinate Measuring Machine (CMM). We compared four different programming methods and found that the efficiency of inspecting depends on the serial size of the parts. The completely automatic CAI-programming method does not necessarily produce the most effective CAI code compared to the competent human programmer. This is notable, especially in large series due to achieved cumulative time savings in the inspection of each part. With small series, fully automatic PMI method and human-assisted automatic PMIfp method provide significant benefits in the inspection process due to time savings in CAI-programming work.

Automating modelling activities in computer-aided design (CAD) systems is no exception within design automation, one of the current research endeavours aiming to use and transform design-related data in design decision-making processes and the generation and evaluation facilitation of new design solutions. The paper explores the differences between CAD models based on their feature-based CAD modelling sequences that lead to the final models' design. The dataset collected and structured for the study contains more than 1400 CAD models clustered on two levels by using an unsupervised K-means clustering algorithm. The algorithm is performed on the number (total and unique) and the first-order Markov model transition matrices of the CAD modelling operations and their sequential order, respectively. Therefore, three and ten groups (clusters) of CAD models are obtained regarding the level of clustering. The results show that most of the obtained groups are specified by the dominant transition between particular modelling operations. In addition, the study also provides insight into the potential of using feature-based CAD modelling operations' sequences as a first step toward automating the user interaction with the CAD system.

The work introduced two novel multiscale multifunctional tire designs developed using the Domain Integrated Design (DID) method and modelled with the LatticeQuery geometric modelling software. Furthermore, this research validates the meta-level parameter “interaction area” proposed for selecting biological analogy in the DID method. These two use cases were simulated with Abaqus. The concepts covered in this work are an example of multi-functional design. The obtained results validate the meta-level parameter derived from the DID methodology.

Improvements in product development can increase the competitiveness of firms. However, new product development in CAD systems involves difficulties and uncertainties that increase along with the pressure to develop the products. A distinct characteristic of CAD modeling for new product development is its uncertainty. This is because the information is usually approximate and incomplete during CAD modeling. Thus, the main objective of this paper is to propose a robust and flexible CAD approach to reduce uncertainty and accelerate new product modeling in the context of design for manufacturing. This methodology permits the convergence towards different product forms depending on the selected manufacturing process. Application of this approach has shown that when uncertainty is high, approving a complete CAD modeling results in a delay in product development. In contrast, CAD modeling using fuzzy models results in a gain of valuable development time because the model is completed when knowledge about manufacturing technologies, company fit and capabilities, and markets is available.

No matter a system's size, complexity, or domain, iterations are fundamental to its design process. However, there is a duality: iterations are both signs of usefully exploring the system's design space and failure to find an appropriate solution. This ambiguity means that we have not been able to connect teams’ iterating behavior to their design's performance, potentially obscuring a way to influence the design process.

As such, our exploratory study unpacks the relationship between design iterations and performance. We observed 88 teams in the 2020 Robots to the Rescue Competition in rich detail. Using logs of 7,956 iterations on a Computer-Aided Design platform, we analyzed how high- and low-performing teams revised their submissions, searching for consistent differences in their behavior. We found significant differences in the iterations’ number, scale, and cadence between these groups of teams. These findings emphasized the correlation between certain iteration patterns and the success of a design: the best teams will likely revise differently than the worst ones. It also showed the importance of a fine-grained, time- dependent view of the design process to resolve open questions in the literature.

Lattice structures are lightweight and possess other unique mechanical and physical properties. Additive manufacturing techniques are often used to produce these structures. Additive manufacturing provides manufacturing freedom that significantly surpasses the one provided by conventional subtractive manufacturing. However, a gap exists between the manufacturing freedom and the geometric modelling freedom in additive manufacturing: it can be extremely challenging to model the designed part because of its high geometric complexity, such as heterogeneous lattice structures. While several tools on the market allow geometric modelling of such structures available on the market, the customization of lattice parameters can still be significantly improved. Moreover, no open-source tools exist to address this issue or to model lattice structures in general. This work presents a novel open-source library for the geometric modelling of lattice structures with customized parameters. The parameter customization is enabled with the function representation approach.

The development of cloud-based Computer-aided design (CAD) enabled real-time CAD collaboration between multiple designers. While this technology has great potential to change the way CAD work is done, it is still little explored. This paper presents a case with two high-performing and two low-performing three-member teams monitored with non-invasive methods (log data) during a six-week design project. The results show that high-performing teams focused more on the editing of assembly, while low-performing teams focused on creating and editing a part. Furthermore, high-performing teams tended to perform consecutive deleting actions and to transition to creating and editing classes of CAD actions after performing viewing actions. Two modelling approaches which lead to high-quality CAD models were identified. One approach is characterized by frequent use of transitions between editing and Organizing-Design (collaborative actions) classes, while the other between creating, editing and reversing classes. Presented results allow design teams to gain insight into sequential patterns which led to the generation of a high-quality CAD model and to better understand the CAD modelling process.

Product development is facing new challenges due to increasingly complex and individualized products in small batch sizes and short time to markets at high quality standards. Integrated product data management along with systematic requirements engineering and early stakeholder involvement are known to be key enablers for the success of future product development. In software development, established platforms such as GitHub exist, which have been shown to improve stakeholder communication, requirements elicitation, and software design decisions. In product development, similar platforms exist with impressive functionality, but which have some drawbacks such as closed source licenses, vendor-specific data formats, and expert-level user interfaces. To overcome the current situation, we study how the ideas of GitHub can be translated to an open source solution for product development and which concepts can be reused or must be changed. Core deliverables of our work are (1) an integrated data model of requirements (or design tasks), project schedules, and revisions of computer-aided design (CAD) models as well as (2) an interface model.

In reengineering technical components, the robust automation of reverse engineering (RE) could overcome the need for human supervision in the surface reconstruction process. Therefore, an enhanced computer-based geometric reasoning to derive tolerable surface deviations for reconstructing optimal surface models would promote a deeper geometric understanding of RE downstream processes. This approach integrates advanced surface information into a deep learning-based recognition framework by explicitly labeling geometric outliers and subsurface boundaries. For this purpose, a synthetic dataset is created that morphs nominal surface models to resemble the macroscopic surface pattern of physical components. For the detection of regular geometry primitives, a 3D-CNN is used to analyze the voxelized components based on signed distance field data. This explicit labeling approach enables surface fitting to derive suitable shape features that fulfill the underlying surface constraints.

Many immersive approaches for design activities show a great potential for their specific use cases, but still overall usage of extended reality technology in product developers day-to-day work is little. The user's workflow between classical desktop work environment and its immersive counterpart is interrupted by both a data gap and an interface gap. The three-dimensional product data usually needs preparation and the user has to physically change the interface in use. The hybrid augmented reality computer workstation aims to close these gaps. A hologram of the current model is visualized next to the screen in reach of the user for intuitive inspection and spatial interactions. In this paper we present use cases for this novel immersive workstation in the CAD workflow. An explorative user study of the typical product designer`s workflow reveals the most common activities. Guided by those, eight uses cases are formulated and classified into fundamental, drafting, and modelling CAD tasks. These cases include novel hybrid augmented reality interactions derived from literature, which are assessed with respect to their applicability.

Topology Optimization (TO) is an established method for the development of high strength and lightweight structural components. However, its results have to be geometrically revised to obtain a computational model that meets product development requirements. Design has to be in accordance with manufacturing constraints. Geometry reconstruction therefore still is a typically manual and tedious task, but increasingly supported by computational and automated approaches. In this paper, the inclusion of a casting constraint in an automated Medial Axis based reconstruction method is presented. Since the Medial Axis provides cross-section values by the computation of maximally inscribed spheres, this information is used for geometry reconstruction and even further for the purposeful adaption of the cross-section to match Heuver's circle method. Thereby, the directed solidification of molten material is considered. With a predefined feeder position, the demonstrator of a suspension control arm is used to show the application of the method. Resulting CAD-models are also structurally evaluated for their stiffness characteristics.

Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing systems, which can be increasingly used to substitute traditional manufacturing technologies. The objective of this paper is to investigate the application of image processing for classifying MPs in an unsupervised approach. To this scope, k-means and hierarchical clustering algorithms are applied to an unlabelled image dataset. The input dataset is constructed from freely accessible web databases and consists of twenty randomly selected CAD models and corresponding images of machine elements: 35% additively manufactured parts and 65% manufactured with traditional manufacturing technologies. The input images are pre-processed to have the same colour and size. The k-means and hierarchical clustering algorithms reported 65% and 60% accuracy, respectively. The algorithms show comparable performance, however, the k-means algorithm failed to predict the correct subdivisions. The research shows promising potential for MP classification and image processing applications.

Digitalisation is making significant inroads into society at the same time as the general commercial trend is to able to personalise the product one acquires. The field of digital product representation, and the techniques for adopting a particular product in accordance with the customer's expectations, have become very important corporate assets. From a company's perspective these assets can be leveraged both for internal efficiency and also for different types of external customer interactions. In this article, the standpoint is that product geometry forms the foundation for digital product representation. It is from this perspective that the geometrical ecosystem comes into focus. Geometry creation and geometry consumption, in combination with geometrical configuration management, are high-value areas that must be mastered. A research-based 20-year industrial perspective building up such capabilities serves as an example. The article concludes with a forward-looking perspective on potential areas for continued exploration on this journey.

Cloud computing has had an increasing influence on engineering and design. A hallmark of sites such as Github is the promise of rapid iteration and real-time collaboration. Recently, cloud collaborative software has migrated into the realm of physical product design, with computer-aided design (CAD) software platforms such as PTC's Onshape. In this research, we suppose that the effect of cloud collaborative software is multi-faceted; that this type of tool affords a number of new capabilities and behaviours for design individuals and teams. We develop a framework on how to contextualize the changes to design tasks afforded by the unique attributes of these cloud-based, collaborative design tools. We find evidence in our research of design engineers leveraging many aspects of the framework, particularly in learning and engagement with their team, and with resources available from communities of users. However, we find that real-world design engineers are not yet utilizing the full capability of synchronous cloud-platforms with respect to real-time synchronous design iteration within teams or communities.

There are three approaches to studying designers – through their cognitive profile, design behaviors, and design artifacts (e.g., quality). However, past work has rarely considered all three data domains together. Here we introduce and describe a framework for a comprehensive approach to engineering design, and discuss how the insights may benefit engineering design research and education. To demonstrate the proposed framework, we conducted an empirical study with a solar energy system design problem. Forty-six engineering students engaged in a week-long computer-aided design challenge that assessed their design behavior and artifacts, and completed a set of psychological tests to measure cognitive competencies. Using a machine learning approach consisting of k-means, hierarchical, and spectral clustering, designers were grouped by similarities on the psychological tests. Significant differences were revealed between designer groups in their sequential design behavior, suggesting that a designer's cognitive profile is related to how they engage in the design process.

Vehicle doors have barely changed in recent decades, and nor has the car. Since autonomous driving will lead to changes in vehicles and how they are used, their doors will also have to be rethought. In the project UNICARagil, researchers from several universities in Germany design and build four prototypes of driverless and autonomous vehicles, which are developed based on a new and modular architecture. As part of this, we developed a concept including a prototype of an automated door system. In this paper, we present our concept development process adapted for door systems of autonomous vehicles. Based on the vehicle concept development process, it should help researchers and engineers to select and design new door concepts in an early phase. At the end, by means of an example, we present the prototype of our door concept as well as a boarding user study we carried out. This study helps evaluate and improve the boarding comfort of future door concepts.