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Part segmentation of industrial 3D models is often limited by the lack of sufficiently large and consistently labeled training datasets. This study proposes a workflow for generating synthetic segmentation datasets from robust parametric computer-aided design (CAD) models and evaluates its applicability on a dental abutment case. The workflow includes the definition of a modeling strategy, creation of a robust parametric CAD model, automated generation of valid geometry variants, and preparation of labeled training data for point-cloud-based segmentation. In the experimental part of the study, a synthetic dataset of segmented dental abutment geometries was generated from the developed parametric CAD model and used to train a PointNeXt-S part-segmentation model. The segmentation performance of the trained model was evaluated on manually labeled real-world abutments. Results show that the segmentation of industrial 3D models improved with increasing synthetic training-set size and further improved when data augmentation was applied. The best-performing augmented model achieved a mean Intersection over Union (IoU) of 89.2% on the real-world validation set, compared with 82.4% without augmentation. The findings indicate that parametric-CAD-based synthetic dataset generation can provide an effective basis for training segmentation models for complex industrial geometries.

Digitalization has reshaped dental laboratory processes through digital tools and artefacts supporting clinician–laboratory collaboration; however, repeated iterations still increase coordination effort and extend delivery times. This study examined how the custom abutment process was executed in a dental laboratory and identified where and why iterations occurred during computer-aided design (CAD) modelling, design verification, and manufacturing preparation. Ten completed orders were selected, and their event log information was analyzed using process mining in Disco, complemented by contextual inquiry with domain practitioners. The analysis reconstructed observed execution from order initiation to delivery and derived a reference representation summarizing the most frequently observed ordering of actions. Across the ten orders analyzed, nine exhibited at least one iteration. Iterations were most frequently observed as returns between CAD modelling and design verification and occurred in four orders, while rescanning occurred in two orders due to insufficient or incompatible initial scan information. Contextual inquiry linked repeated action sequences to changes in digital artefacts and communication exchanges, indicating that iterations were associated with incomplete information or differences in interpretation across roles. The findings show that combining process mining with contextual inquiry enables the identification of iterations and clarifies the conditions under which they emerge.

The growing need for solutions that can support the computer-based and distant assessment of functional models has resulted in ad hoc implementations of various diagramming tools. These tools are typically not intended for the purpose of functional modelling and lack the flexibility and efficiency of the traditional pen-and-paper approach. This paper reports on an experimental study of 42 students who were introduced to functional modelling through either printed vocabulary materials for pen-and-paper modelling or a specifically developed software application for computer-based modelling. All participants received an identical task—model an electric citrus juicer—with a brief description of how one operates and a photograph of a commercial example. The results show no significant difference in their total scores. However, the pen-and-paper group performed significantly better when it came to the selection of appropriate functions and creating plausible function–flow pairs. These results suggest that the current version of the software alters the functional modelling process in which the students typically engage. Also, it has been hypothesised that the software tool’s lack of flexibility and dynamism in presenting the predefined function vocabulary, when compared to the traditional printouts, might result in earlier fixation and the selection of less appropriate functions. On the other hand, the computer-supported approach can be better controlled and is less prone to critical errors, such as disregarding functional modelling conventions.

The conventional prescriptive and descriptive models of design typically decompose the overall design process into elementary processes, such as analysis, synthesis, and evaluation. This study revisits some of the assumptions established by these models and investigates whether they can also be applied for modelling of problem-solution co-evolution patterns that appear during team conceptual design activities. The first set of assumptions concerns the relationship between performing analysis, synthesis, and evaluation and exploring the problem and solution space. The second set concerns the dominant sequences of analysis, synthesis, and evaluation, whereas the third set concerns the nature of transitions between the problem and solution space. The assumptions were empirically tested as part of a protocol analysis study of team ideation and concept review activities. Besides revealing inconsistencies in how analysis, synthesis, and evaluation are defined and interpreted across the literature, the study demonstrates co-evolution patterns, which cannot be described by the conventional models. It highlights the important role of analysis-synthesis cycles during both divergent and convergent activities, which is co-evolution and refinement, respectively. The findings are summarised in the form of a model of the increase in the number of new problem and solution entities as the conceptual design phase progresses, with implications for both design research and design education.

The article describes an attempt to address the automatized evaluation of student three-dimensional (3D) computer-aided design (CAD) models. The driving idea was conceptualized under the restraints of the COVID pandemic, driven by the problem of evaluating a large number of student 3D CAD models. The described computer solution can be implemented using any CAD computer application that supports customization. Test cases showed that the proposed solution was valid and could be used to evaluate many students’ 3D CAD models. The computer solution can also be used to help students to better understand how to create a 3D CAD model, thereby complying with the requirements of particular teachers.

This paper presents a characterization approach for analysing geometric variability in industrial 3D model datasets to support the preparation of synthetic datasets for machine-learning applications. By implementing pairwise Hausdorff distances and manifold-based embedding techniques, the study identifies variability ranges required for generating representative synthetic data and demonstrates how targeted augmentation can effectively reproduce real data’s variability, ultimately leading to more reliable and robust NN model performance.

This study examines how ChatGPT support influences verbal communication in synchronous collaborative CAD activities. Using a verbal protocol analysis of teams solving an embodiment design task, the results show that ChatGPT-supported teams communicated less, devoted less verbal communication to problem- and analysis-related communication, and shifted toward process and solution synthesis, indicating a shortened design co-evolution cycle in which teams move more quickly toward generating solutions. Future work should integrate these findings with broader teamwork and taskwork analyses.

This study investigates how clinicians and technicians describe the preparation and use of CAD models in collaborative design sessions for mass personalised products. Semi-structured interviews with ten professionals were analysed using thematic analysis. The findings revealed three themes: the input data required before modelling, the additional information that supports interaction with the CAD model, and the role-specific ways in which contributors evaluate it. These insights guided the development of an initial parametric CAD model intended to support future collaborative work.

This paper examines the potential of immersive virtual reality technology for design education. A quasi-experimental study has been conducted with 40 students of different expertise levels. The students analysed a design representation using one of the two visualisation technologies: immersive virtual reality (IVR) and non-immersive virtual reality (nIVR). The results show that the expertise in the used technology and the expertise in the design domain significantly affect design understanding. On the other hand, the effect of contextual expertise was not found significant. Spatial ability affected design understanding in nIVR but not in the IVR. Visualisation technology did not have an overall effect on understanding, but IVR helped students with lower expertise to understand specific aspects of a design better (e.g. rotation-based mechanisms). The study suggests that researchers and educators control the students’ expertise when assessing the effect of technology on design education. Overall, the results support the constructivist learning theory, as IVR can support context-dependent and context-independent understanding.

This study explores the integration of network analysis and CAD/PDM log data to analyze collaboration and activity patterns in a multi-year engineering project. Using logs from a collaborative CAD platform with PDM features, the research examines team interactions and network evolution over time. Key findings reveal that early project stages featured smaller, denser networks, while later stages saw larger, less interconnected structures. Subteam formations were dynamic, with variations in size and number. Individual-level analysis showed that user influence, measured through eigenvector centrality, did not always align with activity volume. This work highlights the potential of CAD/PDM data for understanding collaboration dynamics and lays the groundwork for further studies on team interactions in design processes.