Naslov
Analysis of collaborative CAD modelling activities using machine learning methods

Autori
Celjak, Robert

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet strojarstva i brodogradnje

Mjesto
Zagreb

Datum
11.05

Godina
2023

Stranica
96

Mentor
Škec, Stanko

Ključne riječi
CAD modelling ; machine learning ; user archetypes ; clustering ; pattern recognition ; collaboration

Sažetak
Computer-Aided Design (CAD) models are one of the primary artefacts in the design process. They allow designers to develop and collaborate on design within the design workflow. The advent of cloud computing has enhanced collaboration in design by enabling simultaneous real-time work on shared CAD models. To determine the best approaches toward design team composition and users’ modelling practises, it is crucial to identify user archetypes and designers’ sequential patterns across different aspects of the CAD modelling process. This thesis presents the results of the implementation of clustering and pattern recognition machine learning methods on a dataset obtained from a project-based design course. The data was collected in a non-intrusive manner, preprocessed, and organised within an adopted and modified CAD action classification. The data analysis was performed on a dataset which included 14 three-member design teams, which performed 547 357 CAD actions within the design project. The results show that higher-performing teams worked individually while lower-performing worked individually and as a team equally. Lower-performing teams had role overlap among designers, while higher-performing teams had clearly defined roles for part, assembly, and versatile designers. Two team compositions were observed based on the types of CAD actions performed. The first had two or all three members performing similar amounts of creating, editing, and organising classes of CAD actions. The second had a dominant, versatile user leading the team in actions, related to said classes, performed. The pattern recognition algorithm identified common sequential patterns in the modelling processes of users. These results can lead to the identification of user archetypes which can facilitate forming design teams of complementary members. These results provide valuable insights for engineers and educators seeking to understand the sequential patterns of the modelling process and their potential impact on CAD model quality.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

POVEZANOST RADA