This paper discusses an ontological integration framework in the context of mereotopological formal ontology and CAD systems. Based on a comprehensive literature review of current design knowledge management and formal ontology research works, the authors propose the integration framework that can incorporate design knowledge with CAD systems. The primary role of mereotopology in this research work is the formal representation of design knowledge for the 3D solid assembly models. Most of the existing research works about design ontology present an abstract form and often require significant additional efforts to be integrated into CAD systems. Thus, this work introduces a formal ontology for CAD (OCAD) and macro-parametric approach as a practical way to integrate design knowledge with CAD systems. For the implementation, Protégé, SPARQL, and commercial CAD system are employed. We also propose the enhancement of the developed framework for the future use to aid dynamic modeling.

Loss of design intents and related information throughout the lifecycle of products are common. Capturing design intents of an assembly, which include a temporal (dynamic) stages, is even harder. This research work aims to enhance the spatiotemporal mereotopology (STM) based ontology in tune with the contemporary efforts in this research domain. The main idea with such STM ontology is to capture dynamic design intents and develop an integrated data translation framework from Computer Aided Design (CAD) system to a visualization system. This integration framework is intended to enhance design sharing in a collaborative environment. In this paper, the framework is demonstrated with a simple mechanical assembly that includes rotational, reciprocating and linear motions, directly related to dynamic design intents.

Digital Twin (DTw) technology is a cornerstone of Industry 4.0, enabling real-time monitoring, predictive maintenance, and performance optimization across diverse industries. A key requirement for effective DTw implementation is high geometric fidelity—ensuring the digital model accurately represents the physical counterpart. Fidelity metrics provide a quantitative means to assess this alignment in terms of geometry, behavior, and performance. Among these, 3D shape descriptors play a central role in evaluating geometric fidelity, offering computational tools to measure shape similarity between physical and digital entities. This paper presents a comprehensive review of 3D shape descriptor methods and their applicability to geometric fidelity assessment in DTw systems. We introduce a structured taxonomy encompassing classical, structural, texture-based, and deep learning-based descriptors, and evaluate each in terms of transformation invariance, robustness to noise, computational efficiency, and suitability for various DTw applications. Building upon this analysis, we propose a conceptual fidelity metric that maps descriptor properties to the specific fidelity requirements of different application domains. This metric serves as a foundational framework for shape-based fidelity evaluation and supports the selection of appropriate descriptors based on system needs. Importantly, this work aligns with and contributes to the emerging ISO 30138 standardization initiative by offering a descriptor-driven approach to fidelity assessment. Through this integration of taxonomy, metric design, and standardization insight, this paper provides a roadmap for more consistent, scalable, and interoperable fidelity measurement in digital twin environments—particularly those demanding high precision and reliability.

Over the last decades, noticeable efforts have been made to construct design knowledge during the detailed geometric definition phase systematically. However, physical products exhibit functional behaviors, which explain that they evolve over space and time. Hence, there is a need to extend assembly product knowledge towards the spatiotemporal dimension to provide more realistic knowledge models in assembly design. Systematic semantic knowledge representation via ontology enables designers to understand the anticipated product’s behavior in advance. In this article, Interval Algebra (IA) and Region Connection Calculus (RCC) are investigated to formalize and construct ontological spatiotemporal assembly product motion knowledge. IA is commonly used to represent the temporality between two entities, while RCC is more appropriate to represent the ‘part-to-part’ relationships of two topological spaces. This paper discusses the roles of IA and RCC and presents a case study of a nutcracker assembly model’s behavior. The assembly product motion ontology with the aid of IA and RCC is evaluated using a task-based approach. The evaluation shows the added value of the developed ontology compared to others published in the literature.