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The digitalization of manufacturing processes has been driven by the evolution of Computer-Aided Design (CAD) software over the past half-century, transitioning from 2D, paper-based drawings to 3D, model-centric environments. Central to this transformation is the concept of the digital thread, which facilitates interconnected and seamless information flows across the product lifecycle. Realizing this vision from an operational perspective requires a robust information carrier capable of storing and transmitting essential information through a consistent data structure. Model-Based Definition (MBD) addresses this need by embedding semantic information directly into 3D CAD models, thereby supporting streamlined information exchange and aiming to replace traditional 2D documentation in Model-Based Enterprise (MBE) environments. Given the prominence and transformative potential of MBD, this study presents a systematic literature review covering two decades of fragmented research across diverse manufacturing processes and domains, offering a structured reference to unify prior efforts and support future implementation. The analysis of 132 systematically selected high-quality references from Scopus, Web of Science, leading software vendors, and national and international standards ensures a comprehensive and up-to-date synthesis. By consolidating key findings, this review delivers a state-of-the-art overview and outlines a roadmap for adoption in modern intelligent manufacturing systems. Particular attention is given to unmapped areas, laying a solid foundation for future research and standardization efforts, ultimately unveiling a potential game changer for the digital thread in manufacturing across sectors, organizational scales, and geographic regions.

Machine-readable semantic information, such as simulation settings, inspection configurations, machine parameters, process outcomes/logs, is central to digital manufacturing, enabling automation, interoperability, data traceability, and adaptive reconfiguration across the product lifecycle. When anchored to specific geometric entities, these semantic structures form the backbone of Model-Based Definition (MBD), a data engineering architecture in which the CAD model acts as the single source of truth to realize the digital thread. Within this architecture, both rule-based and machine-learning pipelines can orchestrate simulation, optimization, and control tasks using shared, semantically enriched models. Despite this potential, most engineering file formats either lack expressive, machine-readable semantics or encode them in ways that are format-specific, difficult to adapt to different needs, and hard to reuse. This letter presents a manifesto for the eXtended Formats (X-Formats) family, a lightweight stand-off markup approach that links structured annotations to engineering files without modifying the original source. Semantics can be defined independently of the underlying syntax (e.g., STL, B-rep, STEP, and DXF for CAD; PNG, JPEG, GIF, and SVG for images; MP4 and MOV for videos; GeoTIFF for geospatial data) and are anchored to file-specific entities that can be detected by external feature-extraction mechanisms. X-Formats structure semantics in a three-level hierarchy composed of property, layer, and schema, which is fundamental for supporting modular dataset design and allowing rapid customization across domains by replacing only the geometry-descriptor block. This letter introduces the core architectural principles, anchoring strategy, supporting tooling, and broader implications of this novel, product-centric, approach to engineering data management.

Configure-to-Order (CTO) assembly systems are becoming pivotal in meeting the rising demand for mass customization. However, enabling rapid and cost-effective system reconfiguration remains a major challenge in manufacturing. Despite the flexibility of general-purpose machines, fixture design introduces significant bottlenecks, often requiring extensive manual effort to access, organize, and interpret fragmented or incomplete data. This leads to inefficient design pipelines that can compromise product functionality and result in costly redesign iterations, even when using advanced Computer-Aided Fixture Design (CAFD) tools. Although the Model-Based Definition (MBD) paradigm offers a promising response to these information-related challenges, its adoption within the CAFD domain remains limited. This work aims to support the broader adoption of MBD in this field through a multi-level contribution. First, a structured literature review identifies the essential semantic information required in an MBD dataset tailored to CAFD. Second, this content is structured in a machine-readable form using the ISO standard STEP AP242. Third, an MBD-driven CAFD framework is proposed. Finally, a proof-of-concept CAFD tool is developed by integrating established methodologies with commercial software to automate non-value-added tasks, such as configuring Computer-Aided Technologies (CAx). The results demonstrate the potential of this approach to streamline CAFD processes and establish a fully connected digital thread.

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part.

This paper proposes and implements a novel pipeline for the self-reconfiguration of a flexible, reconfigurable, CAD-based, and autonomous Vision Inspection System (VIS), expanding upon the modular framework theoretically outlined in (Lupi, F., Maffei, A., & Lanzetta, M. (2024). CAD-based Autonomous Vision Inspection Systems. Procedia Computer Science, 232, 2127–2136. https://doi.org/10.1016/J.PROCS.2024.02.033.). The pipeline automates the extraction and processing of inspection features manually incorporated by the designer into the Computer Aided Design (CAD) 3D model during the design stage, in accordance with Model Based Design (MBD) principles, which, in turn, facilitate virtuous approaches such as concurrent engineering and design for (Dfx), ultimately minimizing the time to market. The enriched CAD, containing inspection annotations (textual or dimensional) attached to geometrical entities, serving as the pipeline’s input, can be exported in a neutral file format, adhering to the Standard for Product Data Exchange (STEP) Application Protocol (AP)242, regardless of the modeling software used. The pipeline’s output is a Reconfiguration (ReCo) file, enabling the flexible hardware (e.g., robotic inspection cell) and software components of the VIS to be reconfigured via software (programmable). The main achievements of this work include: (i) demonstrating the feasibility of an end-to-end (i.e., CAD-to-ReCo file) pipeline that integrates the proposed software modules via Application Programming Interfaces (API)s, and (ii) formally defining the ReCo file. Experimental results from a demonstrative implementation enhance the clarity of the paper. The accuracy in defect detection achieved a 96% true positive rate and a 6% false positive rate, resulting in an overall accuracy of 94% and a precision of 88% across 72 quality inspection checks for six different inspection features of two product variants, each tested on six samples.

Automated industrial Visual Inspection Systems (VIS) are typically customized for specific applications, limiting their flexibility. They are characterized by a demanding setup, high capital investments, and significant knowledge barriers. In this paper, we propose an alternative architecture for the visual inspection of 3D printed parts or complex assemblies using a robotic arm equipped with hand-eye sensors and controllable lighting system. The core of the proposed Flexible Vision Inspection System (FVIS) is the self-extraction of 3D text annotations from STandard for the Exchange of Product model (STEP) AP242 files. The system self-selects and parametrizes the most suitable inspection algorithm, including lighting settings. Additionally, it autonomously performs self-localization, self-referencing of physical products, and self-planning of robot inspection path based on CAD information. This framework, characterized by self-X, cost-effective, non-invasive, and plug-and-play architecture has the potential to disrupt the business model of vision inspection, enabling an as-a-service solution aligned with the next generation of flexible manufacturing.