Three-phase induction motors are the primary actuators for converting electrical energy into mechanical energy in the productive sector, constituting key assets due to their widespread use and critical function. Reducing maintenance costs and implementing predictive techniques incentivize the development of systems to identify intrinsic defects. The increasing demand for customization in manufacturing affects maintenance due to fast production line adaptations. This leads to unforeseen failures that compromise reliability. There is a lack of research on detecting and diagnosing faults in induction motors under intermittent drives or varying operating conditions. To fill this gap, the present research proposes a methodology for recommending algorithms to diagnose and detect broken bar defects in three-phase induction motors during transient operation based on a cognitive system. The framework explains and detects fault causality. Using experimental data (current, voltage, vibration), three-phase induction motors were tested under normal conditions, applying various severities of broken bar faults with load torque variations. Features were extracted from each signal, and feature selection algorithms of different mathematical natures were applied. Machine learning models were built, validated, and tested with multicriteria measures. To assess robustness, white noise was inserted into the experimental signals. The Consistency-Based Filter algorithm emerged as the most suitable for feature selection combined with Random Forest and Multilayer Perceptron models. The best results were achieved with up to 80% noise tolerance without compromising predictive capacity for diagnosing defect severity. Features following a Gaussian distribution showed better predictive capacity, resulting in a reliable framework for fault diagnosis in induction motors.

The aerospace manufacturing industry is characterized by high product complexity, long life cycles, and frequent project changes that increase costs, lead times, and environmental impacts. Traditional manufacturing systems struggle to integrate heterogeneous automation technologies and manage highly customized production environments. This study aims to analyse the state of the art in Cyber-Physical Production Systems (CPPS), Reconfigurable Manufacturing Systems (RMS), and Semantic Interoperability (SI), and to propose a knowledge- and data-driven framework to support intelligent aerospace manufacturing.A systematic literature review combined with a bibliometric analysis was conducted to identify relevant approaches, technologies, and research gaps related to CPPS, RMS, and semantic interoperability in aerospace manufacturing. An in-depth content analysis of selected studies was then performed to synthesise their contributions and limitations, serving as the basis for the conceptual framework design.The study proposes the Knowledge and Data-Driven Intelligent Cyber-Physical Production System (KDD-iCPPS) framework. The framework enables semantic interoperability and seamless information exchange across design and manufacturing domains. It supports key manufacturing activities such as process planning, cost estimation, and quality assurance, while capturing both explicit and implicit domain knowledge through formal ontologies and data-driven mechanisms. The KDD-iCPPS framework addresses key limitations of conventional manufacturing systems by integrating knowledge-based and data-driven capabilities within a CPPS architecture. Future work will focus on industrial validation through experimental case studies and comparative assessments with existing practices to refine and extend the framework’s applicability in the aerospace sector.

Industries must remain agile to respond to evolving market behaviours driven by fluctuating consumer demands, technological advances, and global economic uncertainties. A key challenge is integrating real-time data for agile decision-making that balances customisation, efficiency, cost, and sustainability. This paper proposes an intelligent and integrated manufacturing system that leverages sensors, artificial intelligence, and ontological knowledge layers to enable real-time reconfiguration of production parameters, specifically in CNC turning operations. The framework is based on a revised ISA-95 standard and was validated through a case study involving the turning of ABNT 8640 steel. Sensor data, including vibration and temperature, were integrated into an ontological model that interfaced with the ERP system. This integration enabled the automatic adjustment of cutting parameters in response to production contexts, including delayed orders and machine load, through an XML-based connection between the ontology and the machines’ PLCs. The model demonstrates the potential of combining heterogeneous data and computational intelligence to enhance industrial adaptability and efficiency. The revised ISA-95 standard supports system scalability and interoperability. Nonetheless, critical challenges remain, such as ensuring data reliability, achieving full system integration, and capturing tacit knowledge from experienced operators to fully realise the system’s capabilities.

Collaborative robots (COBOT) play a key role in Industry 4.0 by enabling safe human-machine interaction and flexible task execution. However, their integration adds complexity to the robot selection process, which now must consider criteria such as safety, adaptability, and collaboration, in addition to traditional technical factors. This paper presents a comprehensive review of multi-criteria decision-making (MCDM) methods and selection criteria for industrial and collaborative robots. Based on this review, a structured decision support framework is proposed, combining the PROMETHEE method to classify alternatives with the Mudge technique to assign weights to subjective criteria. The framework enables agile, transparent, and consistent robot selection without requiring a great deal of expertise in MCDM techniques. The objective is to reduce planning time, improve decision quality, and increase competitiveness in smart manufacturing environments. Future work may enhance the framework by incorporating real-world robot datasets and expanding the evaluation criteria to reflect the evolving demands of the industrial sector.

The progress of smart manufacturing systems demands the integration of multiple artificial intelligence (AI) paradigms to support the decision-making process in increasingly complex and dynamic environments. Although data-driven approaches excel in pattern discovery and process optimization, they often lack interpretability and the ability to incorporate specialized knowledge. Conversely, symbolic approaches provide transparency and explainability but may present limitations in adapting to unforeseen scenarios. This article presents toward a hybrid semantic framework for manufacturing intelligence that systematically integrates data-driven and symbolic AI approaches through a three-layer hierarchical structure: physical layer, digital layer, and cognitive layer. The framework incorporates an intelligence orchestration layer and a semantic alignment layer to enable semantic communication between heterogeneous components and harmonize knowledge from diverse sources. The experimental evaluation focuses on aerospace sheet metal manufacturing parts, demonstrating the integration between automated feature extraction and specialized knowledge formalization through ontologies. A conversational interface powered by large language models enables natural language interaction with specialized ontologies, providing automated inference capabilities and explainable decision-making. The results validate the feasibility of combining automated characteristic extraction with semantic knowledge representation, establishing a foundation for hybrid AI systems in manufacturing environments. The proposed framework addresses critical challenges in cyber-physical production systems by enabling adaptability, explainability, and real-time decision-making while maintaining semantic consistency between empirical and formal knowledge sources.

The aerospace manufacturing industry faces substantial complexity, particularly in the aircraft manufacturing process, which requires integrating advanced components and systems with diverse geometries and materials. This environment necessitates robust information systems to manage information exchange across the product life cycle and reduce disruptions during project development. Traditional manufacturing systems struggle to integrate diverse automation technologies and maintain efficiency in highly customised and technologically complex aerospace production. Interferences caused by project changes can lead to increased costs, longer time commitments, and greater environmental impacts. Based on this context, this research proposes a multi-layer knowledge and data-driven integrated framework to seamlessly integrate digital and physical technologies, facilitating communication and transparency across the complex manufacturing process. It supports manufacturing tasks such as process planning, cost estimation, and quality assurance, ensuring the capture and utilisation of explicit and implicit knowledge. Implementing the multi-layer knowledge and data-driven integrated framework enhances manufacturing efficiency, reduces costs, and improves product quality in the aerospace industry. An experimental case demonstrated the ability to store data and knowledge in a structured way, thereby generating different manufacturing plans, supporting process decision-making, and improving the 72.1% efficiency of plan generation with human validation. Future research will focus on validating the manufacturing plan generated from existing manual process plans, enabling optimisation of manufacturing according to the most suitable plan presented, aiming to refine it further and expand its applicability in the aerospace sector.

Integrating Computer-Aided Design (CAD) product data with shop-floor execution is frequently constrained by the lack of a traceable path linking geometric features, process-planning rules, and machine capabilities. This study proposes an information and knowledge orchestration framework that converts heterogeneous CAD-derived data into an auditable manufacturing-planning package. The architecture systematically normalizes feature descriptions, maps them to semantic ontologies, applies formal reasoning, and routes exceptions to expert validation. Validated through a motor-shaft remanufacturing case, the system demonstrated absolute traceability: all 128 cutting-parameter records maintained explicit rule and ontology provenance. The reasoning engine successfully fired 24 of 36 rules and appropriately withheld machine-incompatible steps from downstream execution. Additionally, an ablation study revealed that omitting a categorical admissibility gate causes incompatible feature families to merge erroneously. This contribution provides a measured and reproducible demonstration of CAD-to-manufacturing integration, ensuring that operational decisions are grounded in explicit knowledge rather than procedural defaults.

This paper presents a human‑centric Product Lifecycle Management (PLM) framework aligned with Industry 5.0 that brings together Digital Twins (DTs), Smart Manufacturing, and Generative AI (GenAI) under a PLM backbone. The framework is designed around three pillars—human‑centricity, sustainability, and resilience—and incorporates explainable AI (XAI) and lifecycle assessment to support transparent, accountable decisions. A focused systematic literature review motivates the architecture and identifies gaps in explainability, interoperability, and end‑to‑end lifecycle coverage. The study demonstrates feasibility with a NASA milling case study, integrating sensor data, DT‑based emulation, and a predictive model within PLM workflows to achieve traceability and operator‑in‑the‑loop validation. Results show modest predictive performance, highlighting the need for feature engineering and model generalization; the paper outlines concrete improvement paths and discuss industrial adoption barriers (skills, integration, and governance). Contributions are: (i) a precise definition of PLM 5.0 and its dependencies, (ii) a modular framework integrating DT/GenAI/XAI with PLM and sustainability assessment, and (iii) a reproducible case configuration that maps each operational step to the framework. The paper concludes with open challenges in interoperability, security, and ethical governance and proposes evaluation and benchmarking directions.

This article presents a semi-systematic literature review of innovation helix models (triple, quadruple, and quintuple), examining their conceptual evolution, empirical applications, and emerging trends. The mixed-methods approach combining bibliometric mapping and thematic analysis identified five core domains: actor interaction, framework development, entrepreneurship, efficiency and effectiveness, and historical perspectives. The findings delineate the state of the art, while simultaneously exposing underlying divergences and conceptual lacunae. These gaps are articulated through a set of prospective research questions, systematically organized by model perspectives, actors, and salient characteristics. Findings confirm the predominance of the triple helix, while highlighting theoretical limitations that have motivated the development of higher order models. Although the quadruple and quintuple helices introduce societal and ecological dimensions, their operationalization remains limited. On this basis, the study advances a structured synthesis designed not only to orient subsequent scholarly inquiry but also to enhance the practical deployment of innovation helix models in engineering and technology management.

The emergence of Industry 5.0 emphasizes human-centric manufacturing and increases the demand for product personalization, which introduces additional complexity to manual assembly processes. These challenges often require operators to memorize intricate sequences for various products, increasing the likelihood of human error and affecting production quality. In response to this, we present a modular framework developed in Python that leverages computer vision algorithms to monitor each step of the manual assembly process in real-time. The system features a low-code interface, allowing users to digitize and configure workflows without deep technical knowledge. Unlike hardware-dependent solutions, our approach offers a flexible, cost-effective alternative that integrates with existing production environments. A preliminary case study conducted in a controlled setting demonstrated the framework's ability to detect errors during specific assembly tasks. The results indicate its potential to enhance quality control and support operator performance. Future work will focus on applying the framework to complete the assembly process and incorporating augmented reality to provide detailed, step-by-step operator guidance, further reducing errors and improving process efficiency.

The shift to Industry 5.0 emphasizes human-machine collaboration, personalization, sustainability, and worker support. This paper explores integrating Digital Twins (DT) and Cyber-Physical Production Systems (CPPS) within a human-centric Smart Manufacturing framework. Digital Twins offer real-time prediction, simulation, and optimization, enhancing production efficiency and adaptability. CPPS merges computational and physical processes, supporting human-machine collaboration and improved decision-making. The main objective is to enhance sustainability, efficiency, and human-centricity in data-driven manufacturing. This research reviews current technologies, proposes a novel framework integrating DT and CPPS, and assesses implementing a Smart Product (SP) within a Smart Factory (SF) scenario. Key innovations include designing and integrating SPs, developing smart manufacturing processes, and applying DT for enhanced SP functionality. The findings demonstrate improvements in production efficiency, customization, human-machine collaboration, and sustainability, aligning with Industry 5.0 principles. The study concludes with recommendations for future research to integrate ergonomics, cybersecurity, and ethical considerations in smart manufacturing.

Modern Ontology-Based Engineering (OBE) systems, built over the foundations of Knowledge-Based Engineering (KBE), leverage ontology-based technologies and the semantic web to capture and formalise knowledge holistically. This comprehensive knowledge repository can subsequently feed design and decision support tools with robust generative features. However, the integration and reuse of knowledge poses challenges for which it is necessary to develop appropriate methods and tools capable of handling diverse types of knowledge from both human experience and databases. The objective of this paper is to discuss the current limitations of OBE systems to address knowledge integration and reuse, based on the research and experiences of the authors in the aerospace manufacturing industry. This aims to provide a set of Research Questions (RQ) and the author's position to them, to serve as future research work and topics of discussion for the scientific community.

Manufacturing custom parts in several industry sectors has become challenging, especially in the aerospace industry. This factor is due to the complexity of the product, low manufacturing levels, and a competitive market in which it seeks to reduce the consumption of resources and optimise processes. Traditionally, the process of generating and analysing manufacturing plans is manual, in which a team of engineers analyses piece by piece and generates manufacturing plans for each customised part. In a complex product such as an aircraft, this number can exceed 500,000 parts. Disruptions due to project changes can lead to increased expenses, time investments, and environmental impacts. Based on this context, this paper discusses Ontology-Based Engineering (OBE) Systems for Knowledge-Driven Manufacturing using concepts such as Semantic Interoperability (SI), Automated Feature Recognition (AFR), Models for Manufacturing (MfM) and Large Language Model (LLM) to solve the problem of integration of data, information and knowledge at the stages of the production process. The proposed framework enables the seamless integration of digital and physical technologies, facilitating communication and transparency across the complex manufacturing process. Implementing the OBE system enhances manufacturing efficiency, reduces costs, and improves product quality in the aerospace industry. An experimental case showed the ability to store data and knowledge in a structured way and thus generate different manufacturing plans, supporting process decision-making. Future work will focus on validating the manufacturing plans generated by the system and comparing them with traditionally generated ones, with the objective of application in product development.

The evolution of aerospace products, with an increasing number of components, more demanding delivery times, and reduced costs, is leading companies to look for integrated management systems. The future of the aerospace industry is linked to the development of new methodologies, such as Model-Based Engineering (MBE). In the last two decades, the use of semantic structures, such as ontological models, to improve information systems has proven to be very effective in the industry. Models for Manufacturing (MfM) is a recent OBE (Ontology-Based Engineering) methodology for defining complex manufacturing systems' data, functions, and behaviours using graphical models. It is based on agnostic tools and is structured in an architecture of three independent layers. In this paper, the MfM methodology is applied to describe the manufacturing process of Aerospace Sheet Metal (ASM) parts. The implementation is described through a practical example for a part manufactured with one of the most used sheet metal forming processes in the aerospace industry: hydroforming. A study analysed 26 typical sheet metal parts, finding bending and hydroforming to be the main processes. The model aims to characterize the forming process, i.e., to recognize the most appropriate manufacturing process, and subsequently to define the necessary operations and resources, based on the part's Computer Aided-Design model. In addition, the model is complemented by other existing MfM tools for sheet metal forming.

Abstract Paper aims This study investigates integrating digital transformation, human factors, business process management, and emerging technologies to improve organisational efficiency and employee well-being. The research aims to develop a conceptual model that optimises digital processes while reducing the cognitive load on employees. Originality The research fills a gap in the literature by emphasising the intersection of human factors and digital transformation. It introduces a human-centric approach that balances operational efficiency with employee well-being, which has been underexplored in previous studies. Research method A systematic literature review was conducted using Scopus and Web of Science databases to identify relevant studies. Content analysis was used to extract criteria for each domain, and Structural Equation Modelling (SEM) was applied to analyse complex relationships between digital transformation and human factors. Main findings The results indicate that integrating digital tools into organisational processes optimises workflows and decision-making while mitigating cognitive overload. The proposed model prioritises employee engagement, usability, and well-being alongside technological advancement. Implications for theory and practice This study contributes to the theoretical understanding of digital transformation by integrating human factors. The findings provide a structured pathway for organisations to enhance operational efficiency while safeguarding employee well-being, offering a balanced approach to digitalisation that can be applied in real-world scenarios.

The development of new products is attracting increasing attention from industry and academia due, among other factors, to the reduced product development process, market differentiation, greater product complexity, rapid changes in technological knowledge and increased customer sophistication. This scenario requires an efficient cost estimation process and quick decision-making to ensure the efficiency of the production process. Consequently, many approaches have been suggested for use in predicting product costs. However, each has its issues and limitations that affect the effectiveness of the final solution. This paper aims to survey approaches related to the cost estimation process in aerospace industry processes and their respective niches, highlighting the gaps and limitations present to identify the appropriate contexts for applying each. It compiles the latest work on cost estimation approaches, artificial neural networks, and the semantic web in the aerospace industry, using the knowledge acquired for better and comprehensive future implementation.

Industry 4.0 (I4.0) integrates technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and robotics to create interconnected, intelligent, and autonomous production environments. This transformation drives innovation and competitiveness but poses challenges, including system integrations, investments, and workforce upskilling. This work explores the potential of Generative Artificial Intelligence (GAI) as an accelerator for I4.0 adoption considering Industry 5.0 (I5.0) requirements, using I4.0 reference architectures and the four key dimensions: Smart Manufacturing (SM), Smart Working (SW), Smart Products and Services (SPS), and Smart Supply Chain (SSC) to guide the analysis. The literature indicates that GAI has been applied in various domains, but there is a gap in comprehensive research to the industrial context. GAI's potential contributions to SM and SW include generating insights, optimizing operations through Digital Twins (DT), predictive maintenance, and enhancing human-machine collaboration, aligning with the I5.0 concept on personalization and human-centric technology solutions. In SPS and SSC, GAI aids in product development, mass customization, production simulation, and inventory control, providing real-time support and improving supply chain efficiency. The paper concludes that GAI holds significant promise for enhancing I4.0 and I5.0. But further research is needed to learn its impact at each organizational level, develop best practices, and address data quality and integration challenges. Future work will involve a systematic literature review to deepen insights into the integration of GAI with I4.0 and I5.0, with a particular focus on the role of DT and their potential to create more connected and cognitive industrial solutions.

Digital transformation (DX) has driven significant company changes, restructuring products, processes, and services by integrating emerging technologies across all organisational levels. This change enhances workflows, decision-making, and operational efficiency, fostering innovation and competitive advantage. This research analyses how effective technology implementation, employee engagement, usability awareness, and strategic management practices can improve organisational processes. By analysing interconnections among DX, human factors, business processes, and emerging technologies, the research employs a systematic literature review and Structural Equation Modelling (SEM) to identify critical factors for success. The findings highlight that digital tools streamline operations and support data-driven decisions, reducing cognitive overload through user-centred design. This research proposes a Human-Oriented Process Enhancement (H.O.P.E.) model that integrates DX with human-centric factors to guide digital technology applications and improve organisational performance. The practical application of this model was carried out as part of a litigation management project in an automotive supplier manufacturing plant specialising in advanced solutions across seating, interiors, and clean mobility technologies. The project sought to streamline legal processes, enhance compliance, and mitigate risks through structured litigation management. In conclusion, the digital maturity and human factors (DMHF) index, the outcome of the H.O.P.E. model, has proven to be a comprehensive tool for aligning DX adoption with organisational strategic goals considering human-centric factors. Future research will focus on customising the index for industry-specific needs, particularly ergonomics, to ensure organisations achieve sustainable growth in a digital setting.

The manufacturing of an aircraft is a complex process, requiring exceptional precision and adherence to strict quality standards for every component. This process demands continuous sharing of information and knowledge, as any errors occurring during these stages may lead to problems that affect the entire product lifecycle. In this context, anomaly detection becomes a crucial domain of study, particularly in the early stages of product lifecycle management (PLM). Collecting and processing data in real-time facilitates early failure prediction in production processes, aids in decision-making for reworking defective parts, and enhances overall process optimisation. This paper aims to address the problem of lack of standardisation in the product design and manufacturing of parts by proposing an intelligent system to detect and diagnose anomalies in the product design and manufacturing of aerospace sheet metal parts. The proposed approach uses automated feature recognition (AFR) to classify the topological elements of the boundary model and recognise features through semantic rules. Then, machine learning techniques for anomaly detection, such as k-nearest neighbours (KNN), are applied to identify anomalies in the test data. Finally, ontologies are integrated as a method of knowledge formalisation and representation to assist the decision-making process for reworking the anomaly part, suggesting productive processes and corrective tools for anomaly correction. As a result, the approach can identify anomalies in 3D model parts based on their project requirements and geometric features. It allows anomaly visualisation and offers support in decision-making during manufacturing, determining corrective actions, and thus optimising the manufacturing process.

New product development (NPD) is a collaborative and cognitive process for a new product or service development. NPD must meet customers' needs while achieving business objectives such as growth, profitability, and competitive advantage. The stages of NPD include formatting the opportunity, identification, conception and generation of ideas, design, development, and implementation. Knowledge management encompasses fostering the creation and exchange of ideas among multidisciplinary teams, retaining and preserving accumulated technical knowledge, and capitalising on continuous learning opportunities. For this purpose, tools, platforms, and the application of management practices are necessary to accelerate the development of new products. Therefore, this paper explores an analysis dealing with the intersection between collaborative engineering, knowledge management, and digital technologies for new product development. Initially, collaborative engineering issues are discussed to identify the requirements. Then, how collaborative engineering interconnects with knowledge management will be examined, revealing synergies that enhance knowledge creation, sharing, and practical application throughout the product life cycle. The paper will also address the role of emerging technologies in this dynamic context and their transformative influence on how product development teams collaborate and manage knowledge. Therefore, the article will outline how this convergence of these three elements can accelerate the conceptual development stage of new products.