The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems.

This work introduces a novel algorithm for the reconstruction of rolling stocks from a sequence of images. The research aims at producing an accurate and wide image model that can be used as a Digital Twin (DT) for diagnosis, fault prediction, maintenance, and other monitoring operations. When observing large surfaces with nearly constant textures, metallic reflections, and repetitive patterns, motion estimation algorithms based on whole image error minimization and feature pairing with Random Sampling and Consensus (RANSAC) or Least Median of Squares (LMedS) fail to provide appropriate associations. To overcome such an issue, we propose a custom Kalman Filter (KF) modified by adding multiple input-noise sources represented as a Gaussian mixture distribution (GM), and specific algorithms to select appropriate data and variance to use for state prediction and correction. The proposed algorithm has been tested on images of train vessels, having a high number of windows, and large metallic paintings with constant or repetitive patterns. The approach here presented showed to be robust in the presence of high environmental disturbances and a reduced number of features. A large set of rolling stocks has been collected during a six months campaign. The set was employed to demonstrate the validity of the proposed algorithm by comparing the reconstructed twin versus known data. The system showed an overall accuracy in length estimation above 99%.

Digital twin technology has become a driving force in the transformation of the manufacturing industry, playing a crucial role in optimizing processes, increasing productivity, and enhancing product quality. A digital twin (DT) is a digital representation of a physical entity or process, modeled to improve decision-making in a safe and cost-efficient environment. Digital twins (DTs) cover a range of problems in different domains at different phases in the lifecycle of a product or process. DTs have gained momentum due to their seamless integration with technologies such as IoT, machine learning algorithms, and analytics solutions. DTs can have different scopes in the manufacturing domain, including process level, system level, asset level, and component level. This work presents the knowlEdge Digital Twin Framework (DTF), a toolkit that comprises a set of tools to create specific instances of DTs in the manufacturing process. This chapter explains how the DTF relates to other standards, such as ISO 23247. This chapter also presents the implementation done for a dairy company.

Industrial Internet of Things Platforms enable the use of available data to improve production and business processes. However, the data exchange and provisioning between the data sources and platform services remain a challenge as such platforms are usually vendor specific, proprietary, and associated with specific IoT hardware. Therefore, we propose a detailed description of an open-source software-based solution, Thing to Service Matching (TSMatch) which performs fine-grained semantic matching between available IoT data and services. Moreover, the paper presents the actual implementation of the proposed solution in 2 different Aerospace production environments, and a performance evaluation in a testbed environment.

The distribution of services across heterogeneous edge and cloud infrastructures in Beyond 5G (B5G) and Sixth-Generation (6G) networks increases operational complexity, while existing MANagement and Orchestration (MANO) solutions remain single-domain and lack unified telemetry or autonomous cross-domain decision mechanisms. Prior work proposes cross-domain orchestration concepts, but most validations rely on simulation and overlook interoperability issues, non-stationary latency, and inconsistent Key Performance Indicator (KPI) models in real Network Function Virtualization Infrastructure (NFVI) deployments. This paper introduces a modular, technology-agnostic Zero-touch Network and Service Management (ZSM) framework that provides unified NFVI abstraction and a multi-criteria decision engine that jointly considers latency, compute load, and energy consumption for autonomous service placement, scaling, and dynamic resource provisioning. Evaluation in a real-world deployment across geographically distributed testbeds demonstrates that our ZSM framework mitigates latency spikes by up to 96%, reduces overloaded-node latency by 36%, and maintains stable performance under variable load. These results confirm the practicality and effectiveness of zero-touch, multi-domain orchestration for future 6G compute-continuum environments.

Manufacturing as a Service (MaaS) refers to a set of tools and processes that can assist the shared use of networked production facilities. In the core of this paradigm is a vision where manufacturing environments shall profit from an online set of tools and services that can be tailored to the requirements coming from the different manufacturers, thus reaching a higher degree of flexibility and an increase in production efficiency. 320In the context of MaaS, the Horizon 2020 European Connected Factory Platform for Agile Manufacturing (EFPF) provides an operational instantiation of a large-scale MaaS across Europe, integrating a diverse set of services such as data analytics, factory connectors, and an interoperable Data Spine to proportionate a high level of automation across different shop-floors. This chapter explains the EFPF MaaS concept, going over its architectural design, and giving insight into how developers and SMEs can profit from the EFPF open-source SDK to generate new products, and how these products can be integrated into the EFPF broad marketplace. The chapter gives insight also to the different pilots developed in the project, explaining challenges faced, and proposed solutions. <!----> <!----> <!----> <!---->