Smart Manufacturing Systems (SMSs) have evolved into intelligent, data-driven ecosystems that integrate cyber–physical systems, digital twins, and artificial intelligence to enhance efficiency, sustainability, and resilience. This review synthesises more than 250 recent studies across four domains: manufacturing technologies, systems management, sustainable production, and human–robot collaboration. In process optimisation, hybrid machine learning and genetic algorithms reduce surface roughness in machining by up to 35% and decrease energy use in additive manufacturing by 20–30%. In systems management, digital twins and reinforcement learning enable adaptive scheduling and predictive maintenance, increasing operational flexibility and reducing industrial downtime. Sustainability-oriented research shows that additive manufacturing can cut energy consumption by up to threefold compared with subtractive routes, while aluminium recycling and hot-forming processes lower life-cycle impacts. Furthermore, the integration of ISO 14001, ISO 50001, and ISO 14040 supports consistent environmental and energy performance assessment across sectors. Building on this evidence, the review critically examines recent developments in manufacturing technologies, systems management, sustainable practices, and human–robot collaboration, highlighting emerging paradigms such as explainable AI and human-centric design that strengthen safety, transparency, and resilience. Open challenges and research opportunities are outlined to guide future innovation toward intelligent, adaptive, and sustainable manufacturing systems.

Digital twins (DTs) and immersive extended reality (XR) interfaces offer new opportunities for intuitive, human‐centred interaction in smart manufacturing. However, implementations often lack rigorous validation, quantitative performance analysis and assessment of scalability and robustness, especially in small‐scale or resource‐constrained manufacturing settings. This work proposes a modular metaverse framework integrating ROS2, Unity and Meta Quest devices to develop interactive, bidirectional DTs enhanced with gesture‐based and mixed reality (MR) control. The framework is demonstrated through a lab‐scale case study combining a robotic Wire Arc Additive Manufacturing (WAAM) system and a collaborative robot‐based laser‐cleaning station, showing broad applicability across industrial robotics. To evaluate usability, a preliminary user study with 17 participants was conducted, comparing a standard teach pendant with the proposed XR interface for a tool‐inspection task. Results show an 80% reduction in programming time and significant decreases in perceived workload. Measured end‐to‐end gesture‐to‐action latency ranged from 430 to 450 ms, representing suitable timeframe for high‐level interaction and task initiation. The study provides empirical user‐centred evidence for metaverse‐enabled interaction and discusses scalability, latency and industrial constraints. Aligned with the human‐centric intelligence principles of Industry 5.0, the proposed approach improves accessibility, operator well‐being and adaptability, contributing towards more inclusive and robust smart manufacturing systems.

1. The rapid evolution of Industry 4.0 has introduced cyber-physical systems, automation, and intelligent robotics into manufacturing, transforming production lines into intelligent and interconnec...

In open-loop Wire Arc Directed Energy Deposition (WA-DED), the contact tip–to–workpiece distance (CTWD) is commonly adjusted between layers using expected average values of layer height derived from experimental data. However, due to complex thermal effects, mismatches between expected and actual process conditions lead to uncontrolled CTWD fluctuations, which can cause process instabilities, arc extinction events, and geometric defects. Reliable online estimation of CTWD is therefore essential for process monitoring and for enabling future closed-loop control strategies. Existing CTWD monitoring approaches typically rely on vision-based or acoustic emission systems, which are costly or sensitive to industrial noise. This work proposes a data-driven method for online CTWD estimation using only welding current and voltage, signals already available in industrial WA-DED systems. By avoiding additional sensors, the approach enables low-cost, robust, and real-time CTWD estimation suitable for direct industrial deployment. Several machine learning and deep learning models are evaluated, with particular focus on Ridge Regression, Support Vector Regression and a 1-dimensional convolutional neural network. Experimental results show that the deep learning approach provides higher estimation accuracy and robustness compared to conventional machine learning methods, while remaining suitable for real-time implementation. The proposed method offers a practical solution for CTWD monitoring in WA-DED and represents a step towards intelligent process supervision and control in wire-based additive manufacturing.