Enhancing inclusivity in manufacturing environments is critical for addressing workforce diversity and improving overall productivity, particularly for operators with cognitive challenges such as dyscalculia. Operators with mathematical learning difficulties frequently encounter challenges in manufacturing tasks requiring precise numerical interpretation and measurement, which can limit their effective participation in complex workflows. This study presents a human-machine collaboration approach, specifically tailored to support inclusive manufacturing environments, by integrating computer vision, collaborative robotics, and generative AI technologies. The developed system provides targeted assistance across four key stages of a cable cutting and connector assembly process, utilising image-to-robot coordinate transformation and a natural language interface for intuitive voice interaction. The primary methodological contribution of this work is the innovative combination of AI-driven communication and robotic assistance designed to support the alleviation of cognitive burdens associated with numerical processing tasks. Laboratory-scale testing suggests that the system can enhance usability, efficiency, and accuracy, indicating the potential of advanced human-machine collaborative solutions to promote inclusivity and improve operator support in modern manufacturing environments.

Cloud manufacturing integrates cloud computing technologies with traditional manufacturing processes, improving operational efficiency and flexibility. However, it also increases the demand for energy, especially in data centres, which exacerbates the environmental impact. To address this issue, this research work presents a framework suitable for identifying, characterising, and managing energy inefficiencies in digital infrastructures. The framework includes energy data collection, inefficiency characterisation, root cause analysis and implementation of a targeted solution. By focusing on key factors such as server utilisation, data management and resource allocation, the proposed methodology aims to contribute reducing energy consumption, operational costs and CO₂ emissions. The iterative approach proposed in the framework could enable continuous improvement and adaptation, promoting sustainable cloud manufacturing in digital infrastructure environments.

Modern manufacturing increasingly demands energy-and resource-efficient solutions. Conventional metal forming often requires high temperatures to reduce flow stress, resulting in high energy consumption, especially for low-formability alloys. Electrically-Assisted Manufacturing (EAM) has emerged as a promising alternative, leveraging the electroplastic effect, i.e. electricity’s direct influence on plastic deformation. Documented benefits include reduced forming forces, improved ductility, and altered fracture modes. Indeed, integrating electroplasticity into manufacturing aligns with Industry 4.0 and decarbonization goals, enabling lower energy consumption, extended tool life, and greater compatibility with renewable energy sources. This study compares conventional tensile testing and electro-assisted tensile testing (EAM) of Ti6Al4V, evaluating both mechanical results and the energy consumption of the testing machine under different conditions. The comparison results highlight the potential of pulsed current to improve material formability while reducing energy consumption, offering a more sustainable approach to manufacturing.

Manufacturing remains a major source of greenhouse gas emissions, often locked into carbon-intensive routes. Although Additive Manufacturing (AM) improves material efficiency, its high specific energy consumption can offset environmental benefits compared to subtractive manufacturing (SM). Hybrid strategies combining AM and SM offer a promising approach, but model-based procedures to support their selection at the design stage are limited. This study presents an early-stage LCA-based decision-support framework, designed for straightforward industrial implementation, that starts from a CAD model to compare manufacturing sequences and identify those that minimize the carbon footprint (CF) within cradle-to-gate boundaries. The geometry is discretized into uniform layers, and the Solid-to-Cavity Ratio is extended to a layer-resolved form (SCRi) scaled by process-specific impact intensities for milling and Wire Arc Additive Manufacturing (WAAM); the switching layer is selected as the one minimizing the cumulative CF over all admissible positions. Applied to three controlled benchmark geometries and the mock-up of a real industrial landing gear, the framework locates the optimal switching layer and quantifies the resulting reduction in CF, up to 61.2

Electric vehicle design requires integrating diverse specifications with manufacturability constraints to enhance competitiveness. This work presents a framework that combines design optimization and manufacturability analysis to guide strategic decision-making and improve production outcomes. Using data mining, fuzzy logic, and segmentation algorithms, vehicle specifications are categorized into macro-specifications, such as economic accessibility and sustainability. An optimization tool identifies critical design parameters and predicts feasible adjustments within manufacturability limitations. The framework is validated through a case study, with results discussed in terms of design improvements and manufacturability-driven production efficiency, offering insights for product innovation and competitive positioning.Image, application 1Application 1Image, application 2Application 2

The Enhanced Factory for Extra-terrestrial Space Technology Operations (EFESTO) project presents a strategic approach for establishing sustainable manufacturing and recycling operations in Low Earth Orbit (LEO), aiming to mitigate launcher constraints, support long- duration missions, increase space stations independence from Earth resupply and enhance circularity of the resources in space. Utilizing Model-Based Systems Engineering (MBSE), this research aims at identifying and investigating the enabling technologies for in-space manufacturing (ISM) and waste management in microgravity environments. This paper details the comprehensive process, from conception to implementation, including environmental impact assessments and navigating market uncertainties. Preliminary findings demonstrate the project's capacity to bolster space operations, indicating a shift towards a sustainable, service-oriented economy in space. The systematic structure of the project addresses a multitude of challenges, from the development of appropriate space technologies and waste management systems to strategic market and regulatory considerations. Ultimately, EFESTO aims to contribute significantly to extra-terrestrial development, enhancing the resilience and adaptability of space operations while promoting a circular economy and compliance with evolving space standards.

In recent years, the need to design inclusive workplaces has grown, particularly in manufacturing contexts where high cognitive demands may disadvantage neurodiverse individuals. In manufacturing environments, neurodiverse workers often experience difficulties processing standard instructions, increasing cognitive load and errors and reducing overall performance. This study proposes a methodology to assess cognitive load during assembly tasks to support workers with dyslexia. A multi-layer fuzzy logic framework was developed, integrating physiological, environmental, and task-related data. Physiological signals, including heart rate, heart rate variability, electrodermal activity, and eye-tracking data, were collected using wearable sensors. Ambient conditions were also measured. The model emphasizes the Reading dimension of cognitive load, critical for dyslexic individuals challenged by text-based instructions. A controlled laboratory study with 18 neurotypical participants simulated dyslexia scenarios with and without support, compared to a control condition. Results indicated that a lack of support increased cognitive load and reduced performance in complex tasks. In simpler tasks, control participants showed higher cognitive effort, possibly employing overcompensation strategies by exerting additional cognitive resources to maintain performance. Support mechanisms, such as audio prompts, effectively reduced cognitive load, highlighting the framework’s potential for fostering inclusive practices in industrial environments.

Inclusive manufacturing fosters social sustainability and equal opportunities for diverse cognitive profiles, achievable through Industry 4.0/5.0 technologies. A logic-assisted assembly support (LoAS) system to guide neurodiverse operators with logic-related assembly challenges is proposed in this research. The system uses a deep learning paradigm for intelligent object recognition to verify the correct completion of critical steps and to identify assembly errors and incorrect sequences. The operational logic is modelled through a dynamic flowchart automatically generated by a large language model (LLM) based on feedback from the vision system. This flowchart shows each step, along with the tools and components required, and is updated in real time to inform operators of their next steps and current location within the process. The system also includes a step-by-step verification mechanism with detailed completion diagrams to ensure accuracy. Interactive instructions provide personalised step-by-step guidance, visual identification and immediate feedback to correct errors and guide the operator through the assembly sequence. The efficiency and effectiveness of LoAS have been verified at a proof-of-concept lab scale on an industrial workpiece. The results suggest the potential of those tools to foster inclusiveness in manufacturing environments.

Industrial manufacturing processes require precise understanding of instructions, which can be challenging for neurodiverse operators with reading difficulties. To bridge this gap, a digital instruction framework using object detection and natural language processing is proposed in this research. The framework uses an intelligent vision system to monitor task execution, coupled with the automatic generation of personalised voice instructions via large language models. This approach aims to improve accessibility and inclusivity in assembly lines. A case study on the assembly of a horizontal bare-shaft centrifugal pump demonstrates the effectiveness of the framework in reducing assembly errors and improving operational efficiency, making it particularly beneficial for neurodiverse individuals and promoting an inclusive work environment.Application 1Supplementary 1Application 2Supplementary 2Application 3Supplementary 3

The transition to Industry 5.0 emphasizes the need for inclusive and intelligent systems in manufacturing. Traditional text-based assembly instructions can create barriers for workers with cognitive diversity, including dyslexia, by making tasks more challenging and less accessible. This study introduces an AI-driven smart manufacturing interface designed to address these challenges through an integrated approach combining computer vision techniques and natural language processing. The system provides real-time, multimodal guidance through voice, visual, and interactive instructions tailored to individual needs, effectively reducing cognitive load and enhancing task performance. Experimental results show a 22.1% reduction in assembly time and a 57.1% decrease in error rates, demonstrating the system's effectiveness in improving operational efficiency and accuracy. By leveraging innovative information technologies, the interface aligns with the human-centric principles of Industry 5.0, supporting diverse and adaptable manufacturing environments. These findings highlight the potential of smart systems to drive inclusivity and sustainability in industrial ecosystems.

The transition to Industry 5.0 highlights the necessity for human-centric and adaptive manufacturing systems. This study conceptualises a multimodal, generative AI-based assistive system for assembly designed to deliver real-time error detection and adaptive guidance tailored to diverse operator profiles. The system improves human-machine interaction by issuing preventive warnings to the operator prior to critical tasks, detecting assembly errors, providing multimodal corrective instructions during operations, and deploying robotic interventions when operator-driven corrections prove inadequate. Preliminary laboratory-scale implementation results show the system capability in mitigating assembly errors through dynamic assistive technology selection and iterative feedback learning. (c) 2025 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

The manufacturing sector faces significant challenges in promoting inclusivity, particularly for neurodiverse individuals who may encounter difficulties with traditional text-based assembly instructions. This research addresses such challenges by developing a worker-assistance framework designed to improve both operational efficiency and inclusivity within assembly tasks. The proposed AI-based system integrates an intelligent vision system for real-time task monitoring, an intelligent instruction generation module for producing personalised, context-specific guidance, and an instruction delivery module that provides hands-free, voice-guided instructions. In a laboratory-scale case study involving the assembly of a centrifugal pump, the system was tested with simulated cognitive challenges, such as dyslexia-like text distortions. The results indicate that the AI-driven system could significantly decrease assembly errors and task completion time compared to traditional human supervision, while providing support tailored to the needs of neurodiverse operators. These findings suggest the system potential to prevent common errors and improve accessibility for operators with cognitive variations. Future developments may enhance system flexibility for different assembly workflows, introduce techniques to evaluate the cognitive effort, and extend its implementation to a more diverse neurodivergent workforce to strengthen inclusivity in manufacturing environments.

Sustainable manufacturing is gaining momentum as industries strive to minimize the environmental impact of manufacturing and improve sustainability by maximizing the resource efficiency and reducing waste. Additive manufacturing (AM), as a technology that demands fewer raw materials and produces less waste compared to more traditional manufacturing approaches, could inherently support these goals. Among the different AM processes, wire arc additive manufacturing (WAAM) and cold metal transfer (CMT) have shown promise, particularly in the steel production field, where forging, casting and machining have so far been the predominant processes. In order to achieve an effective transition to sustainable manufacturing practices, it is necessary to fully characterize the CMT process, not only from an energy perspective but also by ensuring a high material utilization efficiency and product quality. In this study, the main process parameters have been varied in the deposition of single beads and multilayer structures made of AWS ER 308L Si, and the results have been analyzed in terms of specific energy consumption, microstructure and microhardness of the as-built structures. Overall, the study concludes that the process parameters primarily control energy consumption and the heat applied to the components, rather than the resulting microstructure, which, instead, is influenced more by thermal cycling resulting from the layer-by-layer deposition process. This suggests that process parameters could be selected with a focus on energy savings without significant adverse effects on material properties.

This study explores the potential synergy between neurodiversity and advanced technology within Industry 5.0, focusing on the integration of neurodiverse individuals in the workforce through Human-Machine Collaboration and Reciprocal Learning (RL). A cognitive load (CL) assessment procedure is developed using fuzzy logic inference across the dimensions of attention, memory, language, math, logic, and reading. A case study evaluates the effectiveness of RL in assisting assembly tasks. Different error-handling scenarios are compared. Experimental results show how RL can reduce the CL while improving assembly tasks efficiency, underscoring the value of intelligent systems in inclusive manufacturing, enhancing productivity and facilitating the integration of neurodiverse workers.

Wire Arc Additive Manufacturing (WAAM) processes could offer significant advantages, in terms of energy and material use efficiency, when used for the production and repair of metal components. However, although a large number of economic assessments have been carried out on powder-based Additive Manufacturing (AM) processes, few studies have addressed the cost of WAAM technologies. There is a lack of research that has simultaneously considered the economic and environmental impacts of hybrid processes based on WAAM. In this study, a cost model, adapted to Cold Metal Transfer (CMT), has been implemented on a steel airfoil mock-up selected as a case study. All the cost drivers have been identified at the different stages of the process, together with their relationships to the deposition process parameters, which have been systematically varied to assess their influence. The results show a close correlation between the overall cost trend and the cumulative energy demand as the process parameters vary, and that the optimisation of the main CMT process parameters should primarily be aimed at increasing the material deposition efficiency, which bridges the CMT and finishing processes.

The manufacturing industry relies heavily on human labour, making the health and safety of operators paramount. Emerging technologies such as Human-Cyber-Physical Systems (HCPS) and Machine Learning (ML) have the potential to transform the approach to these critical issues. In fact, such technologies enable the creation of virtual replicas of tangible systems, providing innovative solutions for assessing operator safety. In this context, this work presents a digital humanization methodology designed to comprehensively evaluate the health and safety risks associated with operator involvement in production processes. Using cutting-edge sensors, advanced machine learning algorithms, and scenario simulations, such methodology generates real-time virtual representations of the physical condition and behaviour of the operator. These representations allow the risk characterization by estimating magnitude, priority, and occurrence, thereby facilitating early detection and preventive measures against potential hazards. A case study is considered to demonstrate the practical application of the proposed framework.

As manufacturing becomes increasingly digitalized and connected, new opportunities arise for monitoring operator health and safety risks. Addressing these risks by leveraging Industry 4.0 enabling technologies is strategic, as significant improvements in terms of social sustainability, such as the operator well-being, can positively impact manufacturing quality and productivity. In this context, this paper proposes a methodological framework that aims to leverage information derived from a variety of sensor data to provide actionable steps to support decisions to mitigate health and safety risks. A simulated case study to validate the suitability of the proposed framework is reported for relevant scenarios. (c) 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

The automotive sector has recently been taking measures to reduce fuel consumption and greenhouse gas emissions for the mobility of ground vehicles. Light-weighting, via material substitution, and the re-designing of components or even a combination of the two, have been identified as a crucial solution. Additive manufacturing (AM) can be used to technologically complement or even replace conventional manufacturing in several industrial fields. The enabling of complexity-for-free (re) designs is inherent in additive manufacturing. It is expected that certain benefits can be achieved from the adoption of re-design techniques, via AM, that rely on topological optimisation, e.g., a reduced use of resources in both the material production and use phases. However, the consequent higher specific energy consumption and the higher embodied impact of feedstock materials could result in unsustainable environmental costs. This paper investigates the case of the light-weighting of an automobile component to quantify the outcomes of the systematic integration of re-designing and material substitution. A bracket, originally cast in iron, has been manufactured by means of a powder bed-based AM technique in AlSi10Mg through an optimized topology. Both manufacturing routes have been evaluated through a comparative Life Cycle Assessment (LCA) within cradle-to-grave boundaries. A 69%-lightweighting has been achieved, and the carbon dioxide emissions and energy demands of both scenarios have been compared. Besides the use-phase-related savings in terms of both energy and carbon footprint due to the lightweighting, the results highlight the environmental trade-offs and prompt the consideration of such a manufacturing process as an integral part of sustainable product development.

Composite materials showed great potential in replacing metal components in several applications allowing adequate component strength with reduced weight. From a sustainability point of view, a significant number of studies demonstrated that a component made of composite materials is one of the best responses to the recent global legislation for the reduction of energy consumption and CO2 emissions. The sustainability of the production of composite components should be assessed by structured approaches that consider the whole life cycle of the component from the raw material production, the manufacturing process and post processes to the end-of-life (EoL). The purpose of this paper is to present the state-of-the-art life cycle inventory (LCI) data available in the literature for a composite product. Works evaluating the embodied energy of the most common fibres and polymeric matrixes are collected. Each manufacturing technique is reviewed regarding energy efficiency by considering the specific energy consumption (SEC). Among the potentialities which characterise the EoL of a composite product, a focus is given to the recycling techniques. Future research challenges are proposed and discussed. The outcomes revealed a considerable dispersion in embodied energies and SEC values for both the reviewed materials and technologies. The SEC is the only descriptor for process efficiency. However, there is a lack of investigation into the relationship between the process parameters, processed materials, component size and energy consumption. In particular, for additive manufacturing processes, no data were found. In addition, the literature on using natural fibres as a sustainable alternative and recycling methods and their impact is extremely limited.