In recent years, the manufacturing sector has seen an influx of artificial intelligence applications, seeking to harness its capabilities to improve productivity. However, manufacturing organizations have limited understanding of risks that are posed by the usage of artificial intelligence, especially those related to trust, responsibility, and ethics. While significant effort has been put into developing various general frameworks and definitions to capture these risks, manufacturing and supply chain practitioners face difficulties in implementing these and understanding their impact. These issues can have a significant effect on manufacturing companies, not only at an organization level but also on their employees, clients, and suppliers. This paper aims to increase understanding of trustworthy, responsible, and ethical Artificial Intelligence challenges as they apply to manufacturing and supply chains. We first conduct a systematic mapping study on concepts relevant to trust, responsibility and ethics and their interrelationships. We then use a broadened view of a machine learning lifecycle as a basis to understand how risks and challenges related to these concepts emanate from each phase in the lifecycle. We follow a case study driven approach, providing several illustrative examples that focus on how these challenges manifest themselves in actual manufacturing practice. Finally, we propose a series of research questions as a roadmap for future research in trustworthy, responsible and ethical artificial intelligence applications in manufacturing, to ensure that the envisioned economic and societal benefits are delivered safely and responsibly.

The transition to Industry 5.0 (I5.0) marks a shift toward human-centric, sustainable, and resilient manufacturing, leveraging technologies like collaborative robots (cobots) and AR/VR to enhance inclusivity, empowerment, and safety. This study investigates how Learning Factories (LFs) can effectively convey I5.0 principles to students and professionals. A simulated production line using AR/VR allowed participants to interact with virtual cobots, assessing key pillars of safety, inclusivity, and empowerment. A survey was used to assess the impact of this immersive environment on participants' perceptions and unconscious reactions. The findings demonstrate LFs' potential to prepare a workforce that integrates human creativity with technological innovation.

Aircraft maintenance is a multifaceted process that requires highly skilled, qualified and experienced personnel. Effective maintenance processes optimise aircraft operational lifespan, minimise lifecycle costs and improve reliability by reducing the probability of unexpected maintenance events. The initial diagnostic phase relies on detailed visual inspections conducted by certified technicians. Following inspections, data assessment leads to the development of a comprehensive maintenance plan, along with the sourcing of necessary resources and spare parts. As the maintenance, repair and overhaul (MRO) sector transitions into the era of Industry 4.0, there is a growing emphasis on integrating data analytics and cyber-physical systems into maintenance practices. A key objective in this evolution is the adoption of robotic systems for inspection tasks. This shift requires the reconfiguration of formal inspection procedures to ensure compatibility with robotic operations. Moreover, it is critical to address the specific requirements of robotics and to incorporate smart hangar technologies that take advantage of real-time data to improve both efficiency and effectiveness in maintenance operations. This study provides a comprehensive review of the MRO landscape and maintenance checks, with a particular focus on robotic aircraft inspection systems, navigation and smart hangar infrastructure. The discussion concludes with an examination of defect detection methods using machine vision along with relevant metrics to compare with human performance.

This study presents an innovative framework to improve the accessibility and usability of collaborative robot programming. Building on previous research that evaluated the feasibility of using a domain-specific language based on behaviour-driven development, this paper addresses the limitations of earlier work by integrating additional features like a drag-and-drop Blockly web interface. The system enables end users to define and execute robot actions with minimal technical knowledge, making it more adaptable and intuitive. Additionally, a gesture-recognition module facilitates multimodal interaction, allowing users to control robots through natural gestures. The system was evaluated through a user study involving participants with varying levels of professional experience and little to no programming background. Results indicate significant improvements in user satisfaction, with the system usability scale overall score increasing from 7.50 to 8.67 out of a maximum of 10 and integration ratings rising from 4.42 to 4.58 out of 5. Participants completed tasks using a manageable number of blocks (5 to 8) and reported low frustration levels (mean: 8.75 out of 100) alongside moderate mental demand (mean: 38.33 out of 100). These findings demonstrate the tool’s effectiveness in reducing cognitive load, enhancing user engagement and supporting intuitive, efficient programming of collaborative robots for industrial applications.

Sustainability is becoming a major strategic driver within the aviation industry, which has moved from providing primarily economic benefits to delivering the ‘triple bottom line’, including social and environmental impact as well as financial performance. Sustainable aviation is also being tracked by the International Civil Aviation Organisation (ICAO) Global Collation for Sustainable Aviation. Operations and Infrastructure is an important near-term opportunity to deliver sustainability benefits. Digital Technologies, Integrated Vehicle Health Management (IVHM) and Maintenance Repair and Overhaul (MRO) play a prominent role in implementing these benefits, with a particular focus on operational efficiencies. As part of this, the sustainable smart hangar of the future is a concept that is becoming more and more important in forming the future of the aviation industry. The Hangar of the Future is an excellent opportunity for innovation, combining the progress in manufacturing, materials, robotics and artificial intelligence technologies. Succeeding in developing a hangar with these characteristics will provide us with potential benefits ranging from reduced downtime and costs to improved safety and environmental impact. This work explores some of the key features related to the sustainable smart hangar of the future by discussing research that takes place in DARTeC’s (Digital Aviation Research and Technology Centre) hangar led by the IVHM Centre in Cranfield. Additionally, the paper touches on some longer-term aspirations.

The environment for powertrain production system engineers is changing radically. This initial prescriptive study proposes a systems engineering framework based on two previous case studies which are under review for publication concerning design of battery plants. The framework was developed based on ISO/IEC/IEEE 15288 standard using Concept of Operations and Model-Based Systems Engineering in a workshop setting, with a focus on visualisation to understand the practical and emotional needs of the humans in the system. The framework was validated by twelve senior project members.