Reconfigurable manufacturing requires robotic workcells that can adapt to changing products, processes, and equipment without extensive redesign or manual reprogramming. This paper presents the R3M framework, a ROS 2 driven integration architecture that connects model based manufacturing knowledge with execution level robotic control. Product, process, and equipment information is formalised through UML domain models and serialised in AutomationML(AML), enabling automated correspondence between assembly requirements, available skills, and executable recipes. The framework integrates automated programme generation, reinforcement learning based recipe optimisation, and a modular CAD informed six degree of freedom perception layer to support both technical and semantic interoperability across simulated and physical workcells. The approach is evaluated through Cube Kitting and Cylinder Stacking use cases implemented on distinct robotic platforms, including ABB and Universal Robots systems. Experimental results show high reliability, with environment launch performance reaching up to 99.93%, standard skill sequences achieving 100% execution success, and full use case trials exceeding 97% success in simulation and reaching 100% on physical hardware. These findings demonstrate that R3M provides a scalable foundation for adaptive robotic manufacturing, reducing programming effort while supporting modular substitution, robust perception, and simulation to real deployment.
Wire-based directed energy deposition (DED) additive manufacturing (AM) uses an intense energy source, such as an electric arc, laser, and electron beam, to melt metal wire feedstock and deposit a structural part layer by layer. This emerging manufacturing process is advantageous thanks to its large-scale deposition capacity, high efficiency of material and energy usage, and wide applicability to different industrial applications. However, research is still needed to enhance the process productivity and part quality. Wire preheating is a feasible method to significantly enhance deposition rate. It can also help reduce heat input of the main energy source, inhibit pore formation and refine grains, thereby enhancing mechanical properties of the deposited part. Induction heating (IH) is a highly controllable non-contact heating method suited for rapidly and precisely preheating the wire feedstock to a target temperature. In addition, compared with conventional weld wire preheating methods such as resistance heating and bypass heating, IH avoids magnetic blow and is applicable to most metals with flexible setup. However, IH-based preheating of moving wire feedstock is complicated and underexplored for AM applications. In this study, to understand the complex electromagnetic heating mechanism, a multiphysics finite element model of coupled electromagnetic and thermal fields is developed based on the formulation in Eulerian frame, which improves the computational efficiency by 80.9 % compared to the model in Lagrangian frame. Furthermore, in the case of feedstock passing through a stationary magnetic field at a constant wire feed speed, a more efficient steady-state approach is proposed with 98.9 % computational time saving than the transient model. The temperature predictions by the models were validated by thermocouple measurement in an experiment. A range of coil geometries and setups were evaluated using the developed efficient model, revealing the coil effects on the wire preheating temperature and energy consumption.
Sugar beet pulp (SBP) is an abundant polysaccharide-rich agro-industrial residue with distinctly low lignin and significant pectin content. Suitable pretreatment strategies tailored to SBP constituents are thus critical for its valorisation to diverse products. We developed two novel pretreatments, hydrogen peroxide-tween 80 (PS) and polypropylene glycol (PPG) and benchmarked them against dilute hydrochloric acid pretreatment (DAP). The process development includes response surface optimization, sequential pectinase-cellulase hydrolysis, and a techno-economic analysis (TEA). DAP achieved 75% theoretical arabinose yield (28.59 g/L) while solubilizing 80% of pectin, recovered via ethanol precipitation. A subsequent cellulase hydrolysis released 45.1 g/L glucose at 83% theoretical yield. PS removed 62-67% lignin through oxidative delignification with minimal sugar loss (less than 2 g/L), and PPG substantially improved enzyme accessibility through solvent-mediated surface disruption with negligible PPG loading. Sequential enzymatic hydrolysis produced 59.4 g/L and 55.3 g/L total sugars (ara + GalA dominant) from the pectinase stage, and 69.6 g/L and 81.7 g/L (glucose dominant) from the cellulase stage, for PS and PPG, respectively. TEA, using 100 MT SBP/day as plant capacity, showed that the production costs per kg of sugar mixtures were $0.41, $1.03, and $0.96 for DAP, PS, and PPG, respectively. However, DAP's apparent cost advantage is contingent on pectin co-product revenue. Without this credit, its production cost rises to $1.05/kg. This work validates PS and PPG as viable novel pretreatments and establishes a comparative framework linking process chemistry, enzymatic performance, and economics for pretreatment selection in SBP-based 2G biorefinery.
Understanding safety in complex socio-technical systems requires analytical approaches that move beyond linear accident models to examine how interactions across organisational, technical and operational elements shape safety outcomes. This study applies two systemic safety analysis approaches, Causal Analysis based on Systems Theory (CAST) and the Functional Resonance Analysis Method (FRAM), using well-documented aviation investigation data. The study examines how different systemic methods model system behaviour, frame causality and performance variability, and generate different forms of safety recommendations. CAST identified cross-level control and feedback weaknesses that enabled the wrong-surface alignment under the runway-closure night configuration. FRAM showed how coupled performance variability, including missed runway-closure cueing and ambiguous visual cues, shaped the development of misalignment risk while also clarifying the recovery pathway that enabled the go-around. This study suggests that CAST and FRAM are best used as complementary lenses for systemic event analysis. CAST supports governance and assurance redesign, while FRAM informs operational guardrails and variability management under uncertainty.
Supply chains (SCs) are increasingly challenged to deliver greater value with fewer resources while minimising environmental impact. Postponement strategies have emerged as a potential solution to strike this balance, yet their impact on environmental sustainability beyond economic considerations remains under-examined. A systematic literature review provides a design-oriented approach combined with the Contexts, Interventions, Mechanisms, and Outcomes (CIMO) logic to deepen the understanding of the effects of postponement strategies on environmental and economic sustainability. The study reconceptualises postponement and traces its recent evolution, expanding it beyond delaying uncertainty-increasing value-adding activities to include their geographical location and the involvement of different supply chain partners. This further enabled the precise characterisation of contemporary postponement applications, leading to the development of propositions that suggest under which conditions postponement strategies may enhance or harm both the environmental and economic performances of SCs. Finally, a future research agenda is proposed to advance postponement and further support sustainable SCs.
