Modern aircrafts require the assembly of thousands of components with high accuracy and reliability. The normality of drilled holes is a critical geometrical tolerance that is required to be achieved in order to realize an efficient assembly process. Failure to achieve the required tolerance leads to structures prone to fatigue problems and assembly errors. Elastomer-based tactile sensors have been used to support robots in acquiring useful physical interaction information with the environments. However, current tactile sensors have not yet been developed to support robotic machining in achieving the tight tolerances of aerospace structures. In this paper, a novel elastomer-based tactile sensor was developed for cobot machining. Three commercial silicon-based elastomer materials were characterised using mechanical testing in order to select a material with the best deformability. A Finite element model was developed to simulate the deformation of the tactile sensor upon interacting with surfaces with different normalities. Additive manufacturing was employed to fabricate the tactile sensor mould, which was chemically etched to improve the surface quality. The tactile sensor was obtained by directly casting and curing the optimum elastomer material onto the additively manufactured mould. A machine learning approach was used to train the simulated and experimental data obtained from the sensor. The capability of the developed vision tactile sensor was evaluated using real-world experiments with various inclination angles, and achieved a mean perpendicularity tolerance of 0.34°. The developed sensor opens a new perspective on low-cost precision cobot machining.

A finite element model of an impact test on a soft tissue simulant, used as part of a shoulder surrogate, was developed in Ansys© LS-DYNA^®. The surrogate consisted of a metal hemicylindrical core, with a diameter of 75 mm, covered with a 15 mm thick relaxed muscle simulant. The muscle simulant consisted of a 14 mm thick layer of silicone covered with 1 mm thick chamois leather to represent skin. The material properties of the silicone were obtained via quasi-static compression testing (curve fit with hyperelastic models) and compressive stress relaxation testing (curve fit with a Prony series). Outputs of the finite element models were compared against experimental data from impact tests on the shoulder surrogate at energies of 4.9, 9.8 and 14.7 J. The accuracy of the finite element models was assessed using four parameters: peak impact force, maximum deformation, impact duration and impulse. A 5-parameter Mooney-Rivlin material model combined with a 2-term Prony series was found to be suitable for modelling the soft tissue simulant of the shoulder surrogate. This model had under 10% overall mean deviation from the experimental values for the four assessment parameters across the three impact energies. Overall, the model provided a repeatable test method that can be adapted to help predict injuries to skin tissue and the performance/efficacy of personal protective equipment.

The construction sector faces pressure to decarbonise while addressing rising resource demands and agricultural waste. Ordinary Portland cement (OPC) is a major CO₂ emitter, yet biomass residues are often open-burned or landfilled. This study explores corncob ash (CCA) as a sustainable supplementary cementitious material (SCM), examining how calcination conditions influence pozzolanic potential and support circular economy and climate goals, which have not been adequately explored in literature. Ten CCA samples were produced via open-air burning (2–3.5 h) and electric-furnace calcination (400–1000 °C, 2 h), alongside a reference OPC. Mass yield, colour, XRD, XRF, LOI, and LOD were analysed within a process–structure–property–performance–sustainability framework. CCA produced in a 400–700 °C furnace window consistently achieved high amorphous contents (typically ≥80%) and combined pozzolanic oxides (SiO₂ + Al₂O₃ + Fe₂O₃) above the 70% ASTM C618 threshold, with 700 °C for 2 h emerging as an optimal condition. At 1000 °C, extensive crystallisation reduced the expected reactivity despite high total silica. Extended open-air burning (3–3.5 h) yielded chemically acceptable but more variable ashes, with lower amorphous content and higher alkalis than furnace-processed CCA. Simple industrial ecology calculations indicate that valorising a fraction of global CC residues and deploying optimally processed CCA at only 20% OPC replacement could displace 180 million tonnes CC waste and clinker avoidance on the order of 5–6 Mt CO₂ per year, while reducing uncontrolled residue burning and primary raw material extraction. The study provides an experimentally validated calcination window and quality indicators for producing reactive CCA, alongside a clear link from laboratory processing to clinker substitution, circular resource use, and alignment with SDGs 9, 12, and 13. The findings establish a materials science foundation for standardised CCA production protocols and future life cycle and performance evaluations of low-carbon CCA binders.

World Rugby™ permits players to wear padded clothing meeting the requirements of Regulation-12, which limits density, thickness and impact performance. Due to non-uniform geometry, measuring the density of padding can be challenging and provides limited information on mechanical properties. This study investigated how well density could predict the impact performance of rugby padding, whilst reviewing compliance parameters as alternatives. Eleven samples of rugby padding, plus one control material, were tested for compliance, then impacted at energies of 4.9, 9.8 and 14.7 J using the setup as defined in Regulation-12, and finally cut to calculate density. The density and compliance parameters were correlated against peak impact accelerations using a Spearman’s rank test. Density was not significantly correlated with peak acceleration at any energy level, with compliance tests significantly correlated with impact acceleration at only 4.9 J.

A finite element model was developed for assessing the efficacy of rugby body padding in reducing the risk of sustaining cuts and abrasions. The model was developed to predict the onset of damage to a soft tissue simulant from concentrated impact loading (i.e., stud impact) and compared against a corresponding experiment. The damage modelling techniques involved defining an element deletion criterion, whereby those on the surface of the surrogate were deleted if their maximum principal stress reached a predefined value. Candidate maximum principal stress values for element deletion criteria were identified independently from puncture test simulations on the soft tissue simulant. Experimental impacts with a stud were carried out at three energies (2, 4 and 6 J), at three angular orientations (0°, 15° and 30°) and compared to corresponding simulations. Suitable maximum principal stress values for element deletion criteria settings were first identified for the 4 J impact, selecting the candidates that best matched the experimental results. The same element deletion settings were then applied in simulations at 2 and 6 J and the validity of the model was further assessed (difference < 15% for the force at tear and < 30% for time to tear). The damage modelling techniques presented here could be applied to other skin simulants to assess the onset of skin injuries and the ability of padding to prevent them.