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The ability to render realistic texture perception using haptic devices has been consistently challenging. A key component of texture perception is roughness. When we touch surfaces, mechanoreceptors present under the skin are activated and the information is processed by the nervous system, enabling perception of roughness/smoothness. Several distributed haptic devices capable of producing localized skin stretch have been developed with the aim of rendering realistic roughness perception; however, current state-of-the-art devices rely on device fabrication and psychophysical experimentation to determine whether a device configuration will perform as desired. Predictive models can elucidate physical mechanisms, providing insight and a more effective design iteration process. Since existing models (1, 2) are derived from responses to normal stimuli only, they cannot predict the performance of laterally actuated devices which rely on frictional shear forces to produce localized skin stretch. They are also unable to predict the augmentation of roughness perception when the actuators are spatially dispersed across the contact patch or actuated with a relative phase difference (3). In this study, we have developed a model that can predict the perceived roughness for arbitrary external stimuli and validated it against psychophysical experimental results from different haptic devices reported in the literature. The model elucidates two key mechanisms: (i) the variation in the change of strain across the contact patch can predict roughness perception with strong correlation and (ii) the inclusion of lateral shear forces is essential to correctly predict roughness perception. Using the model can accelerate device optimization by obviating the reliance on trial-and-error approaches.

 

Additive Manufacturing (AM) has opened new frontiers for the design of refractory high-entropy alloys (HEAs) for high-temperature applications. The thermal conductivity of the AM feedstock is among the most important thermo-physical properties that control the melting and solidification process. Despite its significance, there remains a notable gap in both computational and experimental research concerning the thermal conductivity of HEAs. Here, we use density functional theory (DFT) to systematically investigate the alloying effects on the transport properties of Ti-Cr-Mo-W-V-Nb-Ta RHEAs, including electrical and thermal conductivities and the Seebeck coefficient. The relaxation time of charge carriers is a key underlying parameter determining thermal conductivity that is exceedingly challenging to predict from first principles alone, and we thus follow the approach by Mukherjee, Satsangi, and Singh [Chem Mater 32, 6507 (2022)] to optimize the relaxation time for RHEAs. We validated thermal conductivity predictions on elemental solids, binary and ternary alloys, and RHEAs and compared them against thermodynamic (CALPHAD) predictions and our experiments with good correlations. To understand observed trends in thermal conductivity, we assessed the phase stability, electronic structure, phonon, and intrinsic- and tensile strength of down-selected RHEAs. Our electronic structure and phonon results connect well with the observed compositional trends for thermal transport in RHEAs. Our DFT assessment and CALPHAD predictions provide a unique design guide for RHEAs with tailored thermal conductivity, a critical consideration for AM and thermal-management applications. 

Clutches are integral components in robotic systems, enabling programming of system stiffness and precise control over a wide range of motion types. While different types of clutches exist, electroadhesive (EA) clutches present several key advantages, such as flexibility, low mass, low power consumption, simplicity, and fast response. Achieving high EA stress in EA clutches has remained a challenge, however, necessitating high voltage input or a large contact area to achieve the desired force. In this work, an EA clutch is proposed with a high EA stress achieved by taking fracture mechanics into account and using a high dielectric composite layer while still maintaining a comparable high switching speed to other dielectric‐based EA clutches. The maximum EA stress is observed to be 108.8 N cm⁻², which is four times larger than what has been reported previously among dielectric‐based EA clutches at room temperature. This high EA stress clutch can facilitate miniaturization and lower the operating voltage as well as extend to high load capacity applications. The proposed approach holds promise for advancements in various domains, including haptics (both kinesthetic and cutaneous), exoskeletons, walking robots, and other systems that require compliance, low mass, and precise force control.