Search

A study has been made of the effect of an externally imposed, low-frequency modulation ([less than or equal to] 100 Hz) on the action of a fluid in machining. It is shown that in conventional machining, fluid action in terms of lubrication is essentially confined to the edges of the chip-tool contact along the tool rake face, with little or no change in the friction condition over much of this face. However, the effectiveness of the lubricating action is significantly enhanced when a controlled low-frequency modulation of sufficient amplitude, such as to break the chip-tool contact, is imposed in the direction of cutting. Measurements show that the friction coefficient between tool and chip is reduced by a factor of up to three in the presence of such a modulation. The extent of the secondary deformation zone in the chip material close to the rake face is also significantly reduced. Direct observations of the tool rake face show that when the modulation is applied, the fluid penetrates into much of the intimate contact region between chip and tool. Keywords: Machining, Modulation, Friction, Lubrication

The deformation field, material flow, and mechanics of chip separation in cutting of metals with superimposed low-frequency modulation (

Community ambulation is essential for maintaining a healthy lifestyle, but it poses significant challenges for individuals with limb loss due to complex task demands. In wearable robotics, particularly powered prostheses, there is a critical need to accurately estimate environmental context, such as walking speed and slope, to offer intuitive and seamless assistance during varied ambulation tasks. We developed a user-independent and multi-context, intent recognition system that was deployed in real-time on an open-source knee and ankle powered prosthesis (OSL). We recruited 11 individuals with transfemoral amputation, with 7 participants used for real-time validation. Our findings revealed two main conclusions: 1) the user-independent (IND) performance across speed and slope was not statistically different from user-dependent (DEP) models in real-time, and did not degrade compared to its offline counterparts, 2) IND walking speed estimates showed ~0.09 m/s mean absolute error (MAE) and slope estimates showed ~0.95° MAE across multi-context scenarios. Additionally, we provide an open-source dataset to facilitate further research in accurately estimating speed and slope using an IND approach in real-world walking tasks on a powered prosthesis., (Copyright © 2024 by ASME; reuse license CC-BY 4.0.)