A 6-Components Mechanistic Model of Cutting Actions in Milling

Most of the cutting models developed in the literature attest only to the presence of cutting forces in the balance of mechanical actions resulting from cutting. However, several studies have highlighted the presence of cutting moments during machining, and particularly 3D cutting in milling. The objective of this paper is to characterise phenomena associated with cutting moments by performing experimental mechanistic modelling in 3D cutting. For this purpose, several modelling factors will be investigated, such as the 3D cutting reference frame, the undeformed chip section, the cutting parameters, the cutting zone, etc. The predictive model of this study proves to be relatively efficient for an experimental model and allows a global prediction of cutting moments in milling. Furthermore, beyond the aspect of stress fields in the workpiece caused by cutting moments, this paper gives perspectives from an energetic point of view for which the share of moments in the energy balance could be substantial for monobloc tools.

Estimating the transmissibility of SARS-CoV-2 during periods of high, low and zero case incidence

Against a backdrop of widespread global transmission, a number of countries have successfully brought large outbreaks of COVID-19 under control and maintained near-elimination status. A key element of epidemic response is the tracking of disease transmissibility in near real-time. During major outbreaks, the reproduction rate can be estimated from a time-series of case, hospitalisation or death counts. In low or zero incidence settings, knowing the potential for the virus to spread is a response priority. Absence of case data means that this potential cannot be estimated directly. We present a semi-mechanistic modelling framework that draws on time-series of both behavioural data and case data (when disease activity is present) to estimate the transmissibility of SARS-CoV-2 from periods of high to low -- or zero -- case incidence, with a coherent transition in interpretation across the changing epidemiological situations. Of note, during periods of epidemic activity, our analysis recovers the effective reproduction number, while during periods of low -- or zero -- case incidence, it provides an estimate of transmission risk. This enables tracking and planning of progress towards the control of large outbreaks, maintenance of virus suppression, and monitoring the risk posed by re-introduction of the virus. We demonstrate the value of our methods by reporting on their use throughout 2020 in Australia, where they have become a central component of the national COVID-19 response.