A Comparative Machinability Study of SS 304 in Turning under Dry, New Micro-Jet, and Flood Cooling Lubrication Conditions

The main objective of this experimental investigation is to examine favourable machining conditions by utilising fewer resources of machining industries for the techno-economical and ecological benefits. The machining operations are performed in turning SS 304 using coated carbide tool inserts under dry, water-soluble cutting fluid solution in the form of flood cooling and small-quantity lubrication (SQL) conditions by employing a newly formed micro-jet for a comparative classical chips study and analysis. The machining experiments are conducted in turning by a 25 kW precision CNC lathe with a special arrangement of micro-jets into the machining zone. Machining speeds and feed rates are varied under dry, micro-jet, and flood cooling conditions and their effects are studied on the type of chips and their morphology, chip reduction coefficient (ξ), and chip shear plane distance (d). The effect of machining environments on tool health conditions (such as BUEs, tool-edge chipping, and edge breaking) is examined for the inferences. In the range of low-speed machining (less than 600 m/min), metal cutting seems easier in flood cooling conditions, but it imposes more unfavourable effects (such as edge chipping and edge breaking) on the ceramic cutting tool’s health. On the other hand, the dry machining condition shows a favourable performance for a ceramic cutting tool. The optimum machining condition is found in the micro-jet SQL by the analysis of experimental data and observation results for the tool and work combination. The analysis of the results is carried out by the response surface methodology (RSM) and artificial neural network (ANN). The ANN model is found to be more accurate than RSM. The aspects of effective green machining are emphasised

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population