Nanofluids, micro-lubrications and machining process optimisations − a review

The lubrication is a prime requirement of metal cutting industries to assure high quality performance. The conventional technique of coolant flow is less economical and eco-friendly. Recently, nano fluids found better cutting fluid in machining due to potential thermal and heat transfer properties. The role of micro-lubrication techniques and process optimization are equally important for improving process performance. The literature review presents the findings of different researchers in the field of nano fluids and micro-lubrication techniques. The experimental studies were focused on better process performance using micro-lubrication techniques, especially nanofluid MQL with optimized process parameters. The thermal conductivity of water based TiO2 nano fluid shows improvement by 22% in base fluids. The case study discussed which is focused on preparation and characterization of nano fluid, experimental setup and optimization of process parameters by Jaya algorithm. Finally, application of nano fluid, and challenges during nano fluid preparation is identified. The scope of research work is recommended for further study to obtain an economical, eco-friendly manufacturing process

Experimental study of hardness effects on surface roughness for nanofluid minimum quantity lu-brication (NanoMQL) technique using Jaya algorithm

The NanoMQL technique is used to overcome the limitations of wet grinding due to economic and ecological problems. The performance measure is largely influenced by the process parameters such as table speed, depth of cut, air pressure, coolant flow rate and nanofluid concentration. In this paper, the performance of NanoMQL technique in terms of surface roughness was evaluated for hard and soft EN31 steel. The Experiments were conducted by response surface methodology (RSM) using statistical software to develop regression model of surface roughness and optimization was carried out using Jaya algorithm. The result shows that lowest value of surface roughness was obtained for NanoMQL of hard steel in comparison with soft steel under grinding environ-ments such as wet, MQL and NanoMQL. Hence to improve the performance of soft steel, the modeling and optimization of surface roughness were carried out. The significant parameters were considered for model development and validity of model determined through ANOVA (Analysis of variance). Lastly, the optimal values were determined using Jaya algorithm for minimum surface roughness and the percentage error observed to be close with the experimental test

Nanofluids, micro-lubrications and machining process optimisations − a review

The lubrication is a prime requirement of metal cutting industries to assure high quality performance. The conventional technique of coolant flow is less economical and eco-friendly. Recently, nano fluids found better cutting fluid in machining due to potential thermal and heat transfer properties. The role of micro-lubrication techniques and process optimization are equally important for improving process performance. The literature review presents the findings of different researchers in the field of nano fluids and micro-lubrication techniques. The experimental studies were focused on better process performance using micro-lubrication techniques, especially nanofluid MQL with optimized process parameters. The thermal conductivity of water based TiO2 nano fluid shows improvement by 22% in base fluids. The case study discussed which is focused on preparation and characterization of nano fluid, experimental setup and optimization of process parameters by Jaya algorithm. Finally, application of nano fluid, and challenges during nano fluid preparation is identified. The scope of research work is recommended for further study to obtain an economical, eco-friendly manufacturing process

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)