In modern days, the conception of sustainability has progressively advanced and has begun receiving global interest. Thus, sustainability is an imperative idea in modern research. Considering the recent trend, this review paper presents a summary of the previously published research articles on minimum quantity lubrication (MQL) assisted machining. The requirement to stir towards sustainability motivated the researchers to revise the effects of substitute lubrication methods on the machining. Conventional lubri-cooling agents are still extensively employed when machining of engineering alloys, but the majority of the recent papers have depicted that the utilization of vegetable oil, nanofluids, and nanoplatelets in MQL system confers superior machining performances as compared to conventional lubrication technology. In actual, the definite principle of this manuscript is to re-examine modern advancements in the MQL technique and also explore the benefits of the vegetable oil and nanofluid as a lubricant. In brief, this paper is a testimony to the advancing capabilities of eco-friendly MQL technique which is a viable alternative to the flood lubrication technology, and the outcomes of this review work can be contemplated as a movement towards sustainable machining.

Environmental protection is the major concern of any form of manufacturing industry today. As focus has shifted towards sustainable cooling strategies, minimum quantity lubrication (MQL) has proven its usefulness. The current survey intends to make the MQL strategy more effective while improving its performance. A Ranque–Hilsch vortex tube (RHVT) was implemented into the MQL process in order to enhance the performance of the manufacturing process. The RHVT is a device that allows for separating the hot and cold air within the compressed air flows that come tangentially into the vortex chamber through the inlet nozzles. Turning tests with a unique combination of cooling technique were performed on titanium (Grade 2), where the effectiveness of the RHVT was evaluated. The surface quality measurements, forces values, and tool wear were carefully investigated. A combination of analysis of variance (ANOVA) and evolutionary techniques (particle swarm optimization (PSO), bacteria foraging optimization (BFO), and teaching learning-based optimization (TLBO)) was brought into use in order to analyze the influence of the process parameters. In the end, an appropriate correlation between PSO, BFO, and TLBO was investigated. It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%.

This study emphasizes the criticality of measuring specific cutting energy in machining Hastelloy C276 for biomedical industry applications, offering valuable insights into machinability and facilitating the optimization of tool selection, cutting parameters, and process efficiency. The research employs artificial intelligence-assisted meta-models for cost-effective and accurate predictions of specific cutting energy consumption. Comparative analyses conducted on Hastelloy C276, utilizing a TiAlN-coated solid carbide insert across various media (dry, MQL, LN2, and MQL+LN2), reveal the superiority of hybrid LN2+MQL in reducing specific cutting energy consumption. Subsequently, the analysis of variance underscores the cutting speed as the most influential parameter as compared to other inputs. Finally, a statistical evaluation compares the Gene Expression Programming (GEP) model against the Artificial Neural Network (ANN), and Response Surface Methodology model, demonstrating the superior predictive performance of the GEP meta-model. The GEP model demonstrates validation results with an error range of 0.25%–1.52%, outperforming the ANN and RSM models, which exhibit an error range of 0.49%–8.33% and 2.68%–10.18%, respectively. This study suggests the potential integration of contemporary intelligent methodologies for sustainable superalloy machining in biomedical industry applications, providing a foundation for enhanced productivity and reduced environmental impact of surgical instrument and biomedical device machining.

The necessity to progress towards sustainability has inspired modern researchers to examine the lubrication and cooling effects of vegetable oils on conventional metal cutting operations. Consequently, as an eco-friendly vegetable product, castor oil can be the right choice as Minimum quantity lubrication (MQL) base fluid. Nonetheless, the high viscosity of castor oil limits its flowability and restricts its industrial application. Conversely, palm oil possesses superior lubricity, as well as flowability characteristics. Hence, an attempt has been made to improve the lubrication behavior of castor oil. Here, six castor-palm mixtures (varying from 1:0.5–1:3) were utilized as MQL-fluid, and the values of machining responses viz. average surface roughness, specific cutting energy, and tool wear were evaluated. Furthermore, an integrated Shannon’s Entropy-based Technique for order preference by similarity to ideal solution (TOPSIS) framework was employed for selecting the most suitable volume ratio of castor-palm oil mixture. The rank provided by the TOPSIS method confirmed that 1:2 was the best volume ratio for castor-palm oil mixture. Afterward, a comparative analysis demonstrated that the best castor-palm volume fraction resulted in 8.262 and 16.146% lowering of surface roughness, 5.459 and 7.971% decrement of specific cutting energy, 2.445 and 3.155% drop in tool wear compared to that of castor and palm oil medium, respectively.