Sustainability in machining has emerged as important in modern manufacturing, aiming to reduce the environmental impact, maintain economic viability, and social responsibility. This investigation focuses on the machinability of Ti-6Al-4 V alloy, a high-strength material used in aerospace, biomedical, and automotive applications, using a wiper geometry tool. The research aims to minimise machining power by employing advanced machine learning models and a meta-heuristic optimisation algorithm. Machining power is one of the critical parameters of environmental impact. The study fills a gap in the existing literature by integrating machine learning-based techniques for power modelling in machining. To predict machining power, two machine learning-driven regression approaches are implemented, relying on primary machining parameters including cutting speed, feed rate, and depth of cut. The predictive performance is evaluated for practical applications, and the predictive model is combined with a meta-heuristic optimisation algorithm to identify optimal machining conditions in minimum quantity lubrication (MQL) that minimise power consumption while maintaining process performance. The study offers valuable insights into sustainable machining strategies for Ti-6Al-4 V alloy, providing a robust framework for industrial applications and contributing to sustainable manufacturing and energy-efficient production.

Sustainable machining of Ni-Fe-Cr superalloys remains a critical challenge due to their high strength and poor thermal conductivity. Although vegetable-oil-based minimum quantity lubrication (MQL) has shown promising performance, its potential is further enhanced with nanoparticle additives. However, duplex-nozzle MQL and graphene oxide (GO) nanofluids have been scarcely explored for machining Ni-based alloys. Addressing these research gaps, the present study develops a groundnut-oil-based GO nanofluid and experimentally evaluates its performance in turning Incoloy 800HT under duplex-nozzle nano-MQL. Surface roughness, tool wear, power consumption, carbon emissions, and chip morphology were analyzed across varying cutting conditions. The GO nanofluid demonstrated superior lubrication and cooling capabilities, achieving surface roughness as low as 0.231 & micro;m and reducing flank wear to 0.042-0.129 mm through improved heat dissipation. Cutting speed strongly influenced both wear and power consumption, which ranged from 310.3 to 694.5 W. Carbon emissions decreased with higher speed-feed combinations, and chip morphology indicated stable cutting under a duplex nozzle nano-MQL. Additionally, machine-learning algorithms, namely random forest (RF) and Gaussian process regression (GPR), were implemented for predicting and modeling machining responses. The RF yielded coefficient of determination (R-2) values of 0.975, 0.963, 0.962, and 0.965 for Ra, VBc, Pc, and Ce, respectively. Whereas GPR provided higher accuracy with R-2 values of 0.99, 1.00, 0.99, and 0.99 for the same-machining responses. In addition, the prediction accuracy was verified through mean absolute error (MAE). Based on R-2 and MAE results, GPR outperformed RF, therefore, GPR is recommended for future application in machining research.

Integrating solid waste ferrochrome slag with graphene oxide reinforcements in Al7075 hybrid nanocomposites opens new avenues for the aerospace, marine, automobile applications. This study addresses the critical research gap by synthesizing, characterizing, and assessing the machinability of Al7075/Fe-Cr slag (5wt. %)/GO (0.5, 1, and 1.5wt. %) hybrid nanocomposites which have been fabricated through novel ultrasonic-stir-squeeze casting method and machined under sustainable dual-nozzle MQL environment. Microstructure analysis of 0.5wt.% GO shows a dendritic structure with fine, uniform grains, indicating significant grain refinement during solidification. This composition yielded improved mechanical and tribological properties with microhardness of 165 HV, an UTS of 226MPa, an elongation of 8.17%, low wear coefficient of 0.098-0.2174, and coefficient of friction of 0.307-0.346 respectively. Machinability investigations revealed acceptable ranges of flank wear from 0.03-0.078mm, surface roughness from 0.403-1.293µm, temperature from 56.3-92.70C, power consumption from 0.334-0.507kW, carbon and noise emission from 0.00412 -0.02105 kgCO2, and 74-78.9dB respectively. Circularity and cylindricity remained well below 0.1mm tolerance zone, producing saw tooth chips. The lowest tensile residual stress was observed for 1wt.% GO composite (181.06MPa) post-machining due to the self-lubricating effect of GO paired with MQL. The optimal parameters are found to be w: 1.1061wt.%, v:76.1616m/min, f: 0.05mm/rev, and d: 0.1101mm with composite desirability 0.9878. The synergistic effect of the inherent solid nano-lubricant layer from the GO, squeeze casting structure, and the MQL fluid film significantly improves overall manufacturing sustainability.

Machining Incoloy 800HT is inherently difficult due to poor thermal conductivity and strong work-hardening behavior, resulting in high cutting temperatures, accelerated tool wear, and degraded surface quality. Consequently, achieving improved machinability and dimensional accuracy requires the careful selection of cutting tools, optimized machining parameters, and effective cooling strategies. With increasing emphasis on sustainable manufacturing, environmentally friendly machining methods have become essential. Accordingly, this study evaluates the turning performance of Incoloy 800HT under three sustainable cooling conditions, namely minimum quantity lubrication (MQL), nitrogen (N2) gas, and a novel hybrid (MQL + N2 gas) system using an AlTiN-coated PVD carbide cutting tool. Pilot machining results indicated a clear performance advantage of the hybrid cooling system over individual MQL and N2 gas environments. Compared with N2 gas and MQL, hybrid cooling achieved reductions of 37.86 and 24.70

The objective of the research is to investigate the machinability characteristics of squeeze cast Al 7085 through carbide insert using Taguchi design of experiment and obtain the parametric optimization of responses such as noise emission and vickers micro-hardness. Squeeze cast sample surface generates micro-hardness of 139.3-149.6 HV whereas emissions of noise during dry turning are in the range of 68.1dB-77.1dB respectively. Impact of process parameters on noise emission are increasing in trend with 52.94%, 32.33% and 14.69% contribution at 95% confidence level. The contribution of cutting speed and depth of cut on micro-hardness of specimen are significant with 69.39% and 29.39% respectively. Prediction models through multiple linear regressions are found to be fitted well as coefficient of regression approaches one. During desirability multi-response optimization approach, the optimal parameters are found to be vc: 127.255 m/min, f: 0.05 mm/rev and ap: 0.1 mm with minimum values of micro- hardness and noise emission are 141.752 HV and 71.5405dB respectively. The outcome of the research has shown improvements in terms of good machinability and may be adopted in industries for green and sustainable manufacturing process.

This research focuses on synthesis, characterization and machinability analysis of ultrasonic aided stir-squeeze cast Al7075/Fe-Cr slag (5 % wt.) nanocomposite and squeeze cast unreinforced Al 7075 as comparative assessment. Ultrasonic vibration facilitated bubble generation, dendrite breakup, and homogeneous grain structure, while squeeze casting minimized shrinkage defects and improved intermetallic phase distribution of nanocomposite. Al7075/Fe-Cr slag nanocomposite exhibited higher hardness of 148 HV than unreinforced Al7075 as 118 HV and XRD confirm the nanocrystalline nature of the sample. From machinability assessment, VBc, Ra, T, Pc range from 0.036-0.061 mm, 0.28-1.16 mu m, 55.4-74.4 degrees C and 0.313-0.481 kW for Al7075 alloy and 0.038-0.07 mm, 0.52-1.6 mu m, 60.9-106 degrees C and 0.326-0.545 kW for Al7075/Fe-Cr nanocomposite respectively using uncoated carbide insert under dry environment. VBc and Ra are within criteria limit. The growth of BUE is less in nanocomposite (0.01 mm) than Al7075 alloy (0.025 mm). Machining of Al705/Fe-Cr nanocomposite shows improvement of dimensional accuracy as circularity and cylindricity values are less i.e. 0.004 and 0.025 (well within the tolerance zone of 0.1) as compared to Al 7075 alloy. Analysis of variance shows significance of the parameters and models developed through regression analysis. The optimal cutting parameters for Al7075 alloy have been found to be v = 60 m/min, f = 0.05 mm/rev and d = 0.1492 mm whereas v = 60 m/min, f = 0.05 mm/rev and d = 0.1384 mm for nanocomposite using desirability approach and validated with errors less than 5 %. The research signifies improvement in mechanical properties and machinability characteristics of nanocomposite towards green and sustainable manufacturing.

This work examines the machining responses of dry turning in ultrasonic-assisted stir-squeeze cast A356 hybrid nanocomposites reinforced with zirconia (ZrO2) and graphene oxide (GO). Accordingly, flank wear (VBc) ranged from 0.061 to 0.238 mm, influenced by abrasion, adhesion, built-up edge (BUE) formation, and diffusion mechanisms. Cutting speed had the most significant effect on flank wear (65.65%), followed by depth of cut (18.2%) and feed rate (11.13%), supported by a well-fitted regression model (R2 = 0.987; p < 0.05). Surface roughness (Ra) ranged from 1.733 to 7.012 μm, with cutting speed, feed rate, and depth of cut contributing 70.42%, 15.43%, and 9.56%, respectively. The cutting temperature was limited to 127 °C, primarily influenced by cutting speed (60.68%), whereas cutting power varied between 0.353 and 0.644 kW, mainly governed by cutting speed (68.71%) and depth of cut (25.92%). The chip morphology showed a segmented sawtooth pattern due to cyclic fracture initiation during material removal. Multi-criteria optimization using complex proportional assessment (COPRAS) identified v = 90 m/min, f = 0.06 mm/rev, and d = 0.1 mm as the optimal parameters, yielding a tool life of 22.6 min and a machining cost of INR 58.69 per item. This research is further focused on the implementation of different cooling lubrication techniques utilizing environmentally friendly cutting fluids, including Minimum-Quantity Lubrication and nano-MQL, among other types of environments.

The natural frequency responses of delaminated composite plates are investigated in this work. A flat laminated panel with defect in the form of delamination is simulated by employing ANSYS. For modeling of the composite panel, shell element with eight nodes is used. First, the convergence and validation study were performed. Further, numerical examples are presented and thoroughly discussed to elucidate the influence of size of the defect, modular ratio, aspect ratio, position and location of damage, and thickness-to-size ratio on the natural frequency of composite flat plates with defects.

Additive manufacturing (AM) of titanium alloy is emerging in various aerospace, marine, automobile, and medical implants applications because of high strength-to-weight ratio, enhanced mechanical, biocompatibility, wear, and corrosion resistance properties. AM processes are meant for near-net-shape components with higher mechanical properties due to difference in microstructure and lacks ductility. Optimization of additively manufactured (Amed) process parameters (laser power, scan speed, and layer thickness etc) and post-processing techniques such as stress relief heat treatment and sustainable machining are essentially required to achieve required microstructure, mechanical properties, and surface quality to meet the strict industrial tolerances. Micro-textured tools used with MQL improved the machinability of AMed Ti-6Al-4V reducing chip-tool contact by 38%, feed force by 28.9%, and surface roughness by 10.4% respectively. Reduction of crater wear for cryogenic machining than dry is 58% for direct metal laser sintering and 80% for both heat-treated DMLS and wrought Ti6Al4V alloys. Substantial improvements in drilling performance of Ti-6Al-4V have been observed under hybrid hBN-GNP nanofluids i.e. reductions in energy consumption by 28.8% (wrought) and 24.36% (wire arc additive manufacturing [WAAM]), along with improvement of surface finish by 48.57% and 49.47% respectively. Machining wrought Ti-6Al-4V yields 39.52% higher CO 2 emission than WAAM alloy as revealed from sustainability assessment under nanofluids application. It is worthwhile to analyse machining performance of AMed Ti-6Al-4V under novel sustainable cooling and lubrication environments such as nanofluids as it enhances thermo-physical characteristics and beneficial for sustainable manufacturing. Therefore, this paper reviews the fabrication, characterization, and sustainable machinability aspects of novel additive manufactured Ti-6Al-4V followed by challenges, research gaps, and future scopes.

Crude steel production is a complex process that consumes a lot of energy. However, this industry makes the largest contribution to the global economy. Steel finds its application in varied economic sectors ranging from construction works to consumer goods. To meet the needs and ensure easy availability of crude steel, its production becomes an important point of discussion. Steel production occurs via two major routes: the blast furnace basic oxygen furnace (BF-BOF) route, also known as the primary route, and the electric arc furnace (EAF) route, the secondary route. The paper primarily focuses on the processes and steps involved in the secondary route of steel making. This route makes use of scrap steel and contributes to the green economy. Further, the discussion extends toward the critical challenges and the reasons behind the production losses incurred during crude steel production.

Aluminium metal matrix composites are profoundly utilized in the aerospace, automobile, and marine sectors owing to their enhanced mechanical qualities, such as a high strength-to-weight ratio and corrosion resistance. This work focuses on analysing the influence of various machining parameters, specifically cutting speed, feed rate, and depth of cut, on noise emission and hardness during the turning process of Al7075 reinforced with 5 wt.% Fe-Cr composites. The studies were conducted on a CNC lathe using uncoated carbide insert employing a Taguchi L9 orthogonal array. At run 5, with a cutting speed of 110 m/min, a feed rate of 0.1 mm/rev, and a depth of cut of 0.3 mm, a greater noise level (79.5 dB) was obtained. The maximal hardness of 167 HV was reached at run no. 3 (cutting speed: 60 m/min, feed rate: 0.15 mm/rev, and depth of cut: 0.3 mm). The ANOVA findings indicated that cutting speed significantly influences both noise emission and hardness. Multiple linear regression models for both Ne and H are found to be significant as R2 approaches 1 with p-value <0.05 and obtained optimal parameters through desirability approach. The research findings provide insights for improving machining performance during the turning of AMMC composites.

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L16 orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT.

The present study deals with the machinability (flank wear, surface integrity, cutting temperature and power consumption) and sustainability (Carbon emission and noise emission) investigations during hard machining under dual nozzle assisted novel ZrO2 based nano fluid MQL condition considering Taguchi L27 OA design of experiment. 0.3 % wt. ZrO2 nanofluid MQL medium effectively reduce cutting temperature and found as 65 degrees C, which shows the effective cooling and lubrication properties of zirconia nanofluid. Improvement in surface quality has been observed with lower rate of flank wear. Depth of cut and feed are found to be the most influential factor affecting power consumption and carbon emission with contribution of 47.213 % and 60.861 % respectively. Noise emission mostly affected by the depth of cut with a contribution of 43.40 %. Optimal parametric condition yields d: 0.1 mm, f: 0.1 mm/rev and v: 80 m/min through WASPAS and sustainable towards manufacturing industries for cleaner hard machining.

This study involves the fabrication of a novel ultrasonically enabled stir-squeeze cast Al356/0.5 wt.% GO/1 wt.% ZrO2 hybrid nanocomposite with uniform distribution and investigates sustainable MQL precision turning using rice bran vegetable oil. The primary wear mechanisms identified include abrasion from hard reinforcements, adhesion resulting from the transfer or adherence of work material, and diffusion caused by elevated cutting temperatures through atomic migration and built-up edge (BUE). Depth of cut influences more on flank wear with 74.12%, cutting speed on surface roughness and cutting power with 80.12%, 76.9% and depth of cut and cutting speed on Ne with 48.44% and 47.08% contribution with segmented saw tooth formation. Utilizing COPRAS, optimal parameters are depth of cut of 0.1 mm; feed of 0.06 mm/rev, and cutting speed of 90 m/min. Tool life is 38.1 minutes at optimal run where circularity deviation is greatly reduced, i.e., 0.029, and total machining cost per cut is Rs. 52.21 only. An increase in energy and carbon footprint savings per annum, i.e., 21.65% and 22.11 kgCO(2)/kWh, occurred which enhances economic and ecological advantages toward sustainability.

This current study comprehensively examines the influence of MQL process parameters, including nozzle types (Nn), nozzle distance (Nd), and nozzle flow rate (Nf), on machinability and sustainability factors such as flank wear, surface integrity, cutting power, cutting temperature, and carbon emissions during hard turning, utilizing a Taguchi L18 orthogonal array design of experiments. The dual-jet nozzle has proven to be the most sustainable and efficient, with reductions of 8.43

The current study involves the analysis of machinability and sustainability metrics during hard turning through Taguchi L27 OA design of experiment under GO nano-cutting fluid MQL environment by low cost CVD coated carbide tools. This study examines machinability and sustainability factors, including flank wear, surface integrity, cutting temperature, power consumption, carbon emissions, and noise emissions, during hard machining operations. GO nano-cutting fluid provides superior cooling and lubrication facility at the cutting zone, resulting in lowest cutting temperatures of 55.2 degrees C and cutting noise levels of 69.3 dB throughout the investigation. At a parametric combination of d (0.1 mm), f (0.05 mm), and v (200 m/min), the lowest surface roughness was detected, exhibiting minimal surface defects with high precision as the lowest circularity and cylindricity error obtained at this run. Feed (57.30 %) and depth of cut (49.51 %) significantly affect carbon emissions and noise emissions, respectively. Cutting speed is the primary factor influencing flank wear and temperature, with a contribution rate of 61.69 % and 47.50 %, respectively. Feed greatly influences surface roughness with a contribution rate of 44.95 %, whereas depth of cut predominantly affects cutting power with a contribution rate of 51.92 %. The multi-response optimization implementing WASPAS, followed by the entropy method, yields an optimal parametric combination of d (0.1 mm), f (0.05 mm), and v (80 m/min) within the examined range for the most favorable solution considering both machinability and sustainability.

MgO nanofluid exhibits excellent thermo-physical characteristics and has been effectively applied in heat exchanger research. Using MgO-based nano-cutting fluid as a coolant in machining hardened steel introduces a unique feature, as this cutting fluid has yet to be explored in machining research. In this study, MgO nano-cutting fluid was used for turning hardened AISI D2 steel (57 ± 1 HRC) with a dual jet nozzle MQL system and a CVD-coated (TiCN/Al2O3) carbide tool. Three distinct weight-based concentrations of nanofluid (0.5, 1, and 1.5

Sustainability in machining is a critical aspect of modern manufacturing, aimed at reducing the environmental impact of production processes while ensuring economic and social benefits. This study investigates the machinability of Ti6Al4V alloy for optimal machining power requirement and optimization of minimum quantity lubrication (MQL) machining using advanced machine learning techniques. The primary objective is to minimize the machining power, a critical factor in manufacturing high-strength alloys. Two machine learning-based regression models—namely support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS)—are employed to predict machining power based on input parameters such as cutting speed (v), feed rate (f), and depth of cut (d). The predictive capability of the models was to assess their predictive accuracy and generalization capability. The process is optimized using a popular teaching–learning-based optimization (TLBO) algorithm to obtain the best combination of process parameters to minimize the machining power. The algorithm yielded a minimum machining power of 334.24 W for 71.16 m/min cutting speed, 0.056 mm/rev feed, and 0.2 mm depth of cut. The evolutionary optimization framework successfully identifies optimal parameter settings, substantially improving energy efficiency and machining performance. Further, microstructural studies of chips and confirmation tests were conducted to validate the optimum process parameters.

This study compares the hard turning performance under dual-nozzle minimum quantity lubrication (MQL) using mineral oil and 1-butyl-3-methylimidazolium chloride-based ionic fluids. Key performance indicators, including tool life (based on tool wear), surface roughness, cutting power, cutting temperature, cutting sound, carbon emission, and circularity error, were evaluated to assess manufacturing sustainability. The results revealed that ionic fluid-assisted MQL significantly outperformed mineral oil, improving tool life by 28.75% and reducing surface roughness by 5.58%, attributed to the superior lubrication and cooling ability of ionic fluids. Additionally, after 85 min of machining, the power consumption and carbon emission were greatly reduced under ionic fluid conditions, indicating a lower environmental impact. For precision machining concerns, the ionic fluid proved more favorable, as circularity error under mineral oil conditions was 2.67 times higher than with ionic fluids. The weighted Pugh matrix awarded ionic fluid a higher sustainability score (+7) than mineral oil (+1), establishing it as the superior cooling option for hard turning, enhancing sustainability in machining difficult-to-cut metals.

Ionic liquids have recently acquired popularity as excellent lubricant additives due to their exceptional tribological and thermal properties. Hard turning of hardened steels (>45 HRC) poses challenges like high cutting temperatures, leading to rapid tool wear and increased costs. This study developed a unique cutting fluid using 1-butyl-3-methylimidazolium chloride ionic liquid in concentrations of 1, 3, and 5 wt.% to improve the machinability of AISI D2 hardened steel (57 ± 1 HRC) with a low-cost CVD-coated carbide tool. Results showed reduced cutting temperature, tool wear, and power consumption. The tool life was 51.5 min for flank wear (VBc = 0.2 mm) and 40 min for surface roughness criteria. Circularity error and carbon emissions were 0.02 mm and 1.827 kg CO 2 , respectively, with a machining cost of $ 0.909 per cut, making it an economical, and environmentally sustainable approach for hard turning.