Clean technological machining operations can improve traditional methods’ environmental, economic, and technical viability, resulting in sustainability, compatibility, and human-centered machining. This, this work focuses on sustainable machining of Al-Mg-Zr alloy with minimum quantity lubricant (MQL)-assisted machining using a polycrystalline diamond (PCD) tool. The effect of various process parameters on the surface roughness and cutting temperature were analyzed. The Taguchi L₂₅ orthogonal array-based experimental design has been utilized. Experiments have been carried out in the MQL environment, and pressure was maintained at 8 bar. The multiple responses were optimized using desirability function analysis (DFA). Analysis of variance (ANOVA) shows that cutting speed and depth of cut are the most prominent factors for surface roughness and cutting temperature. Therefore, the DFA suggested that, to attain reasonable response values, a lower to moderate value of depth of cut, cutting speed and feed rate are appreciable. An artificial neural network (ANN) model with four different learning algorithms was used to predict the surface roughness and temperature. Apart from this, to address the sustainability aspect, life cycle assessment (LCA) of MQL-assisted and dry machining has been carried out. Energy consumption, CO₂ emissions, and processing time have been determined for MQL-assisted and dry machining. The results showed that MQL-machining required a very nominal amount of cutting fluid, which produced a smaller carbon footprint. Moreover, very little energy consumption is required in MQL-machining to achieve high material removal and very low tool change.

The growing demand for fiber-reinforced polymer (FRP) in industrial applications has prompted the exploration of natural fiber-based composites as a viable alternative to synthetic fibers. Using jute–rattan fiber-reinforced composite offers the potential for environmentally sustainable waste material decomposition and cost reduction compared to conventional fiber materials. This article focuses on the impact of different machining constraints on surface roughness and delamination during the drilling process of the jute–rattan FRP composite. Inspired by this unexplored research area; this article emphasizes the influence of various machining constraints on surface roughness and delamination in drilling jute–rattan FRP composite. Response surface methodology designs the experiment using drill bit material; spindle speed; and feed rate as input variables to measure surface roughness and delamination factors. The technique of order of preference by similarity to the ideal solution method is used to optimize the machining parameters; and for predicting surface roughness and delamination; two machine learning-based models named random forest (RF) and support vector machine (SVM) are utilized. To evaluate the accuracy of the predicted values; the correlation coefficient (R2); mean absolute percentage error; and mean squared error were used. RF performed better in comparison with SVM; with a higher value of R2 for both testing and training datasets; which is 0.997; 0.981; and 0.985 for surface roughness; entry delamination; and exit delamination; respectively. Hence; this study presents an innovative methodology for predicting surface roughness and delamination through machine learning techniques.

The focus of this study was to investigate the potential of using waste materials like, waste glass powder and wood flour as fillers in polypropylene thermoplastic, and to assess their mechanical, morphological and thermal properties, as a means to add value to these wastes. Waste glass powder, with particle sizes of 125 μm, 63 μm, and 45 μm, was used as a filler material in the polypropylene matrix, along with wood flour particles of 250 μm in size. After identifying the optimal particle size (45 μm) sample, the WGP content was increased from 10 wt% to 15 wt% and 20 wt%. A four-step procedure was followed to create the composites, involving a kinetic mixer, sieve analysis, vacuum oven drying, and a compression molding machine. The results demonstrate that flexural strength and flexural modulus were highest in samples composed of 10 wt% and 15 wt% glass powder with a particle size of 45 μm. The tensile strength was highest in 100 wt% PP but the tensile modulus in 20 wt% glass powder with a 45 μm particle-sized sample. The maximum rebound hardness was also found in 100 wt% PP. The TGA results showed that 15 wt% WGP with a particle size of 45 μm sample was most thermally stable. A higher percentage (20 wt%) addition of waste glass powder caused non-uniform dispersion and the agglomeration of the fillers in the polymer matrix composite, which was visible in the microscopic analysis. This study found that the mechanical, morphological and thermal properties of the composite were improved by using finer particles and certain filler percentages.

Heat-treated steel is widely used in industrial applications due to its high strength and other desirable mechanical qualities. Grinding, which requires a lot of power and is expensive, is typically used to harden machining. In recent times, hard machining has emerged as a viable alternative to grind in select applications. In this investigation, turning operations with a carbide insert (CNMA 120408-KR3215) were carried out on SKD 11 (53 HRC) hardened steel. A total of nine machining tests were completed using the L₉ orthogonal array. The response variables considered in this study were surface roughness (Ra) and material removal rate (MRR). The analysis of the signal to noise ratio reveals that the optimal combination of cutting process parameters for achieving a desired surface roughness consists of a cutting speed of 119m/min, a feed rate of 0.11mm/rev, and a depth of cut of 0.2mm. The contribution of each process parameter to the machining performance of the carbide tool-work piece combination is determined through the use of ANOVA. Depth of cut has the greatest impact (57.33) to MRR, while feed rate has the highest contribution (82.15) to Ra. Moreover, desirability function analysis (DFA) was conducted to optimize the multiple responses. DFA suggested that, to attain a satisfactory response to the output parameters, higher range of cutting speed, depth of cut, and lower range of feed rate are appreciable; therefore, the analytical findings suggest that a cutting speed of 189m/min, feed rate of 0.11mm/rev, and a depth of cut of 0.5mm can induce a favorable Ra of 0.971μm and MRR of 10.248cm³/min. In hard machining, cutting speed has a bigger influence on surface finish than feed rate.