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The cutting fluid's lubrication affects a workpiece's surface finish and cutting tool lifespan during turning. To optimize machine performance, appropriate lubrication is needed. Empirical experiments examined how machining factors affected a redesigned single-point cutting tool. Texturing the tool's rake surface without altering its physical qualities was achieved utilizing super-drilling and laser engraving technologies. The goal was to build a surface junction that would keep cutting fluid in contact with the tool longer, improving lubrication and cooling. Both standard and customized tools were used to compare cutting force, temperature, power usage, and surface polish. Magnified pictures from the scanning electron microscope were utilized to analyze tool wear in different places. The improved tool dramatically lowered mean cutting force, heat output, and power consumption in experiments. The textured tool produced continuous chips instead of discontinuous/burnt chips due to increased friction at the tool–chip interface. The updated tool improved lubrication and cooling with cutting fluid. [ABSTRACT FROM AUTHOR]

The development of many engineered product applications for automobiles and aircraft parts has initiated the search for novel materials as alternatives to metal matrix composites (MMCs). Natural-fiber-reinforced polymer composites offer distinct advantages such as biodegradability, eco-friendliness, flexibility, low density, and higher specific strengths, etc. This study focuses on natural-fiber (hemp and banana)-fabric-reinforced polymer composites suitable for exterior-engineered parts. The hand lay-up process is used to fabricate these hybrid composites. Exterior-engineered products are highly susceptible to moisture, which can deteriorate their mechanical performances, including their tensile and flexural strength, thereby affecting the durability of the hybrid composites. Therefore, the hybrid composites are subjected to water absorption tests, where samples are immersed in distilled water for week-long intervals. After each interval, the water-absorbed specimens are tested for their tensile and flexural characteristics as per ASTM D-3039 and ASTM D-790, respectively. The moisture treatment had a notable impact on the composite materials, causing a slight decrease in the tensile strength by 2% due to the diminished lateral strength in the interlaminar fibers. Contrary to expectations, the flexural strength of the composites improved by 2.7% after the moisture treatment, highlighting the potential of the moisture treatment process to enhance the elastic properties of such composites. The dimensions of the specimens changed after the water immersion test, resulting in increased longitudinal and decreased lateral dimensions. The surface morphologies of the composite failure samples showed fiber delamination, fiber breakage, voids, and matrix fractures. [ABSTRACT FROM AUTHOR]

This review article focuses on the aluminum-based metal matrix composites (Al-based MMCs). Studies or investigations of their mechanical and tribological properties performed by researchers worldwide in the past are presented in detail. The processing techniques and applications for Al-based MMCs are also documented here. A brief background on the composite materials, their constituents, and their classification, as well as the different matrix materials and particulates used in Al-based MMCs, can be found in this review. Then, an overview of dual-particle-size reinforced composites, heat treatment of Al alloys, and temper designations used in heat treatment are also included. In addition, the factors influencing the mechanical and wear properties of Al-based MMCs are discussed. The primary objective is that both present and future researchers and investigators will be assisted by the comprehensive knowledge compiled in this article to further explore and work towards the betterment of society in general. [ABSTRACT FROM AUTHOR]

Pure aluminium poses inferior properties that limit its use in load-bearing applications. Reinforcing multiwall carbon nano-tube (solid lubricant) and graphene to aluminium matrix offers better (antifriction, hardness, and wear resistance) properties in composites for such applications. A stir casting processing route is employed to prepare the hybrid composite (aluminium-multiwall carbon nanotube-graphene Al-MWCNT-GR). The Taguchi L16 experimental matrix representing four variables (percent reinforcement of graphene, die temperature, melt temperature, and stir speed) operating at four levels were studied to analyze and obtain higher hardness and low wear rate in hybrid composites. Percent reinforcement of graphene showed maximum impact, and die temperature resulted with the least contribution towards both the responses. Criteria importance through intercriteria correlation (CRITIC) method is applied to determine the weight fractions (importance) for hardness and wear rate equal to 0.4752 and 0.5482, respectively. Grey relational analysis (GRA) and multi-objective optimization by the ratio analysis (MOORA) method converts multiple objective functions into a single objective function with weight fractions assigned to each output. Taguchi-CRITIC-MOORA outperformed the Taguchi-CRITIC-GRA method, which could result in 31.77% increase in hardness and a 36.33% decrease in wear rate compared to initial conditions. The optimal conditions ensure a dense microstructure with minimal pores, result in enhanced properties compared to that obtained for initial and average stir casting conditions. The worn-out surface results in a few thin and slender grooves between tracks with less crack propagation, ensuring self-lubrication in composites fabricated with the optimized condition. The better properties resulted in the hybrid composites correspond to optimized stir casting conditions and can be implemented in industries for large-scale applications. [ABSTRACT FROM AUTHOR]

The development of the best properties in polyester composite from pineapple leaf fiber (PALF) as a reinforcing material is a subject of interest. The properties of PALF are reliant upon fiber length, wherein technical difficulties in production of long fibers and processing for better characteristics in polyester composites possess inherent challenges. The PALFs are subjected to silane treatment for altering fiber properties. This research attempts to analyze the impact of silane-treated PALF with varying fiber lengths (5, 10, 15, 20, and 25 mm) on the performance of natural fiber composites (NFC) properties. Open mold and hand lay-up techniques were employed to develop the polyester composites. The prepared PALF-based polyester composites were examined for different properties (impact, flexural, tensile strength, and wear rate). Coefficient of friction and wear studies are performed on the prepared composites subjected to different loads (10, 20, and 30 N) via a pin on disc test rig. Polymer composite fracture surfaces were analyzed to observe the interfacial bonding between fibers and matrix via scanning electron microscopy (SEM). SEM results showed that the application of silane treatment resulted in better surface topography (fiber length of 5–10 mm showed smooth surface resulted in crack proliferation possessing low fracture toughness of 15–32 MPa; whereas a 15–20 mm fiber length resulted in better fiber–matrix bonding, improving the fracture toughness from 42–55 MPa) as a result of change in chemical structure in PALF. The 20 mm length of PALF resulted in better properties (flexural, tensile, impact, and wear resistance) which are attributed to fiber–matrix interfacial bonding. These properties ensure the developed polymer composites can be applied to walls, building insulation, and artificial ceilings. [ABSTRACT FROM AUTHOR]

The present work aims at cost-effective approaches for biodiesel conversion from niger seed (NS) oil by employing the transesterification process, Box–Behnken design (BBD), and artificial intelligence (AI) tools. The performances of biodiesel yield are reliant on transesterification variables (methanol-to-oil molar ratio M:O, reaction time Rt, catalyst concentration CC, and reaction temperature RT). BBD matrices representing the transesterification parameters were utilized for experiment reductions, analyzing factor (individual and interaction) effects, deriving empirical equations, and evaluating prediction accuracy. M:O showed a dominant effect, followed by CC, Rt, and RT, respectively. All two-factor interaction effects are significant, excluding the two interactions (Rt with RT and M:O with RT). The model showed a good correlation or regression coefficient with a value equal to 0.9869. Furthermore, the model produced the best fit, corresponding to the experimental and predicted yield of biodiesel. Three AI algorithms were applied (the big-bang big-crunch algorithm (BB-BC), firefly algorithm (FA), and grey wolf optimization (GWO)) to search for the best transesterification conditions that could maximize biodiesel yield. GWO and FA produced better fitness (biodiesel yield) values compared to BB-BC. GWO and FA experimental conditions resulted in a maximum biodiesel yield equal to 95.3 ± 0.5%. The computation time incurred in optimizing the biodiesel yield was found to be equal to 0.8 s for BB-BC, 1.66 s for GWO, and 15.06 s for FA. GWO determined that the optimized condition is recommended for better solution accuracy with a slight compromise in computation time. The physicochemical properties of the biodiesel yield were tested according to ASTM D6751-15C; the results are in good agreement and the biodiesel yield would be appropriate to use in diesel engines. [ABSTRACT FROM AUTHOR]