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A tool that will last is crucial for refining machining processes, influencing the quality of products, and reducing the expenses of making them. Previous research has demonstrated that several factors influence tool longevity, including the cutting depth, speed, the feed rate, the properties of the tool’s material, and those of the workpiece being machined. Understanding precisely how each of these factors impacts tool life is essential for refining processes and choosing the right tool. There are established mathematical models for estimating the tool’s lifespan, particularly for CBN tools when performing turning operations. Nonetheless, understanding the link between tool lifespan and cutting speed is challenging, given that it does not follow a linear pattern. Traditional methods for determining the equation for a tool lifespan using cutting speed often necessitate conducting tests at various speeds, which may not provide the statistical foundation required by the design of experiment (DoE) techniques. In this research, we delve into the complex relationship between tool lifespan and cutting speed through experiments guided by the Taguchi method and artificial neural network (ANN) models. Several case studies have been conducted to test the practicality and effectiveness of this method in representing complex tool lifespan‐cutting speed relationships.

Several previous studies have addressed the issues raised by the large bulk and slow speed of inertial actuators. The current study used the stick–slip drive theory to construct a compacted piezo‐inertia actuator to overcome these limitations. A cantilever beam integrated into a monolithic elastomer pushes itself with a high‐velocity lateral motion. This research thoroughly examines the design of the actuator and its underlying operating principles. The monolithic elastomer was analyzed using finite element analysis software to determine the appropriate geometry. The mechanical properties of the actuator were obtained by building a prototype and subjecting it to a variety of experimental tests. According to the test data, the actuator had a maximum speed of approximately 26.07 mm/s, a maximum load force of approximately 1.89 N, and a minimum step size of approximately 0.51 μ m. The constructed monolithic elastomer and a cantilever beam actuator were sleek and small, with excellent output characteristics.

Laser welding stands out as one of the most precise and efficient manufacturing techniques, with its ability to generate minimal heat‐affected zones and limit material distortion. This study introduces a cutting‐edge neural network–based predictive model designed to estimate tensile strength and welding deformation in laser welding operations. By incorporating three critical input parameters, laser incident angle, laser velocity, and laser power, the model harnesses the power of a neural network to refine process optimization and elevate the quality of welded joints. Among the tested models, the Bayesian regularization (BR) model demonstrated superior accuracy, achieving a remarkably low mean absolute error (MAE) of just 0.0001982. In contrast, the Levenberg–Marquardt (LM) model yielded an MAE of 89.29, while the scaled conjugate gradient (SCG) model recorded an MAE of 41.67. These findings underscore the effectiveness of the BR model in enhancing predictive accuracy for laser welding applications.

This study evaluates a single‐cylinder hydrogen port injection (HPFI) Diesel engine by systematically varying the hydrogen port‐injection timing and duration to identify settings that enhance overall operation. The objective is to enhance overall engine performance, strengthen combustion characteristics, and reduce harmful exhaust emissions while maintaining a constant operating speed of 1500 rpm under load conditions ranging from 0% to 100%. A structured test matrix was employed in which the hydrogen injection timing was varied from 6° BTDC to 12° ATDC, and the injection duration was adjusted between 9° and 54° crank‐angle degrees. This operating window enabled a systematic assessment of combustion behavior under dual‐fuel conditions. The most favorable response was obtained when hydrogen was introduced at TDC with a 54° CA injection duration. At this setting, the HPFI diesel engine achieved a brake thermal efficiency of 28.58% at 75% load, a notable improvement over the 18.86% observed with pure diesel. The overall fuel demand also decreased, with TEC dropping from 1.01 kJ/h under diesel operation to 0.66 kJ/h at full load. Emission measurements further highlighted the advantages of hydrogen enrichment, including reduced smoke opacity, improved combustion phasing, and a slight rise in peak cylinder pressure attributable to the faster premixed burn of hydrogen. At 75% load, NOx levels fell from 505 ppm with diesel to 433 ppm under hydrogen‐assisted operation, indicating reduced thermal NOx formation. These findings demonstrate that carefully tailoring hydrogen port‐injection timing and duration can simultaneously enhance performance, combustion efficiency, and emissions. Optimizing hydrogen port injection in diesel engines significantly improves efficiency and reduces emissions, demonstrating a sustainable dual‐fuel strategy for cleaner transportation solutions and lower environmental impact.

One of the metal additive manufacturing techniques, Laser Powder Bed Fusion (LPBF), is utilised to fabricate several metal composites, including S30 and AlSi10Mg, which are extensively utilised in the automotive and aerospace sectors. The main objective of this manufacturing is to achieve high surface quality for the complex dimensional parts specially heat exchangers and turbomachinery components. In this research, the 30 datasets of S30 alloy cube samples are collected from literature for analysis. The supervised classification algorithm (Bayesian learning) is used for the analysis and surface roughness prediction in term of current, Line offset and scans speed. The manual calculations of Bayesian leaning are performed to obtain the probability prediction for the objective function. Then the same input and output parameters are trained and modelled by ANN classifier using sklearn library from python. The performance metrics for classifier such as sensitivity, specificity, precision and accuracy are calculated for Bayesian learning and compared with ANN classifier. ANN classifier Prediction of performance characteristic gave accurate results which plays very important role in LPBF method because of high experimentation cost.

The experimental studies of drop weight and Izod impact test of FFAL (flax fibre aluminium laminate) are presented in the research. The materials taken for the study are plain woven flax and aluminium lamina with epoxy resin as an adhesive material. Alkaline with diluted epoxy chemical treatment is added to flax, and aluminium is treated with NaOH to strengthen the binding between the fibres and metal. The FFAL was prepared by hand layup method followed by compression moulding technique. The low-velocity and Izod impact tests were conducted for treated and untreated samples. The outcomes exhibited that the increase in the impact strength of 40% and energy absorption capacity of low-velocity impact strength also improved for the treated sample. The experimental damage of low-velocity and Izod impact test results are also examined.

Natural fibre metal laminates are widely being researched for their mechanical properties and in the making of metal and polymer matrix composites. The hybrid metal–natural fibre laminate composites have largely improved mechanical properties and can withstand greater forces without failing. In this study, Aluminium 6082 is the metal chosen, and flax fibre is used to reinforce the Aluminium 6082. However, natural fibre and metal fibre have difficulty bonding with a polymer matrix to enhance their mechanical performance. Hence, alkaline surface treatment method is applied for flax and metal fibre to increase the adhesive strength of the material. Fabricate the treated and untreated samples using hand layup followed by compression moulding technique. The micromechanical performance including tensile strength and flexural strength of treated and untreated samples was investigated. It was found that untreated flax fibre-reinforced aluminium laminate does not have proper interfacial adhesion. The resulting fabricated sample with the treated exhibit there is an improvement in tensile and flexural properties and microstructural characterisation also examined and it showed the interfacial adhesion mechanism affected the improvement in the properties.

In recent times, the growing popularity of joint-free mechanical systems and structures is attributed to advancements in 3D printing technology. Unlike traditional mechanically joined systems, 3D-printed products require fewer fasteners. However, the widespread adoption of additive manufacturing in the mechanical industries is hindered by limitations in handling various engineering materials. Currently, only a restricted range of ductile and plastic materials is utilized in additive manufacturing processes. This study aims to replace adhesive bonding and bolt joints with an innovative approach involving equivalent geometrical layers. The strength of these joints is intended to be achieved through careful consideration of layer thickness and geometry. The research investigates the strength of conventional lap joints, such as adhesive-bonded or bolted joints, across different materials. Finite element models of these ASTM specimens will be developed in ANSYS for static analysis and comparison. The ultimate goal is to establish an equivalent design procedure that replaces traditional joints with layers of materials through the additive manufacturing process. To validate this approach, a quadcopter structure was designed using 3D printing technology, fabricated with ABS and PLA materials, assembled, and flight-tested to achieve a thrust-to-weight ratio of nearly two. The successful validation of the design demonstrates that 3D-printed additive manufacturing is a valuable technology for constructing lightweight and high-performance UAV structures. Notably, the quadcopter frame was produced as a single component, streamlining the assembly process compared to traditional assemblies consisting of eight to ten parts.

This research discovered how borate‐based germanium‐doped Bioglass was made using a microemulsion‐assisted sol–gel technique for possible use in biology. The created Bioglass showed good network structure and open spaces. FTIR spectroscopy proved the existence of important groups, showing that germanium was successfully mixed into the borate structure. Placing the Bioglass in a solution that mimics body fluids helped it form a layer of hydroxyapatite on its surface, as shown by FTIR, XRD, and FESEM tests, indicating it was very compatible with biological systems. Toxicity assessments validated the nontoxic nature of the material at concentrations below 2 mg/mL. Furthermore, the bioglass exhibited antimicrobial activity against Staphylococcus aureus, Klebsiella pneumoniae, and Pseudomonas aeruginosa, with minimum inhibitory concentrations of 5, 5, and 2.5 mg/mL, respectively, highlighting its suitability for biological applications. This study proposes further investigations into repeatability, scalability, and clinical compliance, which are warranted to facilitate the translation of these promising germanium‐doped borate bioglass materials for therapeutic and regenerative medicine applications. This study successfully synthesized germanium‐doped borate bioglass using a microemulsion‐assisted sol–gel method, demonstrating exceptional bioactivity, biocompatibility, and antimicrobial properties. The material induced hydroxyapatite formation, exhibited low cytotoxicity at concentrations below 2 mg/mL, and showed antibacterial efficacy, highlighting its potential for biomedical applications in tissue engineering and regenerative medicine.

At high temperatures, coatings provide a protective scale development on surfaces to maintain long-term stability. In the current study, ASTM-SA210-Grade A1 (GrA1) and ASTM-SA213-T-11 (T11) boiler tube steels were coated with NiCrAl alloy with high-velocity oxy-fuel (HVOF) to prevent oxidation and hot corrosion. For hot corrosion and oxidation, 50 cycles at 900°C were taken into account. Additionally, tests of hot-corrosion behavior were conducted in an atmosphere containing molten salt (Na 2 SO 4 -60%V 2 O 5 ), while tests of oxidation behavior were conducted in static air. The kinetics of oxidation were calculated using the thermogravimetric method. Using XRD, EPMA, and SEM/EDAX methods, the produced oxide scales were characterized. The oxidation rate of NiCrAl-coated steels was found to be lower than that of uncoated steels. The coated steels subjected to oxidation in air exhibit slow scale growth kinetics and oxides of α-Al 2 O 3 and Cr 2 O 3 on the outermost surface, while accelerated oxidation caused by the molten salt exhibits metastable Al 2 O 3 . Along the nickel-rich splat boundary, Cr and Al were formed a preferential oxidation, which prevents other oxygen from entering the coating via pores and voids, resulting in steady-state oxidation.