Recent Advances in Materials and Manufacturing Science

The impact of MWCNT on the mechanical characteristics of glass fiber-reinforced polymer (GFRP) composites is the focus of the current investigation. The fabrication technique includes hand layup molding, ultrasonication, and magnetic stirring to prepare the MWCNT/epoxy/fiber multi-phase composites. Samples were cut and cured using the appropriate post-processing to get them ready for mechanical characterizations in compliance with ASTM standards. Furthermore, the mechanical properties improved with the inclusion of MWCNT. In comparison with pure GFRP, the tensile strength of GFRP composites adding 0.1% and 0.2% MWCNT increased by 6.2% and 21%, respectively. As seen in the morphological fractography, improvements in the interfacial adhesion between the glass fiber and the new matrix provided additional support for the results.

In contrast to typical metals and alloys, magnesium (Mg) metal composites have a lower density, superior mechanical properties, high corrosion resistance, and variable thermal expansion coefficients. They are commonly used in the aerospace and transportation industries. Mg metal matrix composites are employed for enhanced mechanical and wear/abrasion characteristics. One approach to improve the tensile, chemical, and wear properties of various Mg-based composites is to incorporate reinforcements such as Silicon Carbide, Boron Carbide, Alumina, metallic glass, and others. When graphite is used, the tensile strength and hardness of the composite decrease, as does the wear rate. The performance of Mg composites is influenced by the reinforcement materials used and the consistency of the matrix material. This article focuses on the processing techniques, types of fillers, and tribomechanical properties of Mg metal matrix composites. The review highlights various research studies on the latest trends, emphasizing the combination of reinforcements with an Mg matrix. To lower production costs and improve the tensile properties of Mg hybrid composites, fillers such as rice husk, fly ash, and other waste materials can be employed.

Analyzing the microstructural behavior of materials operating under fluctuating temperature conditions has become essential, as these fluctuations significantly impact material properties at both micro and molecular levels. Studying stainless steel is essential since it is a material that is used extensively in a variety of temperature ranges. Investigating how heat treatment affects the mechanical properties, microstructural growth, and deformation behavior of grade 304 stainless steel is the goal of this study. Due to its widespread usage and good performance in temperature-variable situations, stainless steel grade 304 is employed in this study for heat treatment and microstructural examination at room temperature and higher temperatures. The full annealing was conducted at room temperature, as well as at 1010 and 1120 °C. The annealing process led to small, redistributed grains in the deformed material, which improved its properties compared to untreated SS304. Post-annealing, the material exhibited an increase in fine, equiaxed grains and a reduction in large, elongated grains. The annealed samples at 1010 and 1120 °C demonstrated microstructural changes along cellular walls, where a limited number of dislocations unraveled. This suggests that annealing may prompt dislocation along cellular walls to move to more energetically stable regions, fostering the formation of sub-grain boundaries and enhancing the mechanical characteristics of SS304.

Aluminum composites strengthened with ceramics have superior mechanical and thermal properties when compared to monolithic aluminum alloys. But poor wettability and undesirable chemical reactions are a major problem while fabricating such composites. In this study, an attempt is made to enhance wettability via metal coated ceramic particles. Silicon carbide (SiC) particles were coated with nickel-phosphorous (Ni–P) layer through electroless nickel-phosphorous plating method. The incorporation of plated powders with different reinforcement levels, specifically 5, 10, and 15 weight percent, is utilized to create Aluminum reinforced Ni–P plated SiC composites named as Al/Ni-PSiC and comparisons are drawn with pure Al and Al/SiC sintered composites. To probe the surface and the interface region, techniques such as SEM, EDS spectra, and X-ray diffraction (XRD) are utilized effectively. XRD is employed to determine the presence of any intermetallic compounds and potential contaminants. Mechanical studies, including hardness, ultimate tensile strength (UTS), and yield strength (YS), have been noted to rise with the rising content of Ni–P plated SiC. Ni–P plating is found favorable for avoiding agglomeration, ensuring uniform dispersion, less interaction with aluminum to advance any intermetallic compounds like Al4C3 phase.

This research focuses on contemporary strategies and methodologies in operations management, emphasizing resource allocation optimization. Key factors like demand variability, production capacity, and technological advancements are examined. The study explores decision-making processes in dynamic operational environments, balancing conflicting objectives such as cost minimization and service level maximization. It delves into hybrid models, specifically those combining artificial neural network (ANN) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), evaluating adaptability, efficacy, and associated advantages and limitations. The research provides insights guiding the feasibility and applicability of these models for resource optimization. Successful applications of hybrid ANN and TOPSIS models across industries are investigated, offering real-world examples, strategic applications, best practices, and challenges. The paper contributes valuable insights for organizations implementing similar approaches in operations management strategies.

This review focuses on applying nanofluids in Minimum Quantity Lubrication (MQL) for machining processes, emphasizing their potential to enhance cutting performance while reducing environmental and economic costs. The objective is to analyze the advancements in nanofluid-based MQL systems, highlighting their impact on improving tool life, surface quality, and thermal management compared to conventional lubricants. The novelty of this study lies in its comprehensive evaluation of various nanoparticles, such as metal oxides and carbon-based materials, and their distinct effects on lubrication and cooling mechanisms in different machining techniques. At an optimal nanoparticle concentration of 3%, the cutting temperature is reduced by 50% and lubrication efficiency is enhanced by 60%. By consolidating experimental findings and addressing implementation challenges, this review offers valuable insights into optimizing nanofluid formulations for sustainable and efficient machining operations.

Diamond turning is used in many industrial applications to achieve a very fine surface quality and high form accuracy. Machining of soft material is challenging as material response affects the surface roughness generation mechanism. Magnesium alloys are widely used materials in the biomedical industries for implants, automobile industries, and lightweight material application in electronic, manufacturing, and aerospace industries. In the present work, diamond turning of magnesium alloy AZ31B is carried out using polycrystalline diamond tool. In the machining parameters, spindle speed and depth of cut remain fixed, whereas the feed rate is changed. The diamond turning is performed under the wet machining condition. The effect of feed rate on the quality of diamond-turned surfaces is analyzed. The minimum roughness (Ra) of 164 ± 21 nm is achieved on the surface diamond-turned with a mid-level feed rate. Furthermore, the concentric tool marks, scratches, and surface defects related to inclusions are visible on the machined surface. The future work can be carried out with a detailed analysis of the correlation of surface roughness generation mechanism with process parameters.

Hexagonal boron nitride (hBN), a two-dimensional (2D) nanomaterial renowned for its exceptional chemical and mechanical stability, has emerged as a promising candidate for atomically thin coatings, nanofluidic, and water desalination. In many of these uses, hBN surfaces interact directly with water. This study explores the frictional properties of 2D hBN surfaces, specifically nanoslits and nanopore-containing nanosheets, in the context of water desalination. Through molecular dynamics (MD) simulations and change in free energy due to infinite separation from molecular contact of hBN and water at interface, we demonstrate that nanoslits exhibit higher friction compared to nanopore-embedded nanosheets. These findings highlight the critical need to understand the frictional behavior of hBN in water desalination applications, offering insights for optimizing its performance in such systems.

Additive manufacturing (AM) is revolutionizing production processes by enabling the creation of complex geometries with high precision and reduced material waste. Fused deposition modelling is most extensively used method of additive manufacturing to build the parts. This study investigates the influence of key process parameters like layer thickness, printing speed, infill density, infill style, and printing temperature on the printing time, dimensional accuracy and material consumption of 3D-printed parts using PC-ABS with graphene. Specimen were prepared according to “L27” orthogonal array. The Taguchi method was employed to identify the optimal settings that enhance dimensional accuracy while minimizing material usage and printing time. Taguchi method also used to determine most influential factor for performance parameters.

The evolving landscape of aerospace engineering continually desires such a material that pertains advanced mechanical strength, durability, and thermal stability. This paper explores the development and characterization of hybrid polymer-based matrix nanocomposites (HPMNCs), where epoxy is the main constituent, tailored for applications in industry of aerospace. Through a synthesis of epoxy resin with various nanofillers, including carbon nanotubes and graphene oxide, we fabricate a composite material designed to meet the stringent requirements of aerospace environments. The study systematically appraises the mechanical properties, for example, impact resistance, tensile strength, and flexural modulus, alongside thermal behaviors, including glass transition temperature as well as thermal conductivity. The inclusion of nanoscale reinforcements within the epoxy matrix significantly enhances its mechanical properties because of active load transfer and the inherent strength of the nanofillers. Thermal analysis reveals that these nanocomposites exhibit improved thermal stability and reduced thermal expansion coefficients, essential for maintaining structural integrity under the thermal extremes of aerospace operations. Scanning electron microscopy (SEM) analysis provides insights into the dispersion quality of nanofillers and the atomic coordination between the matrix and reinforcing elements. The experimental results are corroborated with theoretical models to predict the behavior of these composites under various loading and environmental conditions. The findings suggest that epoxy-based HPMNCs not only fulfill but exceed the performance expectations for aerospace materials, offering potential for significant advancements in aerospace design and manufacturing. This study lays the groundwork for further research into the scalability of these materials and their practical implementation within the aerospace sector, paving the way for lighter, more efficient aircraft designs.

This paper provides a comprehensive review of the current research on the structural, mechanical, and thermal behavior of these advanced materials and focuses on the influence of various hybrid nanofiller combinations. By incorporating nanofillers like graphene, carbon nanotubes (CNTs), and nanoclays into epoxy matrices, the resulting nanocomposites exhibit superior properties due to improved dispersion, interfacial bonding, and interactions between fillers. Epoxy-based hybrid polymer matrix nanocomposites have emerged as a transformative material class and offer remarkable improvements in mechanical, thermal, and structural properties. The review discusses the key factors influencing composite performance and includes nanofiller characteristics, fabrication methods, and hybridization strategies. The findings reveal that hybrid nanocomposites show significant improvements with tensile strength increasing up to 70%, thermal stability enhanced by 20–30 °C and thermal conductivity boosted by 60% compared to plain epoxy. Mechanisms of reinforcement—such as effective load transfer and strong interfacial adhesion are investigated in detail—highlighting the impact of surface functionalization and improved dispersion strategies. The study ends with a discussion of potential future research avenues, highlighting the necessity of standardizing material characterization and design in order to fully realize the potential of epoxy-based hybrid nanocomposites for high-performance applications in the electronic, automotive, and aerospace industries.

Photothermal therapy involves the treatment of various human organs and tissues. To facilitate this, agar-based tissues phantoms are fabricated, as their thermal properties are formulated to closely replicate those of human tissues. Agar-based mediums are cost-effective, biocompatible, and flexible, with their properties adjustable by varying concentrations. They also serve as ethical and practical alternatives for experiments. To optimize thermal properties that closely match human tissue, agar concentrations ranging from 1.5 to 4.0 g were mixed with 100 ml of distilled water. This mixture was continuously stirred at 750 RPM using a magnetic stirrer at 92 °C. The agar-based tissues phantoms are used for preclinical photo thermal therapy applications. These investigations highlight the significant potential of agar-based tissue as effective platforms. The aim is to assess the thermal characteristics of agar-based tissue, including thermal conductivity, diffusivity and volumetric heat capacity. Thermal characteristics were formulate by using the advanced Trident C-THERM device with the flex TPS (ISO-22007) technique. The findings reveal variations in thermal conductivity, diffusivity and Heat capacity based on agar concentration.

Optimization of the machining parameters in any manufacturing industry is a crucial aspect since it is necessary for enhancing productivity and quality of the workpiece with least wastage. Therefore, the present article is focused on optimizing the machining process parameters of Electrochemical Discharge Machining (ECDM) using Multi-Criteria Decision Making (MCDM) approach. The machining parameters chosen are Applied Voltage, Inter-electrode Gap, and Electrolytic Concentration each having three different levels. The design of experiment is chosen as per Response Surface Methodology (RSM) approach resulting in 17 numbers of experimental runs. For MCDM, hybrid Entropy-VIKOR Method has been used to get the optimal machining parameters for four selected responses, i.e., Material Removal Rate, Entrance Overcut, Taper Angle, and Machining Time. The results obtained through this method reported A3B3C2, i.e., 60 V AV, 30% EC and 45 mm IEG as the optimal parameter combination which is marked as Rank 1.

In current years, Zinc (Zn)-based biomaterials (BMs) have been considered as novel materials due to their intermediate degradation properties. Pure Zn exhibits inadequate mechanical, biocompatible properties, so to improve the performance characteristics bioceramic materials have been combined to achieve specific biocompatibility and biodegradability. In this work, Zn-Hydroxyapatite (Zn-HA) and Zn-HA-Manganese (Zn-HA-Mn) composites were synthesized using a microwave-based sintering process. The degradation effect due to the addition of HA and Mn has been checked in a simulated body fluid solution (SBF). Zn-5HA was found to have better degradation characteristics than other samples. Adding of Hydroxyapatite (HA) and Manganese (Mn) to the Zn matrix in Zn-5HA-4Mn samples led higher corrosion rate and pH levels. The rate of weight loss due to SBF immersion testing of all samples have been found from high to low: Zn-HA-Mn > Zn-HA > Zn. Further, the compression testing of the developed composites shows that Zn-HA composite exhibits higher mechanical integrity than other combinations.

Small-scale industries face significant difficulties in terms of maintaining their profit margin. At the same time, expertise to deal with energy conservation is not generally available on-site. Energy can constitute a significant portion of total expenses for any small-scale organization. Present work aims to put forth salient findings from on-site energy audit conducted in a biotechnology plant situated in Taloja MIDC near Mumbai, Maharashtra. Significant energy saving was observed in thermal insulation as well as electrical system of the plant. Two energy conservation measures are identified which require zero investment while remaining two measures have payback period of 5.42 months and 4 months only. This audit has helped the organization to reduce its energy consumption by 55.60 kVA, resulting in monetary saving of Rs. 260,881/year. Total saving proposed is Rs. 706,201/- with a necessary investment of Rs. 106,514/-. Electricity bill analysis and thermography has been deployed as an important tool for energy conservation during this audit.

The COVID-19 health crisis has had a profound global influence on public health. In 2020, more than 50 million individuals were exposed to the virus, resulting in over 1.5 million deaths from respiratory complications. The severe acute respiratory syndrome coronavirus 2 is the source of COVID-19 (SARS-CoV-2), is a newly emerged infectious disease. Our understanding of its transmission is continually evolving through scientific and medical research. The virus spreads primarily through contact with mucous membranes in the eyes, nose, and mouth, potentially leading to severe illness or death. Similar to measles and influenza, COVID-19 is a viral infection that can spread through aerosols, although other modes of transmission are also possible. AIRDETO, a filter less air purifier, utilizes advanced technologies like electrostatic plasma, ionization, UV light, and photocatalysis to effectively sterilize and purify indoor air.

Multiple quality characteristics are a serious concern for the rapidly increasing sector in recent years. To address this issue, the current study describes a novel approach to optimizing several rollers burnishing quality characteristics called as the desire function. Various multi-character models have been constructed by adjusting roller burnishing settings between lower and upper limits for different combinations of responses with the goal of improving surface roughness. Number of tool passes, spindle speed, feed rate, and interference were used as model variables, whereas surface roughness, dimensional deviation, cylindricity, and parallelism were used as response features to construct prognostic models. Practical work was carried out on the Al alloy 6061 using a 5-level design. The results showed that the greatest desirability of 0.943 was attained for all four response characteristics at an ideal spindle speed of 50.001 rpm, an interference of 5.747 mm, a feed rate of 0.095 mm/rev, and five tool passes. The computed model’s validity at maximum desirability value has been confirmed by executing confirmation experiments in the most favorable scenario. The desirability technique is effective for optimizing multi-response characteristics throughout the roller burnishing process, as demonstrated by the results of optimum process parameter combinations.

This comprehensive overview delves into the intricate intersection of multi-material additive manufacturing (MMAM) and machine learning (ML), exploring their synergistic applications and addressing inherent challenges. The article unfolds the potential of ML in optimizing MMAM processes, particularly in designing metamaterials with unique properties. Focusing on structural engineering, it delineates ML’s impact on computational cost reduction, design resolution enhancement, and predictive performance improvement. The integration of ML with MMAM is exemplified through various studies, showcasing ML algorithms’ effectiveness in predicting stress–strain curves, material strength, and optimization of mechanical metamaterials. The article navigates through the challenges of metal–metal, metal–ceramic, metal–polymer, and polymer MMAM, elucidating the complexities of material compatibility, bonding, and dimensional accuracy. ML emerges as a crucial ally in overcoming these challenges, offering solutions in predicting material interactions, optimizing parameters, and ensuring robust bonds between dissimilar materials. Emphasizing the evolving landscape of 3D printing, the article explores the revolutionary strides in metal–ceramic and metal–polymer MMAM, underlining the role of ML in optimizing the design process. It concludes by underscoring the transformative potential of ML in advancing MMAM, fostering innovation, and overcoming limitations in multi-material extrusion-based rapid prototyping.

The aim of the present research work is to conduct productivity assessment studies, to improve operational efficiency, and provide recommendations for productivity improvement for a solar module manufacturing plant. An effort is made in this work to construct a current state value stream map for capturing the existing data for subsequent analysis of a solar panel manufacturing plant. After a thorough analysis, the future state value stream map is developed. The recommendations derived from the future value stream help the organization implement corrective solutions to existing bottlenecks in order to enhance customer satisfaction. Also, the requirement for incorporating the lean tools in organizations leads to better productivity as well as provides insights on potential constraints.

In the aerospace sector, M50 NiL steel is utilized for high-performance bearing steel in aero-engine shafts. M50 denotes a high alloy steel that employs nitriding for secondary strengthening. The heat treatment prior to nitriding aims for a strong core and hardened surface, with nitriding minimally affecting these traits. Surface coating enhances the hardness and corrosion resistance of M50 bearing steel. The nitriding process omits tempering and hardening steps. Liquid nitriding is utilized in this context. The nitriding procedure was conducted over various temperatures and durations for experimental assessment. This study’s primary goal is to evaluate the microstructure, hardness, and corrosion resistance of bearing steel. Research has investigated the corrosion behavior of M50 bearing steel in environments like sodium chloride (NaCl) solutions. The material is initially shaped using electrical discharge machining (EDM) for precise dimensions. Heat treatment is crucial for improving corrosion resistance. Electro potentiodynamic testing evaluates the surface roughness of materials through diverse parameters. Materials are immersed in the solution for three hours. By using potentiostat apparatus, corroded surfaces are characterized, and Tafel plots are drawn; from the results obtained, it is evident that lower temperature samples exhibited better corrosion rate than untreated M50 NiL steel.

The single-point incremental forming (SPIF) technique is an innovative method for sheet metal processing, whereby a sheet blank is firmly clamped at its edge while a single-point tool gradually deforms it along a predetermined trajectory in successive downward increments. This method provides three-dimensional sculpting without using dies. Surface roughness and non-uniform thickness distribution provide significant challenges in the parts formed by using SPIF process, highlighting the essential need to study the effect of forming parameters on these characteristics throughout the process. The present work adopts a systematic approach to determine the combined effects of tool diameter, feed rate, and tool rotating speed in controlling surface roughness and material thinning. The investigation conducts experiments on AA 1200 aluminum alloy followed by Taguchi technique and variance analysis data evaluation. The study reveals which processing variables allow for maximum surface finish reduction along with increased material thickness which leads to better manufacturing performance and product quality.

The incremental sheet forming (ISF) process is an advanced manufacturing process capable of producing a variety of sheet metal components without using complex dies and tools. This process involves deforming a sheet along a predetermined tool path using CNC machine. Most of the published literature on toolpath optimization focuses on step-size optimization only. However, the application direction of the step size is an important parameter in the development of the toolpath. Owing to this inspiration, this experimental investigation intended to analyze the toolpath with a focus on the direction of step-size application. To conduct the experiments, a full-factorial design was planned using Minitab. The design included one categorical factor: step-size application direction (horizontal, vertical, and inclined) with three levels, and two numeric factors: step size (0.25 and 0.75 mm) with two levels, and wall angles (31°, 45°, and 59°) with three levels. Average sheet thickness, average internal surface roughness, and forming time are the process responses. After the accomplishment of the experiments, it was revealed that step-size application direction has a considerable impact on surface roughness and forming time, although its impact on the average sheet thickness is negligible. The wall angle significantly influences the average sheet thickness and surface roughness (Ra). An inverse association between surface roughness and wall angle is observed.

The importance of sustainability in machining processes is increasing due to limited resources and growing environmental considerations. Hastelloy-X is difficult-to-cut material, especially in a dry environment. The present work was carried out by using alumina–zirconia (Al2O3–ZrO2) ceramic inserts in a dry machining environment. Machining experiments were performed with varying cutting speeds (100, 125, 150, 174, and 200 m/min.), feed rates (0.05, 0.1, 0.15, 0.2, and 0.25 mm/rev), and depth of cut (0.1, 0.2, 0.3, 0.4, and 0.5 mm). A total of 25 experiments were performed using Taguchi’s L25 orthogonal array. Consequently, the tool face wear (TFW), tool wear loss (TWL), surface roughness, and material removal rate were analysed for a comprehensive understanding of the machinability and tool wear. At low cutting speeds and high depths of cut, high deterioration and catastrophic fracture were observed, whilst by lowering the cutting speed and depth of cut the built-up edge (BUE) formed. From the workpiece material at elevated temperatures, diffused iron (Fe) and chromium (Cr) react with oxygen (O) to form mild oxides that initiate the BUE. Tool face wear and surface roughness both are most influenced by cutting speed, followed by depth of cut and feed rate. Tool face wear has direct correlation to the surface roughness. The sustainability assessment considers multiple criteria, including energy consumption, coolant cost, waste generation, operator health, surface roughness, workpiece cleaning, coolant recycling and disposal, tool cost, and noise level. Each criterion is rated on a scale of 1 to 10 to quantify its impact and significance in the sustainability assessment. Finally, the Product Sustainability Index (PSI) value of 7 indicates that the ceramic tool offers significant benefits in productivity, environmental sustainability, and workpiece quality for machining Hastelloy-X. The lower energy consumption, noise levels, and tool wear rate make it an attractive option. However, the moderate tool cost rating suggests potential for further cost reductions to enhance the economic sustainability of the machining process.

The abundance of natural fibres can facilitate its cost-effective utilization in fibre-reinforced composites by substituting the synthetic variant. In this study, a vacuum pressurization impregnation (VPI) treatment of semi-matured coir (SMC) fibres is done, followed by a mechano-chemical extraction procedure. After that, tests like thermogravimetric analysis (TGA), x-ray diffraction (XRD) and scanning electron microscopy (SEM) are done to examine the thermal features, crystallinity and morphological aspects of the fibres. The results are compared with non-VPI SMC fibres, extracted through a similar way. It is found that the VPI-treated SMC fibre exhibits higher crystallinity, lower thermal deterioration and reduced porosity as compared to non-VPI SMC fibre. An experimental design is created to systematically assess the breaking strength of these fibres, with predictions made using ANFIS, a machine learning model. Furthermore, particle swarm optimization (PSO) is taken to build a PSO-ANFIS model, for optimizing the ANFIS parameters. The results indicate that the PSO-ANFIS model outperforms standalone ANFIS, yielding superior predictive performance with lower root-mean-square error (RMSE) and higher R2 values, specifically 0.59951 and 0.99563, respectively. These findings support the potential of VPI-treated SMC fibres as effective reinforcements in natural fibre composites and highlight the PSO-ANFIS model’s efficacy for accurate breaking strength prediction. It may be beneficial for similar applications in practical composite material design.

The natural fibres obtained from various sources may be economically utilized to replace the synthetic fibres in fibre-reinforced composite materials. This study initiates by performing a novel vacuum pressurization impregnation (VPI) treatment of ripe areca husk fibres (AHF), followed by their mechano-chemical extraction. Further, the physical, thermal and morphological features are compared with the untreated ones with techniques like x-ray diffraction (XRD), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The extraction parameters are optimized using response surface methodology (RSM) and particle swarm optimization (PSO) to minimize the hemicellulose content, which influences moisture absorption and fibre performance. The results show that VPI-treated AHF exhibits lower cellulose crystallinity, enhanced thermal stability and reduced porosity as compared to untreated fibres. The RSM develops a quadratic model with a precision of 41.6830 and detects any potential outliers, whilst PSO predicts optimal conditions of 7.2017% NaOH concentration, 1466.3 RPM stirrer speed and 8.805 h of stirring time, achieving an optimal response value of 0.319 g hemicellulose with an error of 0.62%. The study concludes that PSO is more efficacious than RSM in optimizing the extraction process for minimizing hemicellulose. Overall, the VPI-treated AHF shows great potential as a sustainable reinforcement in composite materials, offering improved mechanical and thermal properties through optimized extraction techniques.

The abrasive flow machining (AFM) process has applications in finishing internal geometries with intricate shapes. Abrasive media is crucial in such applications due to self-deformable behaviour under applied load. The parts manufactured using additive manufacturing techniques have high surface roughness, limiting their application. In the present study, the finishing behaviour of xanthan gum (XG)-based abrasive media has been analysed. The cylindrical acrylonitrile butadiene styrene (ABS) parts were printed using fused deposition modelling (FDM) and finished using AFM. An abrasive media composed of natural gum-based hydrogel and SiC abrasives has been prepared and used for finishing. The effect of extrusion pressure (EP), abrasive concentration (AC), and layer thickness (LT) were investigated on material removal (MR) and percentage improvement in surface roughness (% ΔRa). The experimental results show a maximum MR of 36 mg and a maximum % ΔRa of 27.63%.

Product design is the process of discovering a market opportunity, clearly articulating the problem, producing a suitable solution, and testing the product with real environment. Design thinking is one of the modern approaches in product design and development which incredibly helpful when solving challenges that are unclear or poorly defined. It generally reframes the issue from a human-centred perspective as it generates many ideas during brainstorming sessions and uses a hands-on technique during prototyping and testing. This study represents step by step implementation of significant phases of design thinking process to develop an electrical fumigation device mainly useful in Ayurvedic therapies. Fumigation (Dhoopan) is an ancient therapy in which dry herbal ingredients are burnt slowly to generate the medicated smoke. The device is adaptable and multi-purpose in all kinds of fumigation therapies along with air and surface sanitization with fume’s temperature control facility. Stainless steel (SS304) provides anti corrosiveness as well as aesthetic appearance. A comprehensive study has also been carried out to evaluate the effectiveness of the device in terms of air sanitization. It can reduce bacterial colonies by 40% and fungi spores by 32.75% within the specified location compared to the traditional fumigation. The device is also useful to common civilians in terms of air and surface disinfection for domestic applications.

Gas metal arc welding (GMAW)-based wire-arc additive manufacturing (WAAM) method has gained popularity due to exhibition of better features like effortlessness in deposition of material at a higher rate and fabrication of multi-walled components with minimum cost. The additive components of stainless steels have gained a lot attention for multiple applications owing to their favourable characteristics such as excellent strength, high resistance to rust and corrosion, durability, and aesthetic nature. GMAW method requires a systematic approach to control input variables to built a multi-walled component. Thus, the present has made an attempt to attain the appropriate process conditions for bead morphologies of bead height (BH), and bead width (BW). GMAW process was employed for single-layer depositions by considering voltage, gas mixture ratio, and travel speed as design variables. SS309L was used as filler wire material with SS316L material as substrate plate. Box–Behnken technique of RSM has been used to design a systematic planning of experimentation. The obtained results were analysed by using ANOVA, normal probability plots, and main effect plots. Empirical relations were also developed between design variables and output measures. Author believes the current study will be suitable for researchers as starting point to select appropriate design variables.

In the present investigation, the effect of transition depth on the work hardening was studied when a bimetallic liner plate with a chromium carbide overlay on a mild steel substrate (CCO sample) and an impact plate made of austenitic manganese steel (impact plate sample) was subjected to abrasive wear. The abrasive wear test was conducted per the standard ASTM G65 test procedure. The CCO sample showed a lesser mass loss and enhanced resistance to abrasive wear as compared to the impact plate sample. The enhanced resistance to wear was attributed to the carbide phase in the CCO sample. However, impact plate sample showed a lower mean work hardening depth compared to the CCO sample. The nonwork hardened areas of both the samples showed nonexistence of dislocations and substructures. The microstructural investigation of the work-hardened area of austenitic manganese steel impact plate showed dislocation pile-up along the grain boundaries and at the grain interior. However, the CCO sample showed the formation of slip bands in the vicinity of the work-hardened area indicating localized plastic deformation.

This paper investigates the bending and wear behaviour of hollow aluminium alloy pipes of circular cross section. Three-point bending tests were conducted at different strain rates (0.001, 0.01, and 0.1 s−1) and span lengths (100, 120, and 140 mm) on universal testing machine to understand the bending behaviour of the hollow pipes. The wear behaviour of the pipes was analysed under three different loads (30, 40, and 50 N) and speeds (200, 300, and 400 rpm) on pin-on-disc machine. It is found that the bending strength of the pipe is sensitive to the strain rate and the specimen geometry. Also, the wear and friction characteristics of the circular pipe change with varying loads and speeds.

Aluminum (Al) foam produced via powder metallurgy (PM) route is prone to several issues that can affect its microstructure and mechanical properties resulting in poor compression. Nonuniform pore size distribution may occur because of inconsistent mixing or uneven distribution of the foaming agent within the aluminum powder matrix which may lead to weak regions in the foam, reducing its mechanical performance, such as compression strength. This paper focus on utilizing silicon carbide (SiC) particles with 5%, 10%, and 15% and urea as space holder to prepare Al/SiC composite foams. The effect of different reinforcement content on the pore structure and distribution of aluminum foam composite was focused and axial quasi-static compression tests were conducted. The results indicate that variations in reinforcement significantly influenced the porosity and pore distribution of Al foam. Al/SiC 10% composite foam demonstrated outstanding compressive strength of 48.78 MPa which is three times more than traditional unreinforced aluminum foam.

The present work is outlined for the model-free isoconversion rheo-kinetics of 2-oxazolidone modified novolac epoxy film adhesive through rheological assessment under isothermal condition at different temperatures viz. 160, 165, 170, and 175 °C during its curing process. Effect of polyethersulfone (PES), which is used for toughening the film adhesive, on the isothermal curing reaction of film adhesive is also studied. Model-free isothermal isoconversion rheo-kinetic analysis is attempted while curing reaction via both integral and differential methods of Vyazovkin and Friedman, respectively. Curing behavior is predicted and compared with experimental data at each temperature. It is found that the prediction is > 90% and > 85% accurate for integral and differential method, respectively. Activation energy obtained in both approaches is showing the dependency on the extent of conversion, and it is seen in the range of 165–132 kJ mol−1 for WO-PES and 188–141 kJ mol−1 for W-PES in integral method and 114–90 kJ mol−1 for WO-PES and 108–78 kJ mol−1 for W-PES in differential approach. Contradictory behavior of activation energy in these approaches is attributed to the presence of non-reactive PES.

Military ships carry sensitive articles and equipment that experience shock loads caused by underwater explosions in the vicinity of the ship. Unwanted shock loads of this kind have the potential to damage extremely energetic objects and compromise the accuracy of extremely delicate machinery. This research aims to perform a finite element (FE) analysis of ring-shaped hydrogenated nitrile butadiene rubber (HNBR) and Sorbothane isolators to protect a sensitive article stored inside a cylindrical-shaped component of a naval ship using ABAQUS. These materials have nonlinear elastic and viscous properties, which are modeled in the ship component’s FE model by the third-order Ogden and generalized Maxwell models, respectively. The material constants of these models are determined using the experimental test data of HNBR and Sorbothane. A transverse shock with a peak amplitude of 60 g is applied at the outer surface of the component, and responses are measured in the form of acceleration of the article, deformation, and stress distribution in the isolators. The proposed HNBR and Sorbothane isolators of considered length provide 97.16% and 98.23% shock reduction with 15.23 mm and 9.5 mm maximum deflection in the isolators, respectively. Moreover, the maximum stress developed in the isolators is within the tensile strength of the materials.

This study presents an automated defect detection system for smart factories utilizing advanced deep learning models. The approach leverages convolutional neural networks (CNNs) and transfer learning techniques, employing pre-trained architectures such as VGGNet, ResNet, DenseNet, MobileNetV2, and InceptionV3. This system aims to enhance real-time quality assurance by reducing manual effort, improving production efficiency, and achieving high accuracy in defect classification. The proposed framework contributes to advancing Industry 4.0 practices, emphasizing the integration of IoT and AI in manufacturing. Experimental evaluations demonstrate significant improvements in defect detection accuracy, operational cost reduction, and production scalability.

The process of manufacturing a dental implant screw that produces accurate and efficient tooth crowning is the subject of the current invention particularly focusing on utility of rotomolding mechanism. The technique uses a turret rotational molding machine with a rim that can rotate on a perpendicular axis. The rim also has slots spaced regularly along its outer surface to accommodate several molds in the shape of dental implant screws. To achieve the final screw implants, the process consists of charging (S1) biocompatible resin material into the molds held in the rim, heating (S2) in an electric oven, cooling (S3) with a forced air/water spray, and finally withdrawing the molds from the rim slots. This technique satisfactorily can provide a better option in this field of biomedical for which the idea has been proposed.

The growing demand for Nitinol alloy parts in industries like automotive and aerospace underscores the need for optimizing machining parameters to improve both product quality and cost efficiency. Key factors such as spindle speed (L), feed rate (f), and depth of cut (t) significantly influence the machining process. Important performance indicators, including material removal rate (MRR), tool wear (TW), and surface roughness (Ra), are used to assess the effectiveness of the process. This paper uses a fuzzy logic-based utility function to identify the optimal machining conditions for dry turning Nitinol 56.

This study highlights application of ANOVA in order to analysis the effect of voltage, pulse-on (ton), pulse-off (toff), and wire speed impact output parameters like surface roughness, kerf width, and machine removal rate during the Wire Electrical Discharge Machining (WEDM of Inconel 825). Here, the experimentation has been carried out using Taguchi L9 orthogonal array. The study also utilizes the Grey relation analysis to obtain the optimal combinations as ton = 70 µs, toff = 7 µs, wire speed = 3 (level), and voltage = L (level).

Plastic waste has become a global crisis, with only a small percentage being recycled. This project seeks to address this issue by converting plastic waste into valuable products through a comprehensive multi-step process. This involves de-labelling, sorting, shredding, dyeing, and extrusion, where a screw extruder melts and mixes the plastic to produce items like plastic bricks for construction, roads made from a plastic-concrete mixture, and molded products such as automotive components using injection, blow, rotational, and compression molding techniques. Additionally, calendering is employed to create thin sheets, packaging strips, and filaments for 3D printing, while plastic granules produced through these processes serve various industrial applications. The production process is optimized with programmable logic controllers (PLCs), enabling real-time monitoring to enhance efficiency and product consistency. Sensor data, analyzed through AI and machine learning, helps optimize products and predict equipment life, minimizing mechanical failures. Sensors like temperature, humidity, ultrasonic, and pressure are used to monitor key parameters, reducing maintenance costs. Automation is further enhanced with smart conveyors, IoT integration, and cloud-based monitoring, addressing traditional manufacturing challenges. By recycling plastic waste using these advanced methods, more plastic is diverted from landfills, significantly lowering pollution and greenhouse gas emissions. This contributes to protecting wildlife and their habitats, paving the way for a cleaner, greener future for upcoming generations.

With the advent of emerging technologies, there is a growing demand to enhance overall efficiency with respect to energy, productivity, and tooling. As a result, there is a focus on making machining processes simultaneously sustainable, productive, and efficient. In this research, the influence of machining parameters in CNC lathe, such as feed, cutting speed, and depth of cut, were evaluated under both dry and wet conditions. Since sustainable production requires a balance between energy consumption and quality, response variables like Ra and MRR were analysed. A Taguchi-grey integrated approach was adopted. For single-response analysis, Analysis of Variance (ANOVA), main effect plots, and response tables were employed. The grey relational methodology was used for multiobjective optimization. The ANOVA results indicated that depth of cut had the greatest impact on surface roughness and MRR, followed by feed in Study 1. In Study 2, under wet conditions, feed emerged as the most significant factor affecting surface roughness, while cutting speed was the primary contributor to MRR. According to the grey relational analysis, the optimal combination for realizing the best MRR and Ra in both environments was a cutting speed of 700 rpm, a feed rate of 0.18 mm/rev, and a depth of cut of 1 mm.

Difficult-to-cut materials like titanium and its alloys exhibit superior properties like high resistance to corrosion, excellent strength-to-weight ratio, and more stability at elevated temperature. Due to this, these are widely preferred in aerospace, biomedical, and automotive industries for components like turbine blades, implants, and high-performance engine parts. Machining of such materials become challenging due to its low elastic modulus, hardness, and poor thermal conductivity. The traditional machining methods impose lot of challenges. Thus, electrical discharge machining (EDM), a type of non-traditional processes, was found to be more appropriate in processing of such materials. The challenges faced by the EDM process are lower cutting rate, larger tool wear, reduced surface quality, and recast layer thickness. To eliminate those challenges, the highly electrically and thermally conductive nano-powders need to be added to the dielectric fluid of EDM. These nano-particles in the dielectric gives the improved machining performance and surface morphology. The powders such as alumina (Al2O3), aluminum (Al), multiwall carbon nanotubes (MWCNTs), nano-graphene, expanded graphite, silicon (Si), and silicon carbide (SiC) are used as a powder additive. The addition of these nano-powder results in the reduction in breakdown strength of the dielectric fluid which in turn improves the ignition mechanism. Thus, the present paper discusses a brief overview of the use of different nano-powders during the EDM of titanium alloys.