Supriya J. P.

Digital twin (DT) technology has emerged as a cornerstone of Industry 4.0, facilitating real-time synchronization between physical assets and virtual models to drive operational excellence. Unlike prior surveys that address singular industrial domains, presenting qualifications in general terms without paradigm-to-task mapping, this review uniquely synthesizes DT architectural maturation across Technology Readiness Levels (TRLs) 1 through 9, quantitative performance outcomes from 39 documented industrial implementations spanning 10 sectors, and an explicit algorithmic taxonomy mapping distinct AI paradigms to specific functional DT requirements. By synthesizing empirical data across the aerospace, automotive, and manufacturing sectors, this study evaluates the quantitative impact of DT implementation, highlighting significant gains in predictive maintenance, production efficiency, and design cycle reduction. This research further examines the synergistic role of Machine Learning (ML) paradigms integrated within DT systems, specifically, physics-informed neural networks (PINNS), generative adversarial networks (GANs), deep transfer learning, reinforcement learning, and federated learning, in enhancing diagnostic accuracy and enabling autonomous decision-making. Despite these advancements, this review identifies critical barriers in data interoperability, cybersecurity, and workforce expertise that impede widespread adoption. This paper concludes by outlining future research directions, emphasizing the necessity for standardized data protocols and secure, distributed DT ecosystems to unlock the full potential of cyber-physical integration in a data-driven industrial landscape.

AISI 1040 steel is extensively used in structural and automotive applications, where surface integrity plays a significant role in service performance and coating adhesion. Furthermore, the selected cutting fluids are expected to effectively reduce surface roughness and tool wear by improving lubrication at the tool and workpiece interface. This study investigates the influence of SiO₂ nanoparticle-assisted Pongamia pinnata oil on surface roughness and tool wear during the machining of AISI 1040 steel using an uncoated tungsten carbide tool by varying nanoparticle concentration (Vol.%), cutting speed (m/min), depth of cut (mm), and feed rate (mm/rev). The incorporation of 0.5 (Vol.%) SiO₂ nanoparticles significantly enhances machining performance by improving surface finish and reducing tool wear. Further, a minimum surface roughness value of 1.95 microns and tool wear value of 0.047 mm were achieved at a cutting speed of 101 m/min, feed rate of 0.11 mm/rev, depth of cut of 0.25 mm and 0.5 (Vol.%) SiO₂ nanoparticle concentration. ANOVA results indicate that nanoparticle concentration is the most dominant parameter affecting both surface roughness and tool wear, contributing 85.35% to the variation in surface roughness and 82.2% to the total variation in tool wear. Cutting speed is the second most influential factor, accounting for 11.63% of surface roughness variation and 11.07% of tool wear variation, while feed rate and depth of cut exhibit minimal influence in both cases. A second-order RSM model was developed to predict surface roughness and tool wear, showing excellent agreement with experimental results. The model predicted surface roughness with an average error below 2.43%, while the second-order model for tool wear exhibited an average prediction error of 4.95%, confirming its statistical significance and predictive reliability. Desirability Function Method (DFM) analysis yielded a desirability value of 1.000, confirming the optimal combination of machining parameters at 0.5354 (Vol.%) nanoparticle concentration, a cutting speed of 45 m/min, a depth of cut of 0.50 mm, and a feed rate of 0.1298 mm/rev. Overall, this study demonstrates that 0.5 (Vol.%) SiO₂ nanoparticle-incorporated Pongamia pinnata oil is an effective and sustainable cutting fluid, significantly improving surface integrity and machining performance of AISI 1040 steel during machining. Under these settings, the predicted tool wear was 0.0614 mm, accompanied by a high composite desirability value of 0.92786, indicating excellent overall performance. Moreover, the close agreement between experimental, response surface model and BP-ANN-predicted tool wear and surface roughness confirms that the ANN model reliably and robustly captures the complex, nonlinear effects of machining parameters with minimal systematic error.

This paper investigates the AWJ machinability of Hibiscus Rosa-Sinensis/carbon nanotube (CNT) fiber/epoxy-based hybrid composites by analyzing key machinability metrics such as kerf width (KW), material removal rate (MRR), and surface roughness (Ra). Various process parameters including CNT weight percentage, CNT diameter, stand-off distance, and traverse speed have been varied to optimize the machining performance. Experimental analysis suggested that increasing the CNT weight percentage significantly enhanced material hardness, thereby reducing both the MRR and surface roughness. Moreover, adjusting the stand-off distance and traverse speed further improved the machinability of the composite. ANOVA results highlighted that CNT weight percentage was a significant factor, accounting for 94.17% of the variation in MRR and 93.72% of the variation in surface finish, while the stand-off distance influenced 87.03% of the variation in kerf width. Additionally, response surface methodology (RSM) was utilized to develop predictive models that estimated KW, MRR, and Ra with error rates of 2.95%, 2.23%, and 5.65%, respectively. These insights offer a valuable framework for tailoring the AWJ process to achieve optimal machining outcomes in HRS/CNT/epoxy composite materials

In metal cutting industries, optimizing the thermal conductivity and viscosity of vegetable oil-based cutting fluids is critical for ensuring efficient heat dissipation, effective lubrication, and sustainability, directly influencing tool life and machining performance. This study presents a comprehensive experimental analysis employing statistical methods, particularly Taguchi’s Design of Experiments, to evaluate the thermal conductivity and viscosity of Pongamia pinnata, sunflower, and coconut oil incorporated with Silicon Dioxide (SiO₂), Hexagonal Boron Nitride (hBN), and Cupric Oxide (CuO) nanoparticles across different emulsion ratios and nanoparticle volume fractions. The results revealed that Pongamia pinnata oil containing 0.5 (Vol.%) SiO₂ nanoparticles at an emulsion ratio of 1:7 achieved the maximum thermal conductivity, measured at 0.637 W/mK. Additionally, the results revealed that Pongamia pinnata oil at an emulsion ratio of 1:13 exhibited the highest viscosity of 1.33 mPa·S, confirming that both the type of cutting oil and the emulsion ratio are the primary factors influencing viscosity. Further, the ANOVA analysis for thermal conductivity and viscosity highlights that the type of cutting fluid is the dominant factor, accounting for 90.58% of the total variance in thermal conductivity and 70.47% in viscosity, each with a highly significant p-value of 0.00, underscoring its decisive impact on the stability of both properties. Overall, this research offers important guidance for the selection and formulation of vegetable oil-based emulsions with nanoparticle additives. The results support the development of advanced nano lubricants with enhanced performance, catering to the increasing requirements of diverse industrial applications.

Titanium-based alloys and their associated composites are increasingly favored as the go-to materials in demanding applications across a wide range of sectors, including aerospace, automotive, and military industries. Their unique attributes, such as an elevated strength-to-weight ratio, impressive rigidity, and outstanding resilience to elevated temperatures, make them suitable for components operating under extreme conditions. Despite these advantages, machining these materials poses notable obstacles, largely due to the presence of hard reinforcement phases. These phases can accelerate tool degradation, produce suboptimal surface quality, and elevate overall machining expenses. This comprehensive review offers an in-depth examination of the machinability properties of Titanium alloys and their composites, evaluating both traditional and innovative machining techniques. The analysis focuses on the influence of different reinforcement materials—like silicon carbide (SiC) and aluminum oxide (Al ₂ O ₃ )—on various machining outcomes, including tool durability, cutting forces, and surface condition. Moreover, the study assesses how machining parameters, such as cutting velocity, rate of feed, penetration depth, impact these factors. In addition, it probes the microstructural alterations and chip formation linked to distinct machining settings, offering valuable guidance for refining cutting processes. The review also emphasizes a broad spectrum of industrial applications while discussing new trends and future directions for research in machining and further utilization.

The natural fiber-reinforced nanomaterial filler polymer matrix hybrid composite has superior applications in industrial and manufacturing fields due to its enhanced mechanical and machinability characteristics. However, in order to generate high-quality components, unconventional machining techniques, notably abrasive waterjet machining, have become more popular due to the inhomogeneity of composites, fiber pullout, greater surface roughness, and dimensional inaccuracy under traditional machining. Delamination typically refers to the separation that occurs along a plane parallel to the surface, such as the detachment of a coating from its underlying material or the separation between different layers within the coating itself. This paper investigates the AWJM characteristics of Hibiscus Rosa-Sinensis/Carbon nanotube/Epoxy (HRSCE)-based hybrid composite, focusing on delamination factors at entry, exit, and machining time. An L₂₇ orthogonal array was employed to optimize process parameters, revealing that DF-entry decreased with increasing CNT (wt.%), achieving its lowest values at 3 (wt.%) CNT and 2 mm stand-off distance due to enhanced composite toughness and precise jet focus. Conversely, DF-exit increased with higher CNT (wt.%), stand-off distance and traverse speed, attributed to the composite’s increased brittleness and reduced cutting efficiency. Machining time was predominantly influenced by CNT (wt.%) (92.4%), increasing with higher reinforcement levels due to enhanced material resistance. Response surface methodology models demonstrated high accuracy in predicting machining outcomes, with errors below 3%. Contour and surface plots identified optimal conditions for minimal delamination and machining time as 3 (wt.%) CNT, low stand-off distance (2 mm), and moderate traverse speed (200 mm/min). The SEM and optimal microscopy analysis confirmed that CNT reinforcement positively influenced fiber matrix interfacial integrity and reduced surface damage.