Jaimon Dennis Quadros

Metamaterials are engineered materials with unique properties arising from their structure rather than composition, featuring repeating patterns smaller than the wavelengths they affect. Meta-biomaterials are an important subset of metamaterials and have drawn increasing interest in recent times due to their favorable mechanical properties, biological properties and functional integrity. These exceptional properties have enabled their suitability for diverse biomedical applications, including orthopedic/dental implants, tissue engineering, and medical devices. These materials are generally subjected to cyclic musculoskeletal loads after implantation, making the study of their fatigue behavior critical for ensuring long-term reliability. The current review, therefore, focuses on the fatigue behavior of meta-biomaterials that are manufactured using different additive manufacturing techniques. Various factors like topological design, base material/alloy selection, type of fatigue loading, manufacturing and secondary treatment processes, etc., are carefully analysed, and their influence on fatigue performance is studied. Furthermore, the failure mechanisms of additively manufactured meta-biomaterials with different geometries, structures, and architectures are also analyzed. Thus, this comprehensive review not only elucidates the underlying fatigue mechanisms, but also establishes a framework for the rational design of next-generation of biomedical implants with enhanced durability and functionality.

The current work aims to develop computational models for the thermal characteristics of turbulent CH4 flames for varying burner dimensions. This study develops a platform for data driven analysis of temperature prediction of turbulent non-premixed flames, in which the influence of flow and geometric parameters, including burner head diameter (D), half cone angles (α), and co-flow air velocity (Ucf), have been considered. The algorithms used were ridge regressor (RR), linear regressor (LR), and three variations of support vector regression (SVR): SVR with a linear kernel (SVR-LR), SVR with a radial basis function function (SVR-RBF), and SVR with a polynomial kernel (SVR-Poly). The performance of each computational model was evaluated and contrasted based on several metrics: mean absolute error (𝑀𝐴𝐸), regression coefficient (𝑅 2 ), mean absolute percent error (𝑀𝐴𝑃𝐸), and mean Poisson deviance (𝑀𝑃𝐷). From the modelling of the output data, it was observed that the SVR-RBF predictions were more accurate compared to those from the other algorithms, as it achieved the highest training 𝑅 2 value of 0.955. The testing predictions of RR, SVR-LR, SVR-RBF, and SVR-Poly algorithms were also robust, with 𝑅 2 values ranging between 0.91-0.94. It is, therefore, established that these computational models are effectively suited for predicting sensitive turbulent CH4 flame characteristics based on varying input factors.

The current study focusses on developing a back propagation neural network model for depth of cut during the abrasive water jet machining of a Ti-6AL-4V aluminum alloy. The study analyzed depth of cut for five different water jet abrasive parameters namely, water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size, and nozzle to orifice diameter. Experiments were conducted as per the L27 Taguchi-design of experiments (DoE). The back propagation neural network model comprising of one input layer, one hidden layer and an output layer with an architecture of 1–5–6 was chosen for conducting the analysis. The algorithm predicted the Taguchi based output values for the experimental depth of cut with an accuracy of up to 95 %. The neural network algorithm further automated itself, generating 50 new data sets for K-cross validation, out of which 70 %, 20 %, and 10 % of the data were used for training, testing, and validation, respectively. Confirmatory experiments were conducted for depth of cut and assessed against the data set used for validation (10 %). The results showed that as the depth of cut was small, i.e., ranging from 3 mm to 5 mm, the algorithm was unable to predict the optimized parameters, however, the prediction improved as the depth of cut increased. Overall, the consistency between the neural network predicted and the experimental depth of cut throughout the algorithm confirmed the validity of the procedure and the appropriateness of the algorithm.

This review presents design, analysis and experimental analysis of metamaterials with tunable properties for biomedical applications. Five different metamaterials namely, lightweight metamaterials, pattern transformation metamaterials, negative compressibility metamatrials, pentamode metamaterials and auxetic metamaterials have been discussed in detail with emphasis of these materials in the field of biomedicine. Furthermore, different addivitve manufacturing techniques implemented in the manufacturing of these biomaterials may be customized to provide different mechanical characteristics. Finally, the mechanical properties and deformation mechanisms for the biomaterials have been discussed.

The current study aims to amplify the predictive ability of the numerical model developed for a gas turbine engine-based power plants by process of regeneration and intercooling. Artificial neural networks (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) are the two techniques mainly concentrated in this study which were not properly implemented previously. The performance parameters namely, specific power (SP), thermal efficiency (η), and enthalpy based specific fuel consumption (EBSFC) of a Turboprop engine were predicted using thermodynamic parameters namely, pressure ratio (PR), nozzle pressure ratio (NPR), turbine inlet temperature (TIT), for constant regeneration (R), and intercooling (E) efficiencies. The results showed that a high regression result R2 of 0.9831 and 0.9899was found for the ANFIS model for η for training and testing, respectively. Also, the ANFIS model resulted in best performance of the performance characteristics when compared to ANN.

The present work investigates the mechanical properties of a composite material composed of multi-walled carbon nanotubes (MWCNTs), nano-aluminum powder (NAP), and glass fibers (GF) for five different compositions. The study further investigated how MWCNTs contribute to maintaining the mechanical properties of nanocomposites when exposed to elevated temperatures, up to 180 °C. The evaluation of impact strength revealed that the nanocomposite, composed of 2 % MWCNTs, 15 % NAP, and 10 % GF, demonstrated the greatest impact strength. At room temperature, the composite containing 2 % MWCNTs, 5 % NAP, and 20 % GF exhibited the highest ultimate tensile strength (UTS). Conversely, at elevated temperatures reaching up to 180 °C, the highest UTS was observed in the composition with 2 % MWCNTs, 10 % NAP, and 15 % GF. The hardness of the nanocomposite was influenced by its composition; at room temperature, the maximum hardness was observed in the mixture containing 2 % MWCNTs, 20 % NAP, and 5 % GF. In contrast, at elevated temperatures, the composition with 2 % MWCNTs, 5 % NAP, and 20 % GF exhibited the highest hardness. Overall, the study found that incorporating GF and NAP improved the mechanical properties of the composite. These results indicate that the composite's performance could be further optimized for specific applications through the addition of filler materials.

Auxiliary systems, such as regeneration and intercooling, have been integrated with the primary gas generator to improve power production and fuel economy in modern gas turbine power plants. Implementing these techniques in turbine engines is challenging due to size, weight, and complex flow patterns. A solution is to use a turboprop engine with a smaller mass flow rate and simpler gas paths. The current study involves the numerical analysis of performance parameters namely, specific power (SP), thermal efficiency (eta), and enthalpy based specific fuel consumption (EBSFC) of a turboprop engine using thermodynamic parameters namely, pressure ratio (PR), nozzle pressure ratio (NPR), turbine inlet temperature (TIT), regeneration efficiency (R), and intercooling efficiency (E). The results prove that the introduction of regeneration and intercooling showed significant improvement in the power developed, and reduced fuel consumption.

Molten salts are highly effective as a quenching medium for austempering and martempering processes, enabling precise control of cooling rates to achieve the desired microstructures and mechanical characteristics in steel components. One such promising molten salt is a multicomponent Ca (NO3)2-KNO3 molten salt. The current work explores the cooling severity of molten Ca (NO3)2-KNO3 mixtures, which are commonly used for such purposes. The said mixture, with varying concentrations and bath temperatures was used for quenching the Inconel probe with thermocouples. The temperature data extracted was used to determine the transient heat flux developed at the metal?quenchant interface. A set of critical points were assessed against the peak heat extraction rates. Additionally, the fluctuation of mean heat flux and surface temperature in relation to these crucial points were plotted, along with changes in composition and bath temperature of the quench media. The cooling intensity of these quench solutions, as measured by Inconel probes, correlated well with the average hardness values observed in steel probes. The level of homogeneity in heat transmission, as measured by the spatial variance of the normalized heat energy, decreased as the percentage of KNO3 in the quench medium increased.

The bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively.

Multi-objective grey relational analysis optimization technique and multiple regression analysis were employed to determine the optimum values for depth of cut, surface roughness ( R
a
), and kerf at entry and exit ([Formula: see text] and [Formula: see text]), for abrasive waterjet machining of Ti6AL4V materials. This method highlights a new process to extend the grey relational analysis technique for determining the optimum conditions for obtaining the best quality characteristics. The input parameters of the study were water pressure ( W
p
), transverse speed ( T
s
), abrasive mass flow rate ( A
mf
), abrasive orifice size ( A
os
), nozzle/orifice diameter ratio ( N/O
dia
). The experiments were conducted as per the Taguchi-based L
27
orthogonal array. The grey relational analysis technique found that T
s
was the most significant parameter on the combined outputs. The regression models developed had an R
2
of 81.58%, 79.79%%, 77.20%, and 74.39% for depth of cut, R
a
, [Formula: see text] and [Formula: see text], respectively. Additionally, the analysis of variance showed that W
p
and A
os
had a significant influence on the output parameters. The predicted values were found to be reasonably close with the experimental values, and the maximum average deviation was 8.15% for [Formula: see text].