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Titanium nitride (TiN) thin film coatings were grown over silicon (p-type) substrate using the atmospheric pressure chemical vapour deposition (APCVD) technique. The synthesis process was carried out to evaluate the effect of deposition time on the physical and mechanical characteristics of TiN coating. Thin films grown over Si substrate were further characterised to evaluate the morphological properties, surface roughness and mechanical properties using a scanning electrode microscope (SEM), atomic force microscopy (AFM) and nanoindentation, respectively. EDS equipped with SEM showed the presence of Ti and N elements in considerable amounts. TiN morphology obtained from the SEM test showed small-sized particles on the surface along with cracks and pores. AFM results revealed that by increasing the deposition time, the surface roughness of the coating also increased. The nanomechanical properties such as nanohardness (H) and Young’s modulus (E), etc., evaluated using the nanoindentation technique showed that higher deposition time led to an increase in H and E. Overall, it was observed that deposition time plays a vital role in the TiN coating deposition using the CVD technique.

In this experimental investigation, the influence of different N2 gas flow rates on different properties (e.g. morphological, mechanical, etc.) of chemical vapor deposited (CVD) Titanium nitride (TiN) coatings has been discussed. The TiN coatings had been grown on Si (100) substrate at elevated temperature (1000 °C) using Titanium dioxide (TiO2) powder. SEM images reveal a dense uniform microstructure with an irregular surface pattern. The surface roughness of the coatings was found to be increased from 12.42 to 28.56 nm with an increase in flow rate. XRD results indicate a B1 NaCl crystal structure of the film with reduced crystallite size with the increasing N2 flow rate. Through the corrosion test, it has been observed that due to the variation of N2 flow rate the corrosion resistance of the films decreases with increasing N2 flow rate. The mismatch of thermal expansion co-efficient in between Si substrate and TiN thin film reduces with higher N2 flow rate. The acoustic and optic phonon mode of TiN coatings have been shifted to higher intensities with higher N2 flow rate. The mechanical properties of the film reveal that the maximum value of hardness (H) and Young's modulus (E) are 30.14 and 471.85 GPa respectively.

Titanium Nitride (TiN) thin films were deposited by thermal chemical vapor deposition process (CVD) over Si (100) substrate under different substrate temperatures. Morphological, mechanical, and structural properties were characterized by different techniques e.g. SEM, AFM, Nanoindentation and XRD. SEM images reveal the presence of agglomerated particles over the surface and AFM images reveal the enhancement of surface roughness with higher deposition temperature. The analysis of electrochemical polarization and electrochemical properties of TiN coatings reveals decrease in corrosion resistance with increase in process temperature. The nanoindentation analysis has confirmed that TiN coating possess maximum fracture toughness (KC), hardness (H), and Young’s modulus (Ef) when synthesized at the temperature of 1150 °C. For comparative study, an analysis over structural properties using different models e.g. UDM, UDEM, SSP etc. have been used in this study. The data analysis of TiN coatings are carried out by using Origin 9.0 software.

In this study, diamond-like Carbon (DLC) thin film coatings are deposited by atmospheric pressure chemical vapour deposition (APCVD) process by using C2H2 and H2 as precursor gases on SiO2 substrates. The morphological, structural, mechanical and composition of the DLC thin film coatings are studied by using field emission scanning electron microscopy (FESEM), Raman spectroscopy, nanoindentation, Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). Irrespective of the varying H2 flow rate, smooth surfaces of the thin film coatings are observed in the FESEM images. The Young’s modulus (E) and hardness (H) of the DLC coating increases with an increase in H2 flow rate and the maximum E and H are observed as 193.97 GPa and 22.46 GPa respectively. Due to the SiO2 buffer layer, the residual stress (σ) of the film significantly decreased to a minimum of 1.05 GPa which is much less as compared to the DLC film deposited over the Si substrate. The percentage of sp3 can be calculated from the XPS and it is 58.68% and 70% for the flow rate of H2 of 0 and 80 sccm respectively. The effect of H2 flow rate on the morphology, structural, mechanical and composition of the DLC thin film coatings are analyzed and discussed thoroughly along with mechanics behind it.

In the present study, Titanium carbon nitride (TiCN) thin films have been synthesized over Si (100) substrate using chemical vapour deposition (CVD) process under N2 gas flow rate to study the suitability of CVD for the growth of TiCN coating over machine tools. The morphological, structural, corrosion behavior and mechanical properties of the coatings have been characterized using a scanning electron microscope (SEM), atomic force microscopy (AFM), x-ray diffraction (XRD), corrosion test and Nano-indentation. SEM images revealed a smoother morphology of the TiCN coating surface without any pores. The AFM results revealed an increase in surface roughness from 40.88 nm to 48.25 nm. The particle size of TiCN thin film also found to be increasing with a higher N2 flow rate. The XRD peaks of TiCN were observed in the range of 37°-38°. 44°-44.5° and 61°- 62° attributed to (111), (200) and (220) crystal plane. The XRD results also confirmed the presence of stress in the coatings. The polarization test indicates a reduction of corrosion resistance with a higher N2 gas flow rate. The mechanical properties investigated by the nano-indentation method indicated an increase in Hardness (H) and Young's modulus (E) of the coating with a higher N2 flow rate. The maximum H and E of the coatings were observed as 27.85 GPa and 486.22 GPa respectively.

In the current research, diamond-like carbon thin films are deposited on silicon substrates by plasma-enhanced chemical vapor deposition. The effect of argon-C2H2 flow rate, hydrogen flow rate and deposition temperature on the thin film hardness is investigated. Morphology of the DLC films is investigated by scanning electron microscopy and atomic force microscopy, while the nano-hardness is investigated using nanoindentation. Raman spectroscopy is used for the characterization of the structural properties of the film. A metamodel of the DLC deposition process with argon- C2H2 flow rate, H2 flow rate and deposition temperature as the regressor variables and coating hardness as the response is built by using a novel symbolic regression approach. A state-of-the-art machine learning approach - genetic programming (GP) - is used for the symbolic regression. By carefully evaluating the performance of the current GP metamodel against a classical RSM (response surface methodology) metamodel, it is seen that the GP significantly outperforms RSM.

Titanium aluminum nitride (TiAlN) thin films were synthesized with different N2 flow rate on p-type Si (100) substrate and characterized by FESEM, AFM, XRD and nanoindentation to investigate the morphology, structural and mechanical properties. The FESEM images revealed non-uniform grain distribution with a higher degree of pores with the increase in N2 flow rate. The XRD data indicated the presence of AlN, TiAlN, Al2O3 dominant phases at 33.07°, 55.5°, 61.79° and 47.84°, 54.67° diffraction angles attributed to (100), (220) and (400), (112) diffraction plane, respectively. The AFM images revealed the average roughness as 36.9 nm for 30 sccm N2 flow rate. Furthermore, the mechanical properties such as H, E, plasticity index (H/E) and deformation to plastic resistance (H3/E2) of TiAlN thin film were found to be 21 GPa, 400 GPa, 0.094 and 0.318 GPa, respectively, indicating higher resistance to crack with increasing N2 flow rate.

Diamond-like carbon (DLC) thin films find extensive applications in manufacturing, aerospace, and medical sector and thus, a huge need exists to optimize its deposition process. However, the experi...

Electrical discharge machining (EDM) is a broadly used nonconventional material removal process for the machining of conductive work material irrespective of their hardness. In this article, empirical models for material removal rate (MRR) and surface roughness (Ra) of the workpiece are developed based on the extensive experiments performed on a special steel (WP7V) workpiece using a copper electrode. To account for the various parameters, an experimental design based on response surface methodology (RSM) is conducted considering three different factors namely — current, pulse-on-time, and pulse-off-time, each having three different levels. Analysis of variance (ANOVA) is conducted to test the statistical significance of the proposed empirical models. It is essential to determine the relationship and significance of input–output variation. Thus a sensitivity analysis is conducted. The interaction effect of input variables is also studied. Two different state-of-art optimization techniques, namely genetic algorithm (GA) and particle swarm optimization (PSO), are applied to predict the optimal combination of process parameters. Finally, multi-objective optimization is also carried out to simultaneously maximize MRR while minimizing Ra.

Defects associated with casting of pipes are often a main concern for the industry. In this chapter, a Taguchi analysis is carried out to understand the effect of three process parameter pouring temperature (°C), die spinning speed (rpm), and coolant flow time (mins) on the casting defect of pipes. The defect is defined in this work as the difference between the desired thickness of the pipe and the minimum actual (experimentally) achieved. A L9 orthogonal array is designed to carry out the experiments. Based on the S/N ratio analysis and ANOVA, it is seen that the die spinning speed plays the most critical role in defect of the pipes. As per the conducted experiments and Taguchi analysis, pouring temperature is seen to have the lest influence on the defects.

Obtaining quality castings is of significant interest to researchers and practitioner due to the widespread use of casting components in everyday and industrial use. In this work, Taguchi method is used to optimize the process parameters involved in casting. Three casting parameters, namely pouring temperature (°C), sand particle size (AFS), and mould hardness (No), are considered. The percentage defect in the casting components is considered as the response. A L9 orthogonal array is used for design of experiments for carrying out the casting experiments. Based on the S/N ratio analysis and ANOVA, it is seen that the sand particle size has the most contribution to the linear model. Further, it is found that the level-wise optimal combination of the casting process parameters for minimizing the defects is Level 3 for pouring temperature, Level 2 for sand particle size, and Level 3 for mould hardness.

The conventional methods like linear or polynomial regression, despite their overwhelming accuracy on training data, often fail to achieve the same accuracy on independent test data. In this research, a comparative study of three different machine learning techniques (linear regression, random forest regression, and AdaBoost) is carried out to build predictive models for dry electric discharge machining process. Six different process parameters namely voltage gap, discharge current, pulse-on-time, duty factor, air inlet pressure, and spindle speed are considered to predict the material removal rate. Statistical tests on independent test data show that despite linear regression's considerable accuracy on training data, it fails to achieve the same on independent test data. Random forest regression is seen to have the best performance among the three predictive models.

In this research, a data-driven approach to metamodeling of manufacturing/machining processes is developed. Instead of the conventionally used second-order polynomial regression metamodels, a non-predefined form-free approach is discussed. The highly adaptive metamodeling strategy, called symbolic regression, is carried out by using genetic programming. A central composite design based experimental dataset on electric discharge machining is used as the training and the testing data. Four different process parameters namely (voltage, pulse on time, pulse off time, and current) are used as the independent parameters to quantify three different responses (material removal rate, electrode wear rate, and surface roughness). The performance of the metamodels are evaluated by using various statistical metrics like R2, MAE, MSE. The performance of the metamodels on the training and testing data is found to be adequate for all the responses.

In this article, an improved variant of the cuckoo search (CS) algorithm named Coevolutionary Host-Parasite (CHP) is used for maximizing the metal removal rate in a turning process. The spindle speed, feed rate and depth of cut are considered as the independent parameters that describe the metal removal rate during the turning operation. A data-driven second-order polynomial regression approach is used for this purpose. The training dataset is designed using an L16 orthogonal array. The CHP algorithm is effective in quickly locating the global optima. Furthermore, CHP is seen to be sufficiently robust in the sense that it is able to identify the optima on independent reruns. The CHP predicted optimal solution presents &plusmn, 10% deviations in the optimal process parameters, which shows the robustness of the optimal solution.

Wire electro discharge machining (WEDM) is a versatile non-traditional machining process that is extensively in use to machine the components having intricate profiles and shapes. In WEDM, it is very important to select the optimal process parameters so as to enhance the machine performance. This paper emphasizes the selection of optimal parametric combination of WEDM process while machining on EN31 steel, using grey-fuzzy logic technique. Process parameters such as servo voltage, wire tension, pulse-on-time and pulse-off-time were considered while taking into account several multi-responses such as material removal rate (MRR) and surface roughness (SR). It was found that pulse-on-time of 115 µs, pulse-off-time of 35 µs, servo voltage of 40 V and wire tension of 5 kgf results in a larger value of grey fuzzy reasoning grade (GFRG) which tends to maximize MRR and improve SR. Finally, analysis of variance (ANOVA) is applied to check the influence of each process parameters in the estimation of GFRG.

Diamond-like carbon thin film coatings are extensively used in manufacturing, medical and aerospace industries due to their excellent tribological, mechanical, optical and biological properties. Du...

The process parameter selection in any machining process is one of the most challenging criteria for effective machining. Selection of an optimal parametric combination always results in efficient machining. In the current work, a fuzzy TOPSIS method for selection of optimal laser beam micro-marking process parameters, such as current, pulse frequency, and scanning speed, during laser beam marking on gallium nitride is adopted. Response surface methodology (central composite design) is adopted for the experimental design. The mark width, mark depth, and mark intensity are considered as the process responses. The mark width and mark depth are measured with the help of a precision optical microscope. Microscopic images are then treated using image processing software to calculate the mark intensity. A multi-criteria decision-making model is developed considering the effect of process parameters on process response for selection of optimal process parameter in the laser beam micro-marking process. The triangular fuzzy number is used to calculate the weight of each performance criteria, and the fuzzy TOPSIS hybrid approach is used for selection of optimal parametric combination. The results indicate that a small pulse frequency and high current and scanning speed lead to increase in mark intensity.

Laser micro-marking is an efficient technique for permanent marking and logo printing on materials. This study details the selection of an optimal parametric combination for laser micro-marking. In this work, markings were performed on Gallium Nitride (GaN) with varying the levels of marking parameters. The parameters considered in the present work are current (A), pulse frequency (Hz), and scanning speed (mm/sec). This experiment was designed using a “central composite design,” grounded in the response surface methodology. Mark intensity, which is a prominent response in laser marking, was considered the output response. The data interpretation involved analysis of variance (ANOVA) and mathematical modelling between the input parameters. It is essential to determine the relationship and significance of input-output variation. The interaction effect of various input parameters on mark intensity was also studied. Finally, two techniques, namely genetic algorithm (GA) and particle swarm optimization...

Due to the numerous process parameters involved in the diamond-like carbon (DLC) thin film deposition process, it is practically impossible to find the optimum settings of the deposition process that would lead towards the most desired coating. The optimal parameter selection process gets further complicated when multiple objectives need to be satisfied— which are often in conflict with each other. Though the use of metaheuristics that leads towards Pareto frontier may be a viable solution, it is generally computationally intensive. Thus, in this paper, four different multi-criteria decision-making approaches (weighted sum model, weighted product model, weighted aggregated sum product assessment and technique for order preference by similarity to the ideal solution) are used to optimize the parameters involved in Zr-DLC deposition process. The bias voltage, Zirconium target current, pulse frequency, methane gas flow rate and work distance are selected as the controllable factors. Minimization of wear rate and maximization of deposition rate are the desired goals.

Titanium nitride (TiN), Titanium aluminum nitride (TiAlN), and Titanium aluminum silicon nitride (TiAlSiN) coatings were deposited over Silicon (Si) (100) substrates using chemical vapor deposition (CVD) process at a constant temperature under different N2 flow rate. Scanning electron microscopy (SEM) images reveal a smoother TiN coating surface compared to other coatings. Atomic force microscopy (AFM) indicates enhancement of particle size of TiN and TiAlN coatings with higher N2 gas flow rate. However, the particle size of TiAlSiN found to decrease with a higher N2 flow rate. X-ray diffraction (XRD) results confirm the diffraction phases of TiN, TiAlN and TiAlSiN at (111) and (200) crystal planes. The electrochemical test indicates higher corrosion resistance of TiAlSiN coating as compared to other coatings. Nano-indentation results reveal that TiAlSiN possesses higher hardness (H) and Young's modulus (E) as compared to other coatings.

In the present work, multi-response optimization of electro-discharge machining (EDM) process is carried out based on an experimental analysis of machining superalloy Inconel-718. The study aims at optimizing and determining an optimal set of process variables, namely discharge current (), pulse-on duration () and dielectric fluid-pressure () for achieving optimal machining performance in EDM. Nine independent experiments based on L9 orthogonal array are carried out by using tungsten as the electrode. The productivity performance of the EDM process is measured in terms of material removal rate (MRR) and its cost parameter is measured in terms of tool wear rate (TWR) and electrode wear rate (EWR). The TOPSIS is used in conjunction with five different criterion weight allocation strategies— (namely, mean weight (MW), standard deviation (SDV), entropy, analytic hierarchy process (AHP) and Fuzzy). While MW, SDV and entropy are based on the objective evaluation of the decision-maker (DM), the AHP can model the DM’s subjective evaluation. On the other hand, the uncertainty in the DM’s evaluation is analyzed by using the fuzzy weighing approach.

The current paper presents a comprehensive finite element analysis on stress concentration in orthotropic laminates. This research investigates the Stress Concentration Factor (SCF) in isotropic and orthotropic plates due to presence of a central cutout with respect to ply orientation angle (θ). The numerical simulations are carried out by using an 8 node shell element in ANSYS APDL environment. It is established from the simulations that the SCF is dependent upon the orientation the fibers. For orthotropic laminates, SCF depends on material properties. It increases with increase in Ey/Ex ratio and decreases with increase in μxy.

Titanium aluminium nitride (TiAlN) thin films were deposited by chemical vapour deposition using TiO2 powder, Al powder and N2 gas. The morphology and mechanical properties of the films were characterized by scanning electron microscopy and nanoindentation technique, respectively. The structural properties were characterized by Raman spectroscopy and X-ray diffraction. The XRD result shows TiAlN films are of NaCl-type metal nitride structure. Micro-Raman peaks of the TiAlN thin film were observed within 450 and 642 cm−1 for acoustic and optic range, respectively. A maximum hardness and Young modulus up to 22 and 272.15 GPa, respectively, were observed in the TiAlN film deposited at 1200 °C.

Titanium aluminium nitride (TiAlN) thin film coatings were synthesized via Atmospheric pressure chemical vapour deposition (APCVD) at various deposition temperature using titanium di-oxide (TiO2) powder, Al powder (99% pure) and N2 gas. The thin films were characterized by scanning electron microscopy (SEM), Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) for analysing the morphological, vibrational and compositional characteristics of the deposited film. SEM images showed successful deposition of TiAlN thin film over Si wafers. Raman peaks revealed the optical phonon bands of the coated thin film within a range of 214.6-335.1 cm-1and 552.63-815.23 cm-1 for acoustic and optic range respectively at different processing temperatures. XPS spectra clearly showed the presence of different phases like Ti-N, Ti-O-N, Al2O3, N-Al, TiO2 Al-O in Ti (2p), Al (2p) and N (1s) orbitals, which is in a good agreement with past work.

In the present study a comprehensive work has been carried out to investigate the influence of dielectric on machining parameters of EDM process. An EDM is a non- traditional thermo- erosive process used to machine a materials irrespective of their hardness. It produced a highly precised and better surface topography in materials. Dielectric is an important parameter in EDM and plays a crucial role in determining high material removal rate (MRR) and surface finish during operation. The dielectric fluid behave as a medium which controls the electrical discharge and absorb heat during process. It remove the debris particles and cool the work-piece. Adding of powder such as Ti, Si, graphite, Cu, Al2O3 etc. in dielectric fluid increases the fluid conductive nature with an increase in micro-hardness of material. Selecting suitable dielectric from different present fluids is highly essential for conducting experiment in EDM. This paper presents the literature views of use of different dielectrics and its effect on optimization of machining parameters and respective characteristic properties.