Surface Analysis of Wire-Electrical-Discharge-Machining-Processed Shape-Memory Alloys

Experimental Investigations of Using Aluminum Oxide (Al2O3) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy

In the present study, a comprehensive parametric analysis was carried out using the electrical discharge machining of Ti6Al4V, using pulse-on time, current, and pulse-off time as input factors with output measures of surface roughness and material removal rate. The present study also used two different nanopowders, namely alumina and nano-graphene, to analyze their effect on output measures and surface defects. All the experimental runs were performed using Taguchi's array at three levels. Analysis of variance was employed to study the statistical significance. Empirical relations were generated through Minitab. The regression model term was observed to be significant for both the output responses, which suggested that the generated regressions were adequate. Among the input factors, pulse-off time and current were found to have a vital role in the change in material removal rate, while pulse-on time was observed as a vital input parameter. For surface quality, pulse-on time and pulse-off time were recognized to be influential parameters, while current was observed to be an insignificant factor. Teaching-learning-based optimization was used for the optimization of output responses. The influence of alumina and nano-graphene powder was investigated at optimal process parameters. The machining performance was significantly improved by using both powder-mixed electrical discharge machining as compared to the conventional method. Due to the higher conductivity of nano-graphene powder, it showed a larger improvement as compared to alumina powder. Lastly, scanning electron microscopy was operated to investigate the impact of alumina and graphene powder on surface morphology. The machined surface obtained for the conventional process depicted more surface defects than the powder-mixed process, which is key in aeronautical applications.

Implementation of Passing Vehicle Search Algorithm for Optimization of WEDM Process of Nickel-Based Superalloy Waspaloy

Nickel-based superalloys find their main use in missile engines, atomic devices, investigational aircraft, aerospace engineering, industrial applications, and automotive gas turbines, spacecraft petrochemical tools, steam power, submarines, and broader heating applications. These superalloys impose certain difficulties during the process fabrication owing to their levels of higher hardness. In the current study, the precise machining of Waspaloy was attempted through the wire electrical discharge machining (WEDM) technique. A multi-objective optimization has been performed, and the influence of multi-walled carbon nanotubes (MWCNTs) has been assessed using the passing vehicle search (PVS) algorithm. The effects of machining variables like current, T_off, and T_on were studied using the output measures of material removal rate (MRR), recast layer thickness (RLT), and surface roughness (SR). The Box-Behnken design was applied to generate the experimental matrix. Empirical models were generated which show the interrelationship among the process variables and output measures. The analysis of variance (ANOVA) method was used to check the adequacy, and suitability of the models and to understand the significance of the parameters. The PVS technique was executed for the optimization of MRR, SR, and RLT. Pareto fronts were derived which gives a choice to the user to select any point on the front as per the requirement. To enhance the machining performance, MWCNTs mixed dielectric fluid was utilized, and the effect of these MWCNTs was also analyzed on the surface defects. The use of MWCNTs at 1 g/L enhanced the performance of MRR, SR, and RLT by 65.70%, 50.68%, and 40.96%, respectively. Also, the addition of MWCNTs has shown that the machined surface largely reduces the surface defects.

Review of Neural Network Modeling of Shape Memory Alloys

Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning.

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (T_on), and pulse-off-time (T_off) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and T_on were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm³/s at T_on of 95 µs, T_off of 9 µs, current of 6 A. Least SR was obtained at T_on of 35 µs, T_off of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

Shape memory alloy (SMA), particularly those having a nickel-titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of Al₂O₃ nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (T_on), pulse-off time (T_off), and Al₂O₃ nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi's three levels L₉ approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. Al₂O₃ nanopowder was found to have the highest contributing effect of 76.13% contribution, T_on was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at T_on of 90 µs, T_off of 5 µs, and powder concentration of 2 g/L), the lowest SR value of 3.13 µm, and the lowest RLT value of 10.24 (both responses at T_on of 30 µs, T_off of 25 µs, and powder concentration of 2 g/L). A specific multi-objective Teaching-Learning-Based Optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effectiveness of both the regression model and the TLBO algorithms. Confirmatory trials have shown an extremely close relation which shows the suitability of both the regression model and the TLBO algorithm for the machining of the nanopowder-mixed WEDM process for Nitinol SMA. A considerable reduction in surface defects owing to the addition of Al₂O₃ powder was observed in surface morphology analysis.

Salt Heat Treatment and Passivation to Improve the Corrosion Resistance of Nitinol (Ni-Ti)

Corrosion of nitinol (NiTi) is a major factor in the failure of implantable materials. Recently, as the importance of corrosion of metals has increased, testing according to international guidelines is essential. The purpose of this study was to evaluate the corrosion resistance of NiTi wire through heat treatment and passivation process. In this study, NiTi wire used two commercially available products and a self-manufactured stent. Experimental consideration was carried out according to ASTM standards. Heat treatment was carried out in an air or a salt furnace, and the corrosion was measured after additional process, such as passivation and scratch tests. As a result, the metal potential was rapidly decreased in the air furnace group. On the other hand, the potential of wires was dramatically increased in the salt furnace group compared to the air furnace group. The dislocation decreased below the acceptance criteria (>600 mV) within 60 s of heat treatment time in the air furnace. Moreover, the potential was dramatically improved, even after only 20 min of passivation treatment (1076 mV, 442% compared to the non-passivated group), and it continued to rise until 180 min. This phenomenon was similarly observed in the group of self-manufactured stents. The potential slightly decreased by the scratch process (93.1%) was significantly reduced by the air furnace process (315 mV, 24.4% of the nontreated group). In the passivated group of the air furnace sample with reduced potential, the potential was restored to the level before the air furnace (scratch stage) (1032 mV). In conclusion, the heat treatment is preferably carried out in a salt furnace rather than an air furnace, and the passivation process can be an advantageous tool to improve corrosion resistance by suppressing the oxidation process.

Multi-Response Optimization of Abrasive Waterjet Machining of Ti6Al4V Using Integrated Approach of Utilized Heat Transfer Search Algorithm and RSM

Machining of Titanium alloys (Ti6Al4V) becomes more vital due to its essential role in biomedical, aerospace, and many other industries owing to the enhanced engineering properties. In the current study, a Box–Behnken design of the response surface methodology (RSM) was used to investigate the performance of the abrasive water jet machining (AWJM) of Ti6Al4V. For process parameter optimization, a systematic strategy combining RSM and a heat-transfer search (HTS) algorithm was investigated. The nozzle traverse speed (T_v), abrasive mass flow rate (A_f), and stand-off distance (S_d) were selected as AWJM variables, whereas the material removal rate (MRR), surface roughness (SR), and kerf taper angle (θ) were considered as output responses. Statistical models were developed for the response, and Analysis of variance (ANOVA) was executed for determining the robustness of responses. The single objective optimization result yielded a maximum MRR of 0.2304 g/min (at T_v of 250 mm/min, A_f of 500 g/min, and S_d of 1.5 mm), a minimum SR of 2.99 µm, and a minimum θ of 1.72 (both responses at T_v of 150 mm/min, A_f of 500 g/min, and S_d of 1.5 mm). A multi-objective HTS algorithm was implemented, and Pareto optimal points were produced. 3D and 2D plots were plotted using Pareto optimal points, which highlighted the non-dominant feasible solutions. The effectiveness of the suggested model was proved in predicting and optimizing the AWJM variables. The surface morphology of the machined surfaces was investigated using the scanning electron microscope. The confirmation test was performed using optimized cutting parameters to validate the results.

Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM

Titanium and its alloys exhibit numerous uses in aerospace, automobile, biomedical and marine industries because of their enhanced mechanical properties. However, the machinability of titanium alloys can be cumbersome due to their lower density, high hardness, low thermal conductivity, and low elastic modulus. The wire electrical discharge machining (WEDM) process is an effective choice for machining titanium and its alloys due to its unique machining characteristics. The present work proposes multi-objective optimization of WEDM on Ti6Al4V alloy using a fuzzy integrated multi-criteria decision-making (MCDM) approach. The use of MCDM has become an active area of research due to its proven ability to solve complex problems. The novelty of the present work is to use integrated fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal situation (TOPSIS) to optimize the WEDM process. The experiments were systematically conducted adapting the face-centered central composite design approach of response surface methodology. Three independent factors-pulse-on time (Ton), pulse-off time (Toff), and current-were chosen, each having three levels to monitor the process response in terms of cutting speed (VC), material removal rate (MRR), and surface roughness (SR). To assess the relevance and significance of the models, an analysis of variance was carried out. The optimal process parameters after integrating fuzzy AHP coupled with fuzzy TOPSIS approach found were Ton = 40 µs, Toff = 15 µs, and current = 2A.

Experimental Investigations and Pareto Optimization of Fiber Laser Cutting Process of Ti6Al4V

In the current study, laser cutting of Ti6Al4V was accomplished using Taguchi’s L9 orthogonal array (OA). Laser power, cutting speed, and gas pressure were selected as input process parameters, whereas surface roughness (SR), kerf width, dross height, and material removal rate (MRR) were considered as output variables. The effects of input variables were analyzed through the analysis of variance (ANOVA), main effect plots, residual plots, and contour plots. A heat transfer search algorithm was used to optimize the parameters for the single objective function including higher MRR, minimum SR, minimum dross, and minimum kerf. A multi-objective heat transfer search algorithm was used to create non-dominant optimal Pareto points, giving unique optimal solutions with the corresponding input parameters. For better understanding and ease of selection of input parameters in industry and by scientists, a Pareto graph (2D and 3D graph) is generated from the Pareto points.

Synthesis and Characterization of Mechanical Properties and Wire Cut EDM Process Parameters Analysis in AZ61 Magnesium Alloy + B4C + SiC

Wire Cut Electric Discharge Machining (WCEDM) is a novel method for machining different materials with application of electrical energy by the movement of wire electrode. For this work, an AZ61 magnesium alloy with reinforcement of boron carbide and silicon carbide in different percentage levels was used and a plate was formed through stir casting technique. The process parameters of the stir casting process are namely reinforcement %, stirring speed, time of stirring, and process temperature. The specimens were removed from the casted AZ61 magnesium alloy composites through the Wire Cut Electric Discharge Machining (WCEDM) process, the material removal rate and surface roughness vales were carried out creatively. L 16 orthogonal array (OA) was used for this work to find the material removal rate (MRR) and surface roughness. The process parameters of WCEDM are pulse on time (105, 110, 115 and 120 µs), pulse off time (40, 50, 60 and 70 µs), wire feed rate (2, 4, 6 and 8 m/min), and current (3, 6, 9 and 12 Amps). Further, this study aimed to estimate the maximum ultimate tensile strength and micro hardness of the reinforced composites using the Taguchi route.

Elemental and Thermochemical Analyses of Materials after Electrical Discharge Machining in Water: Focus on Ni and Zn

The mechanism of the material destruction under discharge pulses and material removal mechanism based on the thermochemical nature of the electrical erosion during electrical discharge machining of conductive materials were researched. The experiments were conducted for two structural materials used in the aerospace industry, namely austenite anticorrosion X10CrNiTi18-10 (12kH18N10T) steel and 2024 (D16) duralumin, machined by a brass tool of 0.25 mm in diameter in a deionized water medium. The optimized wire electrical discharge machining factors, measured discharge gaps (recommended offset is 170–175 µm and 195–199 µm, respectively), X-ray photoelectron spectroscopy for both types of materials are reported. Elemental analysis showed the presence of metallic Zn, CuO, iron oxides, chromium oxides, and 58.07% carbides (precipitation and normal atmospheric contamination) for steel and the presence of metallic Zn, CuO, ZnO, aluminum oxide, and 40.37% carbides (contamination) for duralumin. For the first time, calculating the thermochemistry parameters for reactions of Zn(OH)₂, ZnO, and NiO formation was produced. The ability of Ni of chrome–nickel steel to interact with Zn of brass electrode was thermochemically proved. The standard enthalpy of the Ni₅Zn₂₁ intermetallic compound formation (erosion dust) ΔH⁰₂₉₈ is −225.96 kJ/mol; the entropy of the crystalline phase S^c_int is 424.64 J/(mol·K).

Parametric Optimization and Effect of Nano-Graphene Mixed Dielectric Fluid on Performance of Wire Electrical Discharge Machining Process of Ni55.8Ti Shape Memory Alloy

In the current scenario of manufacturing competitiveness, it is a requirement that new technologies are implemented in order to overcome the challenges of achieving component accuracy, high quality, acceptable surface finish, an increase in the production rate, and enhanced product life with a reduced environmental impact. Along with these conventional challenges, the machining of newly developed smart materials, such as shape memory alloys, also require inputs of intelligent machining strategies. Wire electrical discharge machining (WEDM) is one of the non-traditional machining methods which is independent of the mechanical properties of the work sample and is best suited for machining nitinol shape memory alloys. Nano powder-mixed dielectric fluid for the WEDM process is one of the ways of improving the process capabilities. In the current study, Taguchi's L16 orthogonal array was implemented to perform the experiments. Current, pulse-on time, pulse-off time, and nano-graphene powder concentration were selected as input process parameters, with material removal rate (MRR) and surface roughness (SR) as output machining characteristics for investigations. The heat transfer search (HTS) algorithm was implemented for obtaining optimal combinations of input parameters for MRR and SR. Single objective optimization showed a maximum MRR of 1.55 mm³/s, and minimum SR of 2.68 µm. The Pareto curve was generated which gives the optimal non-dominant solutions.

The Accuracy of Finishing WEDM of Inconel 718 Turbine Disc Fir Tree Slots

Servicing aircraft engines sometimes requires manufacturing only a single piece of a given part. Manufacturing a turbine disc using traditional methods is uneconomical. It is necessary to use a different machining method recommended for small lot production. One of the proposed methods is WEDM (wire electrical discharge machining). The article presents the results of the research on finishing WEDM of Inconel 718 turbine disc fir tree slots. The influence of infeed, mean gap voltage, peak current, pulse off-time, and discharge energy on the shape accuracy, surface roughness, microcracks, and the white layer thickness were determined. Mathematical models were developed based on the DoE (Design of Experiment) analysis. The statistical significance of the models was verified with the ANOVA (Analysis of Variance) test. The machining parameters control methods that allow achieving the required shape accuracy, surface roughness, and surface layer condition were presented. The obtained surface roughness was Ra = 0.84 μm, the shape accuracy of the slot in the normal-to-feed direction was Δd = 0.009 μm, the profile shape accuracy was Δr = 0.033 μm, and the thickness of recast (white) layer was approximately 5 μm.

Experimental Research on the Cutting of Metal Materials by Electrical Discharge Machining with Contact Breaking with Metal Band as Transfer Object

The scientific paper presents practical research carried out by a mixed team of Romanian researchers from universities and the business environment. The research consists in applying the process of cutting metallic materials through electrical discharge machining with contact breaking using a metal band as a transfer object. The research was implemented with the help of a specially designed installation in the laboratory and subsequently all the necessary steps were taken to obtain the patent for it. Various metallic materials were cut using this process, but first of all, high alloy steels. In the global research conducted by the authors, active experimental programs and classic experimental programs were used. The composite central factorial experiment was the method that led to the most effective results in terms of interpretations and conclusions. The research as a whole includes unique elements from an engineering point of view and here we can highlight the use of a metal band as a transfer object for this type of process as well as the designed, realized, and subsequently patented installation.

Effect of WEDM Process Parameters on Surface Morphology of Nitinol Shape Memory Alloy

Nickel–titanium shape memory alloys (SMAs) have started becoming popular owing to their unique ability to memorize or regain their original shape from the plastically deformed condition by means of heating or magnetic or mechanical loading. Nickel–titanium alloys, commonly known as nitinol, have been widely used in actuators, microelectromechanical system (MEMS) devices, and many other applications, including in the biomedical, aerospace, and automotive fields. However, nitinol is a difficult-to-cut material because of its versatile specific properties such as the shape memory effect, superelasticity, high specific strength, high wear and corrosion resistance, and severe strain hardening. There are several challenges faced when machining nitinol SMA with conventional machining techniques. Noncontact operation of the wire electrical discharge machining (WEDM) process between the tool (wire) and workpiece significantly eliminates the problems of conventional machining processes. The WEDM process consists of multiple input parameters that should be controlled to obtain great surface quality. In this study, the effect of WEDM process parameters on the surface morphology of nitinol SMA was studied using 3D surface analysis, scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX) analysis. 3D surface analysis results indicated a higher value of surface roughness (SR) on the top of the work surface and a lower SR on the bottom portion of the work surface. The surface morphology of the machined sample obtained at optimized parameters showed a reduction in microcracks, micropores, and globules in comparison with the machined surface obtained at a high discharge energy level. EDX analysis indicated a machined surface free of molybdenum (tool electrode).

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

The paper presents a procedure for the determination of uncertainties in the modeling of surface roughness in the turning of NiTi alloys. The presented procedure is applicable both to the analysis of the measurement values of the two main roughness factors, as well as to research related to the prediction and optimization of the machining process. Type A and B, total, and expanded uncertainties were considered herein, and the obtained uncertainty values were assessed. A procedure for optimizing machining by applying the Monte Carlo (MC) method is also presented. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the machining process. The considered procedure for determining and assessing uncertainty can be useful for the optimal selection of both machining parameters and measuring tools.

Study of Micro Structural Material Changes after WEDM Based on TEM Lamella Analysis

Wire electrical discharge machining is an unconventional machining technology that is crucial in many industries. The surface quality of the machined parts is carefully monitored, but the condition of the subsurface layer also plays a crucial role, especially in case of defects occurrence such as cracks or burnt cavities. The subsurface layer of individual materials is affected differently due to wire electrical discharge machining. For this reason, this study was carried out focusing on a detailed analysis of transmission electron microscope (TEM) lamella made of Ti-6Al-4V titanium alloy, AlZn6Mg2Cu aluminum alloy, pure molybdenum, Creusabro 4800 steel, and Hardox 400 steel. The attention was first of all paid to the concentration and distribution of individual elements in the recast layer and also in the base material, which was often affected by wire electrical discharge machining. Further, a diffraction analysis was performed for each TEM lamella in the adhesive area and in the base material area. In order to assess the macro-effects on the machined material, the topography analysis of the machined surfaces and the morphology analysis were performed using electron microscopy.

Obtaining a Wire of Biocompatible Superelastic Alloy Ti-28Nb-5Zr

Using the methods of electric arc melting, intermediate heat treatments, and consecutive intensive plastic deformation, a Ti-Nb-Zr alloy wire with a diameter of 1200 μm was obtained with a homogeneous chemical and phase (β-Ti body-centered crystal lattice) composition corresponding to the presence of superelasticity and shape memory effect, corrosion resistance and biocompatibility. Perhaps the wire structure is represented by grains with a nanoscale diameter. For the wire obtained after stabilizing annealing, the proof strength Rp0.2 is 635 MPa, tensile strength is 840 MPa and Young's modulus is 22 GPa, relative elongation is 6.76%. No toxicity was detected. The resulting wire is considered to be promising for medical use.