Experimental investigations and optimization of MWCNTs-mixed WEDM process parameters of nitinol shape memory alloy
TL;DR: In this paper, multi-walled carbon nanotubes (MWCNTs) were mixed with dielectric fluid in the wire electrical discharge machining (WEDM) process to enhance the machining performance of Nitinol shape memory alloy (SMA).
Abstract: Excellent characteristics of multi-walled carbon nanotubes (MWCNTs), such as higher toughness and stiffness, enlarged strength, and high thermal conductivity, make them an attractive choice to improve surface characteristics and machining performance. In the current study, MWCNTs mixed with dielectric fluid in the wire electrical discharge machining (WEDM) process was used to enhance the machining performance of Nitinol shape memory alloy (SMA). Significance of WEDM machining variables such as current, pulse-on time (Ton), pulse-off time (Toff), and variation in powder concentration of MWCNTs are studied on material removal rate (MRR) and surface roughness (SR). The addition of MWCNTs substantially improves the machining performance by increasing MRR and simultaneously reducing the SR. Improvement in the MRR of 75.42% and SR of 19.15% is achieved with the use of MWCNTs at 1 g/L in comparison to the conventional WEDM process. An advanced parameterless TLBO algorithm is used for simultaneous optimization of multiple responses. An advanced parameterless TLBO algorithm is used to find the optimal solution of multiple responses. Single objective optimization result has yielded maximum MRR of 0.5262 g/min at a current of 5 A, Ton 110 μs, Toff 1 μs and MWCNTs amount of 1 g/L while minimum SR of 1.27 μm at a current of 1 A, Ton 1 μs, Toff 24 μs and MWCNTs amount of 1 g/L. MOTLBO algorithm is used for simultaneous optimization of MRR and SR. Lastly, the surface integrity of machined surfaces using a field emission scanning electron microscope (FESEM) is also studied to evaluate the effect of MWCNTs on recast layer thickness (RLT) and other surface defects. The incorporation of MWCNTs has shown a substantial reduction in RLT and other surface defects such as reduction in globules of debris, melted material deposition, micro-crack-free, and micro-pores-free surfaces.
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TL;DR: In this paper , the performance of powder-mixed EDM of nitinol SMA with the considerations of design variables of current, pulse-on-time, nano-graphene powder concentration (PC), and pulse-off-time (T off ) on surface roughness, dimensional deviation (DD), and material removal rate (MRR).
Abstract: Excellent characteristics of Nitinol shape memory alloys (SMAs) makes them favourable for use in industrial applications. Precision machining of such advanced alloys becomes a key requirement for industrial applications. Conventional machining processes imposes many difficulties for nitinol SMAs. Electrical discharge machining (EDM) process is appropriate for fabricating intricate and complex profile geometries and also provides a better alternative for difficult-to-cut materials. Addition of nano-particles in an appropriate amount in the dielectric fluid improves the machining by producing good dimensional accuracy, higher productivity, and good surface finish for machining of newly developed advanced alloys. The current study investigated the performance of powder-mixed EDM of nitinol SMA with the considerations of design variables of current, pulse-on-time (T on ), nano-graphene powder concentration (PC), and pulse-off-time (T off ) on surface roughness, dimensional deviation (DD), and material removal rate (MRR). Taguchi's L9 (3 ˆ 4) design was employed to perform the experiments and Minitab 17 software was used for statistical analysis of design variables using ANOVA, residual plots, and main effect plots. ANOVA results depicted that PC, T on , and T off were identified to be the highest contributing parameters with 75.18%, 29.37%, and 45.72% to affect MRR, SR, and DD, respectively. Obtained results has depicted a preferred combined positive trend of increase in MRR with a simultaneous drop in SR and DD after the addition of nano-graphene PC. HST algorithm was used to optimize single and multiple responses. Validation trials were also conducted to reveal the ability and suitability of the HTS technique. Field emission scanning electron microscopy revealed the minor occurrence of resolidified debris particles, globules, micro-pores, and micro-cracks after the addition of nano-graphene PC at 2 g/L.
26 citations
TL;DR: In this article , the authors used Singular Generative Adversarial Network (SinGAN) and DenseNet deep learning models for the prediction of surface morphology and its correlation with the process parameters.
Abstract: Shape memory alloys (SMA) hold a very promising place in the field of manufacturing, especially in biomedical and aerospace applications. Owing to the unique and favorable properties such as pseudo elasticity, shape memory effect and Superelasticity, Nitinol is the most popular amongst other SMAs. However, a major challenge lies in the final surface features of the machined component. In the current study, Nitinol rods were machined using the wire electrical discharge machining (WEDM) process and subsequently, the surfaces were investigated using the Field emission scanning electron miscroscope (FESEM) technique for the features. In addition to this, Singular Generative Adversarial Network (SinGAN) and DenseNet deep learning models were prepared and applied for the prediction of surface morphology and its correlation with the process parameters. It was concluded from the study that the DenseNet model was highly effective in predicting the surface images with 100% average accuracy both with training and testing whereas the least average accuracy of 99.13% and 98.98% with training and testing respectively are observed with the MNB model. Thus, the proposed methodology can prove to be highly beneficial for prediction, specifically for manufacturing applications where the data is limited.
20 citations
TL;DR: In this article , the authors used a regression model and a teaching-learning-based optimization (TLBO) algorithm for the machining of the nanopowder-mixed WEDM process for Nitinol SMA.
Abstract: 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 (Ton), pulse-off time (Toff), 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, Ton 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 Ton of 90 µs, Toff 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 Ton of 30 µs, Toff 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.
17 citations
TL;DR: In this paper , the authors highlight the general concept of shape memory effect (SME), various types of SMA, recent developments in the fabrication technique, and its application have been discussed in detail.
13 citations
20 Oct 2021
TL;DR: In this paper, the authors used machine learning techniques (decision tree, random forest, generalized linear model, and neural network) to predict changes in tool shape during the machining of EN31 tool steel.
Abstract: In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.
12 citations
References
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TL;DR: Shape memory alloys (SMAs) are a class of shape memory materials (SMMs) which have the ability to "memorise" or retain their previous form when subjected to certain stimulus such as thermomechanical or magnetic variations.
2,818 citations
TL;DR: In this article, the deformation history of NiTi was established by photographically recording surface changes of a brittle coating as austenite-martensite phase transition fronts traverse the specimen.
524 citations
TL;DR: A fast non-iterative technique to visualize the total extracellular electrolyte concentration (EEC), which is a fundamental component of the conductivity, is presented.
Abstract: Techniques for electrical brain stimulation (EBS), in which weak electrical stimulation is applied to the brain, have been extensively studied in various therapeutic brain functional applications. The extracellular fluid in the brain is a complex electrolyte that is composed of different types of ions, such as sodium (Na+), potassium (K+), and calcium (Ca+). Abnormal levels of electrolytes can cause a variety of pathological disorders. In this paper, we present a novel technique to visualize the total electrolyte concentration in the extracellular compartment of biological tissues. The electrical conductivity of biological tissues can be expressed as a product of the concentration and the mobility of the ions. Magnetic resonance electrical impedance tomography (MREIT) investigates the electrical properties in a region of interest (ROI) at low frequencies (below 1 kHz) by injecting currents into the brain region. Combining with diffusion tensor MRI (DT-MRI), we analyze the relation between the concentration of ions and the electrical properties extracted from the magnetic flux density measurements using the MREIT technique. By measuring the magnetic flux density induced by EBS, we propose a fast non-iterative technique to visualize the total extracellular electrolyte concentration (EEC), which is a fundamental component of the conductivity. The proposed technique directly recovers the total EEC distribution associated with the water transport mobility tensor.
494 citations
TL;DR: Elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated and the effects of common controlling parameters such as the population size and the number of generations on the results are investigated.
Abstract: A B S T R A C T Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO) is one of the recently proposed population based algorithms which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.
461 citations
TL;DR: The aim was to characterize the VOC’s profile of the wholemeal flour and of the kernel to find out if any VOCs were specific to varieties and sample matrices, and the results show that it is possible describe samples using VOC profiles and protein data.
Abstract: In this paper volatile organic compounds (VOCs) from durum wheat cultivars and landraces were analyzed using PTR-TOF-MS. The aim was to characterize the VOC’s profile of the wholemeal flour and of the kernel to find out if any VOCs were specific to varieties and sample matrices. The VOC data is accompanied by SDS-PAGE analyses of the storage proteins (gliadins and glutenins). Statistical analyses was carried out both on the signals obtained by MS and on the protein profiles. The difference between the VOC profile of two cultivars or two preparations of the same sample - matrices, in this case kernel vs wholemeal flour - can be very subtle; the high resolution of PTR-TOF-MS - down to levels as low as pptv - made it possible to recognize these differences. The effects of grinding on the VOC profiles were analyzed using SIMPER and Tanglegram statistical methods. Our results show that it is possible describe samples using VOC profiles and protein data.
434 citations