scholarly journals Experimental investigations and optimization of MWCNTs-mixed WEDM process parameters of nitinol shape memory alloy

2021 ◽  
Author(s):  
Rakesh Chaudhari ◽  
Sakshum Khanna ◽  
Jay Vora ◽  
Vivek K. Patel ◽  
Sagar Paneliya ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Process Parameters ◽  
Experimental Investigations ◽  
Wedm Process

Multi-Response Optimization of Micro-WEDM Process Parameters of Ti49.4-Ni50.6 Shape Memory Alloy for Orthopedic Implant Application

2018 ◽  
Vol 1150 ◽  
pp. 1-21
Author(s):  
Adik M. Takale ◽  
Nagesh K. Chougule ◽  
Preetam H. Selmokar ◽  
M.G. Gawari
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Process Parameters ◽  
Removal Rate ◽  
Orthopedic Implant ◽  
Wire Tension ◽  
Response Optimization ◽  
Micro Wedm ◽  
Wedm Process ◽  
Wire Feed

The present work deals with the optimization of micro-WEDM process parameters for machining Ti49.4-Ni50.6 shape memory alloy (SMA) for orthopedic implant application. Effect of micro-WEDM parameters viz. Gap voltage, capacitance, wire feed and wire tension on the response variables such as material removal rate, surface roughness, kerf width and dimensional deviation is determined. As Ti-Ni SMA has fascinating properties and bio-compatibility, have been considered for present work. Nine experiments have been performed on micro-WEDM based on an orthogonal array of Taguchi method. Subsequently, the grey relational analysis (GRA) method is applied to determine an optimal set of process parameters. It is observed that optimized set of parameters A3B3C3D1 viz. 140 V gap voltage, 0.4 µF capacitance, wire feed 30 µm/sec and 30% of wire tension determined by using GRA offers maximum MRR and minimum SR, KW and DD. From the Analysis of Variance, it is seen that the process parameter capacitance is the most significant parameter for multi-response optimization with a percentage contribution of 77.91%. Young’s modulus also checked for biocompatibility. Also, SEM images are taken to confirm the results offering better surface quality. Heat treatment process like annealing is found to be the most suitable to recover shape memory effect of WEDMed samples.

scholarly journals Multi-Response Optimization of WEDM Process Parameters for Machining of Superelastic Nitinol Shape-Memory Alloy Using a Heat-Transfer Search Algorithm

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1277 ◽  
Author(s):  
Rakesh Chaudhari ◽  
Jay J. Vora ◽  
S. S. Mani Prabu ◽  
I. A. Palani ◽  
Vivek K. Patel ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Shape Memory Effect ◽  
Memory Effect ◽  
Process Parameters ◽  
Discharge Process ◽  
The Wire ◽  
Pulse On Time ◽  
Wedm Process

Nitinol, a shape-memory alloy (SMA), is gaining popularity for use in various applications. Machining of these SMAs poses a challenge during conventional machining. Henceforth, in the current study, the wire-electric discharge process has been attempted to machine nickel-titanium (Ni55.8Ti) super-elastic SMA. Furthermore, to render the process viable for industry, a systematic approach comprising response surface methodology (RSM) and a heat-transfer search (HTS) algorithm has been strategized for optimization of process parameters. Pulse-on time, pulse-off time and current were considered as input process parameters, whereas material removal rate (MRR), surface roughness, and micro-hardness were considered as output responses. Residual plots were generated to check the robustness of analysis of variance (ANOVA) results and generated mathematical models. A multi-objective HTS algorithm was executed for generating 2-D and 3-D Pareto optimal points indicating the non-dominant feasible solutions. The proposed combined approach proved to be highly effective in predicting and optimizing the wire electrical discharge machining (WEDM) process parameters. Validation trials were carried out and the error between measured and predicted values was negligible. To ensure the existence of a shape-memory effect even after machining, a differential scanning calorimetry (DSC) test was carried out. The optimized parameters were found to machine the alloy appropriately with the intact shape memory effect.

Modeling and Analysis of Compressive Properties of Porous NiTi Shape Memory Alloy Using Artificial Neural Network

2008 ◽  
Vol 41-42 ◽  
pp. 135-140 ◽  
Author(s):  
Qiang Li ◽  
Xu Dong Sun ◽  
Jing Yuan Yu ◽  
Zhi Gang Liu ◽  
Kai Duan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Thermal Explosion ◽  
Process Parameters ◽  
Niti Shape Memory Alloy ◽  
Compressive Properties ◽  
Porous Niti ◽  
Bp Model

Artificial neural network (ANN) is an intriguing data processing technique. Over the last decade, it was applied widely in the chemistry field, but there were few applications in the porous NiTi shape memory alloy (SMA). In this paper, 32 sets of samples from thermal explosion experiments were used to build a three-layer BP (back propagation) neural network model. According to the registered BP model, the effect of process parameters including heating rate ( ), green density ( ) and particle size of Ti ( d ) on compressive properties of reacted products including ultimate compressive strength ( v D σ ) and ultimate compressive strain (ε ) was analyzed. The predicted results agree with the actual data within reasonable experimental error, which shows that the BP model is a practically very useful tool in the properties analysis and process parameters design of the porous NiTi SMA prepared by thermal explosion method.

MACHINABILITY ASSESSMENT OF SHAPE MEMORY ALLOY NITINOL DURING WEDM OPERATION: APPLICATION POTENTIAL OF TAGUCHI BASED AHP–DFA TECHNIQUE

2021 ◽  
Author(s):  
HIMADRI MAJUMDER ◽  
AKHTAR KHAN ◽  
DEEPAK KUMAR NAIK ◽  
CH. SATEESH KUMAR
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Process Parameters ◽  
Function Analysis ◽  
Kerf Width ◽  
Optimum Process ◽  
Application Potential ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Desirability Function Analysis

This paper exemplifies the feasibility of expanding a multi-criteria decision-making (MCDM) method to select optimum process parameters during the wire electrical discharge machining (WEDM) of nitinol. The application potential of combined desirability function analysis (DFA) and analytical hierarchy process (AHP) has been reported. Nitinol, a shape memory alloy (SMA), can memorize or retain its original shape when subjected to thermo-mechanical or magnetic loads. Four key input variables, like pulse on time ([Formula: see text], pulse off time ([Formula: see text], wire tension (WT), and wire feed (WF) have been studied to optimize three correlated responses, like kerf width, material removal rate (MRR), and surface roughness ([Formula: see text]. Process parameter permutations [Formula: see text]s, [Formula: see text]s, [Formula: see text] kg-F and [Formula: see text][Formula: see text]m/min were found to yield the optimum results. For the desired kerf width, MRR and [Formula: see text], the optimum process parameters were also achieved expending Taguchi’s signal-to-noise ratio. Validation results affirmed that the MCDM approach, AHP–DFA is a proficient strategy to select optimal input parameters for a preferred output eminence for WEDM of nitinol.

Adjustment of the scan track spacing and linear input energy to fabricate dense, pseudoelastic Nitinol shape memory alloy parts by selective laser melting

2021 ◽  
pp. 1045389X2110639
Author(s):  
Mohammadreza Zamani ◽  
Mahmoud Kadkhodaei ◽  
Mohsen Badrossamay ◽  
Ehsan Foroozmehr
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Selective Laser Melting ◽  
Process Parameters ◽  
Room Temperature ◽  
Optimal Parameters ◽  
Research Groups ◽  
Laser Melting ◽  
Influence Of Process Parameters ◽  
Transformation Temperatures

Nitinol is a well-known shape memory alloy (SMA) which is widely used due to its unique properties such as shape memory effect and pseudoelasticity. However, challenges fabricating Nitinol parts have limited the use of this alloy. Nowadays, additive manufacturing methods, specifically selective laser melting (SLM), are being used as an alternative to conventional methods for fabricating Nitinol specimens. Achieving a dense structure and controlling the transformation temperatures in such products have been among the most important challenges for several research groups. In the present study, fabrication of dense Nitinol parts by SLM together with control of their transformation temperatures is investigated with the main purpose of achieving pseudoelastic products at room temperature. For this purpose, the effect of process parameters on density, transformation temperatures, microstructure, hardness, and shape memory response are studied. The influence of process parameters on transformation temperatures varies depending on the amount of power so that the effect of scan tracks spacing for high powers is more pronounced than that for low powers. The hardness and compressive strength of the parts are also affected by the process parameters. Accordingly, optimal parameters are found to fabricate dense pseudoelastic parts with the ability of strain recovery at ambient temperature.

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