Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.41-42.135 ◽

2008 ◽

Vol 41-42 ◽

pp. 135-140 ◽

Cited By ~ 1

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1150.1 ◽

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.

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