Advancements in the Additive Manufacturing of Magnesium and Aluminum Alloys through Laser-Based Approach.

Complex structures can now be manufactured easily utilizing AM technologies to meet the pre-requisite objectives such as reduced part numbers, greater functionality, and lightweight, among others. Polymers, metals, and ceramics are the few materials that can be used in AM technology, but metallic materials (Magnesium and Aluminum) are attracting more attention from the research and industrial point of view. Understanding the role processing parameters of laser-based additive manufacturing is critical to maximize the usage of material in forming the product geometry. LPBF (Laser powder-based fusion) method is regarded as a potent and effective additive manufacturing technique for creating intricate 3D forms/parts with high levels of precision and reproducibility together with acceptable metallurgical characteristics. While dealing with LBPF, some degree of porosity is acceptable because it is unavoidable; hot ripping and cracking must be avoided, though. The necessary manufacturing of pre-alloyed powder and ductility remains to be the primary concern while dealing with a laser-based additive manufacturing approach. The presence of the Al-Si eutectic phase in AlSi10Mg and AlSi12 alloy attributing to excellent castability and low shrinkage, attaining the most attention in the laser-based approach. Related studies with these alloys along with precipitation hardening and heat treatment processing were discussed. The Pure Mg, Mg-Al alloy, Mg-RE alloy, and Mg-Zn alloy along with the mechanical characteristics, electrochemical durability, and biocompatibility of Mg-based material have been elaborated in the work-study. The review article also summarizes the processing parameters of the additive manufacturing powder-based approach relating to different Mg-based alloys. For future aspects, the optimization of processing parameters, composition of the alloy, and quality of powder material used will significantly improve the ductility of additively manufactured Mg alloy by the LPBF approach. Other than that, the recycling of Mg-alloy powder hasn't been investigated yet. Meanwhile, the post-processing approach, including a homogeneous coating on the porous scaffolds, will mark the suitability in terms of future advancements in Mg and Al-based alloys.

Machinability Analysis and Optimization in Wire EDM of Medical Grade NiTiNOL Memory Alloy.

NiTiNOL (Nickel-Titanium) shape memory alloys (SMAs) are ideal replacements for titanium alloys used in bio-medical applications because of their superior properties like shape memory and super elasticity. The machining of NiTiNOL alloy is challenging, as it is a difficult to cut material. Hence, in the current research the experimental studies on machinability aspects of medical grade NiTiNOL SMA during wire electric discharge machining (WEDM) using zinc coated brass wire as electrode material have been carried out. Pulse time (Ton), pause time (Toff), wire feed (WF), and servo voltage (SV) are chosen as varying input process variables and the effects of their combinational values on output responses such as surface roughness (SR), material removal rate (MRR), and tool wear rate (TWR) are studied through response surface methodology (RSM) based developed models. Modified differential evolution (MDE) optimization technique has been developed and the convergence curve of the same has been compared with the results of differential evolution (DE) technique. Scanning electron microscopy (SEM) and energy dispersive X-ray spectrography (EDS) analysis are carried out to study the surface morphology of the machined alloy. SV is found to be more influential process parameter for achieving better MRR with minimal SR and TWR, followed by Ton, Toff, and WF. The WF has good impact on reduced SR and TWR responses and found to be least significant in maximizing MRR.

Modeling and Optimization of Fractal Dimension in Wire Electrical Discharge Machining of EN 31 Steel Using the ANN-GA Approach.

To achieve enhanced surface characteristics in wire electrical discharge machining (WEDM), the present work reports the use of an artificial neural network (ANN) combined with a genetic algorithm (GA) for the correlation and optimization of WEDM process parameters. The parameters considered are the discharge current, voltage, pulse-on time, and pulse-off time, while the response is fractal dimension. The usefulness of fractal dimension to characterize a machined surface lies in the fact that it is independent of the resolution of the instrument or length scales. Experiments were carried out based on a rotatable central composite design. A feed-forward ANN architecture trained using the Levenberg-Marquardt (L-M) back-propagation algorithm has been used to model the complex relationship between WEDM process parameters and fractal dimension. After several trials, 4-3-3-1 neural network architecture has been found to predict the fractal dimension with reasonable accuracy, having an overall R-value of 0.97. Furthermore, the genetic algorithm (GA) has been used to predict the optimal combination of machining parameters to achieve a higher fractal dimension. The predicted optimal condition is seen to be in close agreement with experimental results. Scanning electron micrography of the machined surface reveals that the combined ANN-GA method can significantly improve the surface texture produced from WEDM by reducing the formation of re-solidified globules.