Atomistic investigation on the effect of temperature on mechanical properties of diffusion-welded Aluminium-Nickel

Atomistic investigation of diffusion welding between Aluminium and Nickel has been investigated, by means of Molecular Dynamics (MD) simulation. This study focuses on examining the effect of temperature on diffusion welding between Al-Ni for which it is still lacking. Employing several different temperatures, this study aims to examine the influence of temperature on the mechanical properties of diffusion-welded Al-Ni. The results have shown that the structural evolution significantly affected by the temperature. Better bonding structure is achieved as the temperature is increased which indicated by the wider interfacial region thickness on concentration profiles. However, as the temperature is increased lower ultimate tensile strength is obtained. Therefore, precisely estimates the temperature for particular materials in diffusion welding is a critical point. In this study, the optimum condition that fitsA on the diffusion welding process is when the temperature set on 500 K

Atomistic Investigation on the Role of Temperature and Pressure in Diffusion Welding of Al-Ni

This paper presents an investigation of diffusion welding of aluminum and nickel at the atomic scale by utilizing molecular dynamics simulation. By employing several temperature and pressure values, the significant influence of the two could be obtained and thus the optimum parameter values could be obtained. The results showed that the bonding mechanism is mostly promoted by Al, in which the deformation and defects are involved. The results on both the mechanical properties and the evolution of the diffusion configuration showed that temperature has more impact compared to pressure. It was indicated that by raising the temperature to 700 K with the lowest pressure (50 MPa), both the mechanical properties and the evolution of the diffusion configuration showed a relatively significant difference. On the one hand, the deformation that occurs during welding, which is mostly caused by raising the temperature, obviously promotes joining and therefore more joining depth can be achieved, although it results in a curved diffusion zone at the interface. On the other hand, it also leads to a lower ultimate tensile strength. During the tensile test, raising the temperature also led to focusing the deformation in the diffusion zone, while a lower temperature resulted in a wider area of deformation

A Review on Molecular Dynamics Simulation of Joining Carbon-Nanotubes and Nanowires: Joining and Properties

Carbon-nanotubes (CNTs) and Nanowires (NWs), the two nanomaterials with outstanding properties, are the materials with which their behaviour and properties have long been drawing attention to researchers. However, the tiny nature of these two materials causes difficulties in describing and estimating their behaviour and properties, thus a numerical technique that considers the tiny nature of the materials like Molecular Dynamics (MD) simulation is a promising solution to this problem. Since the early utilization of MD simulation in the investigation of the behaviour of carbon-nanotubes and nanowires, it provides the researcher with an excellent description of how the two materials behave at atomic-scale and then estimate their properties. Recently, MD simulation of CNTs and NWs exhibit growth in the simulation size as with the growth of the computing capabilities. The size of the materials being simulated by MD simulation increased significantly in the recent year, thus giving possibility to achieve a better description of the behaviour and a more precise estimation of the properties. In this review, we provide an overview of the recent advances in the investigation of the joining processes and properties of carbon-nanotubes and nanowires at atomic-scale utilizing molecular dynamics simulation

A Review on Molecular Dynamics Simulation of Joining Carbon-Nanotubes and Nanowires: Joining and Properties

Carbon-nanotubes (CNTs) and Nanowires (NWs), the two nanomaterials with outstanding properties, are the materials with which their behaviour and properties have long been drawing attention to researchers. However, the tiny nature of these two materials causes difficulties in describing and estimating their behaviour and properties, thus a numerical technique that considers the tiny nature of the materials like Molecular Dynamics (MD) simulation is a promising solution to this problem. Since the early utilization of MD simulation in the investigation of the behaviour of carbon-nanotubes and nanowires, it provides the researcher with an excellent description of how the two materials behave at atomic-scale and then estimate their properties. Recently, MD simulation of CNTs and NWs exhibit growth in the simulation size as with the growth of the computing capabilities. The size of the materials being simulated by MD simulation increased significantly in the recent year, thus giving possibility to achieve a better description of the behaviour and a more precise estimation of the properties. In this review, we provide an overview of the recent advances in the investigation of the joining processes and properties of carbon-nanotubes and nanowires at atomic-scale utilizing molecular dynamics simulation

Microstructural and Mechanical Characterization of Ledeburitic AISI D2 Cold-Work Tool Steel in Semisolid Zones via Direct Partial Remelting Process

The success of the thixoforming process largely depends on the created microstructure, which must be globular in the liquid phase. The solid–liquid structural changes that occur on as-annealed D2 tool steel when it is subjected to the so-called DPRM are described in this work (direct partial remelting method). The paper discusses phase changes and how carbide dissolution affects grain boundary liquation and grain spheroidization. Equiaxed grains with undissolved carbide particles have been found in the microstructural analysis at 1250 °C; however, the carbides gradually disappear as the temperature rises. Additionally, the equiaxed grains were transformed to a globular structure, which improves the shape factor and grain size for the thixoforming process. For AISI D2 thixoforming, which produced grains with a diameter of 50 μm and a shape factor of 1.18, temperatures of 1300 °C and a holding period of 5 min were the optimum parameters. The outcomes also showed that the mechanical properties of the AISI D2 were greatly enhanced after using direct partial remelting, where hardness was increased from 220 Hv to 350 Hv and tensile strength from 791 MPa to 961 MPa

Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions

Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when end milling Ti6Al4V alloy with a Physical Vapor Deposition PVD coated tool under dry cutting conditions. Back propagation neural network (BPNN) has been hybridized with two heuristic optimization techniques, namely: gravitational search algorithm (GSA) and genetic algorithm (GA). Taguchi method was used with an L27 orthogonal array to generate 27 experiment runs. Design expert software was used to do analysis of variances (ANOVA). The experimental data were divided randomly into three subsets for training, validation, and testing the developed hybrid intelligent model. ANOVA results revealed that feed rate is highly affected by the surface roughness followed by the depth of cut. One-way ANOVA, including a Post-Hoc test, was used to evaluate the performance of three developed models. The hybrid model of Artificial Neural Network-Gravitational Search Algorithm (ANN-GSA) has outperformed Artificial Neural Network (ANN) and Artificial Neural Network-Genetic Algorithm (ANN-GA) models. ANN-GSA achieved minimum testing mean square error of 7.41 × 10−13 and a maximum R-value of 1. Further, its convergence speed was faster than ANN-GA. GSA proved its ability to improve the performance of BPNN, which suffers from local minima problems

Development of Hybrid Intelligent Models for Prediction Machining Performance Measure in End Milling of Ti6Al4V Alloy with PVD Coated Tool under Dry Cutting Conditions

Ti6Al4V alloy is widely used in aerospace and medical applications. It is classified as a difficult to machine material due to its low thermal conductivity and high chemical reactivity. In this study, hybrid intelligent models have been developed to predict surface roughness when end milling Ti6Al4V alloy with a Physical Vapor Deposition PVD coated tool under dry cutting conditions. Back propagation neural network (BPNN) has been hybridized with two heuristic optimization techniques, namely: gravitational search algorithm (GSA) and genetic algorithm (GA). Taguchi method was used with an L27 orthogonal array to generate 27 experiment runs. Design expert software was used to do analysis of variances (ANOVA). The experimental data were divided randomly into three subsets for training, validation, and testing the developed hybrid intelligent model. ANOVA results revealed that feed rate is highly affected by the surface roughness followed by the depth of cut. One-way ANOVA, including a Post-Hoc test, was used to evaluate the performance of three developed models. The hybrid model of Artificial Neural Network-Gravitational Search Algorithm (ANN-GSA) has outperformed Artificial Neural Network (ANN) and Artificial Neural Network-Genetic Algorithm (ANN-GA) models. ANN-GSA achieved minimum testing mean square error of 7.41 × 10−13 and a maximum R-value of 1. Further, its convergence speed was faster than ANN-GA. GSA proved its ability to improve the performance of BPNN, which suffers from local minima problems