Unraveling the relationship between microstructure and mechanical properties of friction stir-welded copper joints by fuzzy logic neural networks

In this study, fuzzy logic neural networks were employed to optimize the friction stir welding (FSW) process parameters in the joining of copper plates. The FSW parameters were considered as the input variables, for which micro-hardness, nano-hardness, and yield strength of the joints were the responses. The micro-hardness and nano-hardness were measured by Vickers hardness and nanoindentation tests, respectively. The microstructure and substructure of the joints were evaluated by optical, scanning electron, and orientation imaging microscopes. The optimum process parameters through which the maximum strength was achieved were the tool rotational rate of 560 rpm, tool traverse speed of 175 mm/min, and tool axial force of 2.27 kN. The low heat input joints, owing to the finer grain sizes, high density of dislocations, and larger Taylor factors, indicated greater strength relative to the high input joints. Microstructure characterization revealed that dominant strengthening mechanisms of the joints were dislocation density, texture effect, and grain boundary hardening

The performance prediction of electrical discharge machining of AISI D6 tool steel using ANN and ANFIS techniques: a comparative study

AISI-D6 steel is widely used in the creation of dies and molds. In the present paper, first the electrical discharge machining (EDM) of the aforementioned material is performed with a testing plan of 32 trials. Then, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were applied to predict the outputs. The effects of some significant operational parameters—specifically pulse on-time (Ton), pulse current (I), and voltage (V)—on the performance measures of EDM processes such as the material removal rate (MRR), tool wear ratio (TWR), and average surface roughness (Ra) are extracted. To lead the process operators, process plans (i.e., parameter–effect correlations) are created. The outcomes exposed the upper values of pulse on-time caused by higher amounts of MRR and Ra, and likewise lower volumes of TWR. Furthermore, growing the pulse current resulted in upper volumes of the material removal rate, tool wear ratio, and surface roughness. Besides, the higher input voltage resulted in a lower amount of MRR, TWR, and Ra. The estimation models developed by using experimental data recounting MRR, TWR, and Ra. The root means the square error was used to determine the error of training models. Furthermore, the estimated outcomes based on the models have been proven with an unseen validation set of experiments. They are found to be in decent agreement with the experimental issues. The investigation shows the powerful learning capability of an ANFIS model and its advantage in terms of modeling complex linear machining processes

Phase formation in as-solidified and heat-treated Al–Si–Cu–Mg–Ni alloys: Thermodynamic assessment and experimental investigation for alloy design

Thermodynamic simulations based on the CALPHAD method have been carried out to assess the phase formation in Al–7Si–(0–1)Ni–0.5Cu–0.35Mg alloys (in wt.%) under equilibrium and non-equilibrium (Scheil cooling) conditions. Calculations showed that the T-Al9FeNi, γ-Al7Cu4Ni, δ-Al3CuNi and ε-Al3Ni phases are formed at different Ni levels. By analyzing the calculated isothermal sections of the phase diagrams it was revealed that the Ni:Cu and Ni:Fe ratios control precipitation in this alloy system. In order to verify the simulation results, microstructural investigations in as-cast, solution treated and aged conditions were carried out using electron probe microanalysis (EPMA), scanning electron microscopy (SEM), X-ray diffraction (XRD) and transmission electron microscopy (TEM). Furthermore, cooling curve analysis (CCA) was also performed to determine the freezing range of the new alloys and porosity formation during solidification. Hardness measurements of the overaged samples showed that in this alloy system the δ-Al3CuNi phase has a greater influence on the overall strength of the alloys compared to the other Ni-bearing precipitates

Additive manufacturing of AlSi10Mg alloy using direct energy deposition: microstructure and hardness characterization

This paper aims to study the manufacturing of the AlSi10Mg alloy with direct energy deposition (DED) process. ollowing fabrication, the macro- and microstructural evolution of the as-processed specimens was initially investigated using optical microscopy and scanning electron microscopy. Columnar dendritic structure was the dominant solidification feature of the deposit; nevertheless, detailed microstructural analysis revealed cellular morphology near the substrate and equiaxed dendrites at the top end of the deposit. Moreover, the microstructural morphology in the melt pool boundary of the deposit differed from the one in the core of the layers. The remaining porosity of the deposit was evaluated by Archimedes’ principle and by image analysis of the polished surface. Crystallographic texture in the deposit was also assessed using electron backscatter diffraction and x-ray diffraction analysis. The dendrites were unidirectionally oriented at an angle of ~80° to the substrate. EPMA line scans were performed to evaluate the compositional variation and elemental segregation in different locations. Eventually, microhardness (HV) tests were conducted in order to study the hardness gradient in the as-DED-processed specimen along the deposition direction. The presented results, which exhibited a deposit with an almost defect free structure, indicate that the DED process can suitable for the deposition of Al-Si-based alloys with a highly consolidated structure

Physical simulation of hot deformation and microstructural evolution for 42CrMo4 steel prior to direct quenching

Direct quenching and tempering (DQ-T) of hot rolled steel section has been widely used in steel mill for the sake of improvement of mechanical properties and energy saving. Temperature history and microstructural evolution during hot rolling plays a major role in the properties of direct quenched and tempered products. The mathematical and physical modeling of hot forming processes is becoming a very important tool for design and development of required products as well as predicting the microstructure and the properties of the components. These models were mostly used to predict austenite grain size (AGS), dynamic, meta-dynamic and static recrystallization in the rods immediately after hot rolling and prior to DQ process. The hot compression tests were carried out on 42CrMo4 steel in the temperature range of 900–1 100 °C and the strain rate range of 0. 05–1 s−1 in order to study the high temperature softening behavior of the steel. For the exact prediction of flow stress, the effective stress-effective strain curves were obtained from experiments under various conditions. On the basis of experimental results, the dynamic recrystallization fraction (DRX), AGS, hot deformation and activation energy behavior were investigated. It was found that the calculated results were in good agreement with the experimental flow stress and microstructure of the steel for different conditions of hot deformation