Time-Dependent Ginzburg–Landau equation modelling of electron beam additive manufactured Titanium alloy

In this study, the micro-structure evolution in Electron beam additive manufacturing (EBAM) process of Ti–6Al–4V is studied using phase-field modelling. EBAM involves a rapid solidification process and the properties of a build partly depend on the solidification behaviour as well as the micro-structure of the build material. Phasefield modelling was applied to study the evolution of micro-structural scale of dendrites during the Ti-6Al-4V alloy solidification in the EBAM process. The mechanical properties of the final build parts are dependent on the solidification rate which affects the micro-structure of the material. Thus, the evolving of micro-structure plays a critical and effective role towards process parameters optimization. Recent increase in computational power allows for direct simulations of micro-structures during materials processing for specific manufacturing conditions. A MATLAB code was developed to solve the set of Time-Dependent Ginzburg–Landau equation phase field equations. The effect of under-cooling was investigated through the simulations; the greater the under-cooling, the faster the dendrite grows. The micro-structure simulations shows the growth of primary β phase, which has a body-centred cubic crystal structure phase with four fold symmetry comparable with experimental results for the tested range

Assessment of the role of Lean Construction Practices in Environmental Sustainability

The Construction Industry of which the building industry is a subsidiary has been identified as an industry that can help in the achievement of environmental sustainability. Literature has established practices such as Virtual Design and Construction (VDC), Prefabrication and Modularization and Just-in- Time (JIT) and others as environmental sustainable LCPs. Therefore, this paper investigated the adoption of these practices and areas LCPs adoption can help contribute to environmental sustainability in the Nigerian Building Industry. In order to achieve the aim of the study, a questionnaire survey was conducted in Abuja, Lagos, Port-Harcourt, Enugu and Kaduna covering five out of the six geo-political zones of Nigeria. The sample frame for the study consisted of architectural, building consulting and contracting and quantity survey firms in the selected cities. Totally, 446 valid responses were collected and analyzed descriptively using the Statistical Package for Social Sciences (SPSS). The results from the study revealed that firms in the Nigerian building industry have adopted all the 32 investigated LCPs. Specifically, it was found that VDC was the most adopted of all the 32 LCPs investigated in the Nigerian building industry. In addition, the result reveals that respondents are of the opinion that adoption of LCPs in building can help in the achievement of a sustainable environment through massive reduction of construction wastes that are injurious to the ecosystem

Datasets on the measurement of mechanical properties of ferrite and austenite constitutive phases using nanoindentation and micro hardness techniques

Abstract

Inventing a New Africa through Discovery and Innovations in Computational Material Science

Researchers are increasingly relying on computational technologies to help in simulation of properties of new materials and some areas in materials science has enjoyed some level of success which ranges from composites, to polymer science and to advanced ceramics. This review paper discuss certain developments in the area of computational Materials and how Africa can leverage on this technology to develop their emerging Industries, while dwelling more on application of computational material science in energy sector, since energy has been most pressing challenges in Africa which could be addressed by advanced materials. Also, we summarize part of our research work on galvanic corrosion of mild steel bolt in a magnesium alloy (AZ91D) plate simulation using comsol Multiphysics and 2k factorial experiments on factors that influence the recovery of gold during the upgrade of Ilesha-Itagunmodi, Nigeria gold ore through Froth flotation using Anova software. Attempt have been made to identify existing computational method, challenges of computational materials science deployment in Africa, and how material development can be accelerated through the power of computational material science. With this work, we were able to establish that the strength of computational materials science is in making a connection between the experiment and theories of complex phenomena

Inventing a New Africa through Discovery and Innovations in Computational Material Science

Researchers are increasingly relying on computational technologies to help in simulation of properties of new materials and some areas in materials science has enjoyed some level of success which ranges from composites, to polymer science and to advanced ceramics. This review paper discuss certain developments in the area of computational Materials and how Africa can leverage on this technology to develop their emerging Industries, while dwelling more on application of computational material science in energy sector, since energy has been most pressing challenges in Africa which could be addressed by advanced materials. Also, we summarize part of our research work on galvanic corrosion of mild steel bolt in a magnesium alloy (AZ91D) plate simulation using comsol Multiphysics and 2k factorial experiments on factors that influence the recovery of gold during the upgrade of Ilesha-Itagunmodi, Nigeria gold ore through Froth flotation using Anova software. Attempt have been made to identify existing computational method, challenges of computational materials science deployment in Africa, and how material development can be accelerated through the power of computational material science. With this work, we were able to establish that the strength of computational materials science is in making a connection between the experiment and theories of complex phenomena

Machine learning prediction of Nd : YAG Laser welded sintered Metalling alloy : mechanical properties

Abstract: Please refer to full text to view abstract.Ph.D. (Mechanical Engineering

Applying a Neural Network-Based Machine Learning to Laser-Welded Spark Plasma Sintered Steel: Predicting Vickers Micro-Hardness

This paper presents an artificial neural network (ANN) approach to the estimation of the Vickers hardness parameter at the weld zone of laser-welded sintered duplex stainless steel. The sintered welded stainless-steel hardness is primarily determined by the sintering conditions and laser welding processing parameters. In the current investigation, the process parameters for both the sintering and welding processes were trained by ANNs machine learning (ML) model using a TensorFlow framework for the microhardness predictions of laser-welded sintered duplex stainless steel (DSS 2507 grade). A neural network is trained using a thorough dataset. The mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R2 for the train and test data were calculated. The predicted values were in good agreement with the measured hardness values. Based on the results obtained, the ANN method can be effectively used to predict the mechanical properties of materials