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The purpose of this study is to study the influence of the laser power and the scanning speed on the surface hardness, and top surface and side surface roughness of Ti6Al4V metal specimens fabricated via the selective laser melting (SLM) technique. The laser power was varied between 150 and 300 W while the scan speed was varied between 800 and 1400 mm/s. Response surface methodology (RSM) in the Design Expert 11 software environment was used for the design of experiment and results analysis. The distance for surface indentations were targeted at 10–20 μm for the top surface and 60–80 μm for the side surface while the surface hardness profiling was studied using an indenter with the indentation performed at a load of 500 gf and at a dwelling time of 15 s. The study revealed that as the laser power was increased, the surface hardness increases, while the top surface and side surface roughness reduces. Then, when the scanning speed increased, the surface hardness, and top surface and side surface roughness were found to also increase. The optimum range of the process parameters selected are laser speed 300 W and scan speed 1400 mm/s. This produces a minimum surface roughness of 13.006 μm for the top surface roughness and 62.166 μm for the side surface roughness with a corresponding hardness value of 409.391 HV. The findings of this study will assist manufacturers in the process design of the SLM of titanium alloy for aerospace applications.

Additive manufacturing technology involving the deposition of materials in successive layers provides a realistic possibility for easy and quick production of 3D objects on demand. This work involves the computer-aided simulation and performance evaluation of an additive manufacturing technology. The mechanical design, modelling and simulation of the internal accessory of a railcar were carried out with the aid of the ABAQUS® 2016 while the physical experimentations were carried out via the fused deposition modelling (FDM) method using three different 3D printing materials, namely acrylonitrile butadiene styrene (ABS) plastic, epoxy resin and polytetrafluoroethylene. An additive manufacturing system which essentially comprises the mechanical part having the stepper motor, micro-switch and extruder, and the electronics board having MELZI V2.0, a DIY shield board, USB to serial converter and Arduino micro controller was used for the product manufacturing. The analysis of the results gotten indicates that the three materials possess excellent mechanical properties which make them suitable for the service requirements. This work will assist manufacturers in their quest for innovativeness in product development as well as reduction in the manufacturing lead time and cost.

The learning factories are platform created to provide an effective learning environment that will bring about human capacity development in a bid to bridge the gap between learning and practice. In this study, the training modules involving the Artificial Intelligence (AI) system, which comprises of the Artificial Neural Network (ANN) with dynamic time series model was developed. This is to train maintenance personnel on how to constantly monitor and analyze data from the Internet of Things (IoT) and other sources in order to predict the state and potential failure of a railcar wheel bearing. The modules for training include the data acquisition, pre-processing, network training, features extraction and predictive model modules which are set up to acquaint the personnel in the maintenance section of the rail industry on the use of AI for condition based monitoring and prediction of wheel bearing failure. The demonstration of the training modules was carried out using the past data of the wheel-bearing temperature from a secondary source which was pre-processed and iteratively trained using the Levenberg Marquardt algorithm in a MATLAB 2018a environment in order to predict future temperature variations, the remaining useful life of the bearing and to obtain a predictive model. The result obtained indicates the feasibility of the AI in the diagnosis of the railcar wheel bearing condition, prediction of the Remaining Useful Life (RUL) of the bearing as well as the determination of the optimum time for maintenance.

The drive for a more flexible Through-life Engineering servicing technology that can enhance both productivity and profitability for the operation of maintenance, repair, overhaul (MRO) in the aerospace implores harnessing the innovation of hybrid-additive manufacturing (HAM). Hybrid manufacturing system seeks to reduce cost by utilizing the tool-less production principle of additive manufacturing combine with the conventional subtractive manufacturing for product finishing or post-processing. HAM has many merits that make it an attractive option in manufacturing of high performance products as peculiar to MRO businesses. Therefore, this paper aims to establish a cost model derived from a “Time Driven Activity Based Costing” approach for the technological integration of Hybrid system to increase economic competitiveness of highly MRO services in the aerospace industry. Hence, a unique cost formulation method was investigated through analytical technique in cost engineering models for the purpose of manufacturing processes.

The Additive Manufacturing (AM) technology of producing materials layers upon layers to make objects from a 3D model data is a manufacturing process which eliminates the use of tools and fixtures, highly flexible to design modifications can reduce material wastage and inventory etc. In this study, the process optimization of a 3D printer for manufacturing the accessories of railcar was carried out. The Response Surface Methodology (RSM) was used for the Design of Experiment (DoE) which consists of independent process parameters in the following ranges: scan speed (50-20 mm/sec), nozzle diameter (0.1-1.0 mm) thickness of layer (0.10-0.50 mm) and bed temperature (60-200℃). Taking the surface roughness as the response of the designed experiments, the four factors were varied over 2 levels and the statistical analysis of the results obtained was used for the predictive model which correlates the surface roughness of the plastic knob produced as a function of the independent process parameters. The results obtained indicated that the quality of the materials produced, which is a function of the finish requirement, depends on the print quality and the independent process parameters and vice versa. In addition, the rate at which the material run off the nozzle, which is a function of the manufacturing cycle time, is inversely proportional to the diameter of the nozzle.

Additive manufacturing (AM) has risen to be a substantial part of modern manufacturing due to its unique capabilities and has already been fondly adopted in various fields especially in the aerospace. In order to fully harness the benefits of this revolutionary manufacturing technology, this article aims to develop a practical integrated design methodology that can be used to enhance quality and throughput of AM processes. In doing so, investigation were conducted to examine an AM aero-based component through design tools that allow designers to consider an integrated process chain, from component design to pre-processing, manufacturing (laser bed fusion building), post-processing and finished part. The developed design model integrated with decision tools will assist the design experts in developing new knowledge that looks beyond the familiar Design for Additive Manufacturing (DfAM) rather to Design towards product Certification (DoC). In addition, this will serve as an effective design guidance that can inform proper design planning and optimization in aerospace industry production.

Additive manufacturing (AM) of laser-based technological solution is developing at an exponential rate and is stepping towards industrialisation. The value and possibilities it bring to modern design and manufacturing tasks specifically for metallic products cannot be undermined. Amongst its significant benefits are; manufacturability, materials, part quality, production and economics. In spite of the benefits, uncertainties within the processes and high investment costs deter firms from implementing and adopting this technology. As such, getting to know about the technical and economic analysis model is necessary for sustainability and competitiveness of manufacturers. Consequently, while many papers in the literature provide cost estimation models for additively manufactured parts, there does not exist a thorough indicator towards decision making. Hence, a concurrent decision tool using techno-economic analysis that helps justify the implementation of laser additive manufacturing systems, and realisation performance efficiencies is explored in this study.

Ever changing products, technology and competition make manufacturing a challenging task with respect to responding to market opportunities. A key technology exploited for revolutionary change in the phase of manufacturing competitiveness is the additive manufacturing techniques. Additive manufacturing (AM) is a category of technologies, which is fast shifting from mere resource base to a knowledge base, transitioning from prototyping to manufacturing of end usable parts with defined mechanical properties. In South African industrialization context, technology development for value proposition is an encouraged phenomenon. This is being realized through the South African government’s investment in research and equipment funding to science council and academia focusing on the full AM value chain for the aerospace and medical industries. However, despite the skills in the research and development space of AM, laser technology remain an unconventional process that lacks knowledge in terms of how the production techniques can be commercialized. The aim of this study is to evaluate technological capabilities that informs industrial manufacturing setup, and create a business prospects for the laser-based additive manufacturing segment of South Africa.