TUT DIGITAL OPEN REPOSITORY

Recent Submissions

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    Design of a Control System for a Vending Machine.
    (Elsevier, 2020-01-01) Murena, Eriyeti; Sibanda, Vennan; Sibanda, Solomon; Mpofu, Khumbulani
    Vending machines are available in many public places for vending of items like snacks, beverages, newspapers, tickets and smoking cigarettes Recently developed vending machine requires a control system to offer a variety of products to the general public. In this light, this paper, therefore, is aimed at developing a control system for the developed vending machine by developing various inputs required to make the machine function efficiently. The system controls and monitors the vending machine functions, namely: alarm system, product dispensing, refrigeration and payment system. The microcomputer capitalises on the evolution of high-performance processors and stable operating systems to implement control requirements. The project shall use intelligent vending machine input/output board to link other machine peripherals. The control system shall enable the machine to handle coin, mobile and point of sale terminal payment options. Implementation of the control system enhances flexibility in payment, remote machine monitoring and inventory control, and improved user experience through the integration of digital touch screen user interfaces and high-speed transaction processing.
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    Conceptual design of intelligent reconfigurable welding fixture for rail car manufacturing industry.
    (Elsevier, 2020-01-01) Seloane, W.T.; Mpofu, K.; Ramatsetse, B.I.; Modungwa, D.
    Dedicated welding fixtures are used in the railcar manufacturing industry to fix and stabilise the welded components during the welding assembly process in order to achieve the highest possible accuracy and repeatability of connected components. Utilisation of dedicated welding fixtures in a railcar manufacturing industry leads to high production setup, high manufacturing costs, low quality standards and storage difficulties. Furthermore, because of lack of ability to disassemble, traditional welding fixtures cost a fortune in storage costs. In order to reduce cost, increase productivity and meet the market demands, there is a growing requirement for the tools to be flexible, reconfigurable and reconstructible. This paper presents the conceptual designs of the intelligent reconfigurable welding fixtures aimed to address the current problems encountered by the railcar manufacturing industry when utilizing dedicated fixtures. Three design concepts of the Intelligent Reconfigurable Welding Fixture (IRWF) were generated using Computer Aided Design (CAD) called Unigraphics. Weight design matrix concept evaluation criteria was used to select the most suitable concept based on functional requirements such as reconfigurability, intelligence, assembly, manufacturability and maintenance ability. Concept 3 was selected because it satisfied most of the functional requirements of the IRWF
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    Design of a multi-robot system for wind turbine maintenance.
    (MDPI, 2020-05-18) Franko, Josef; Du, Shengdzi; Kallweit, Stephan; Duelberg, Enno; Engemann, Heiko
    The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART—Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.
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    Exploring energy efficiency prediction method for Industry 4.0: a reconfigurable vibrating screen case study.
    (Elsevier, 2020-06-15) Adenuga, Olukorede Tijani; Mpofu, Khumbulani; Ramatsetse, Boitumelo Innocent
    The shift towards energy efficiency (EE) programme in accelerating a resource efficient society requires the development of a portfolio for assessing and demonstrating a solution to support decision-making, planning and policy. EE market entails building capacity for industry initiatives in energy savings towards a culture of greater resource efficiency. Many energy predictions have been research by analysing of a large amount of historical and sensing data with high accuracy of the prediction results. However, most of these researches does not apply in practice to energy efficiency and IoT energy management systems, which involves real-time data performance. The proposed methodology is conceptualise on cyber physical systems and application of directed communication approach through a direct and indirect information exchange between agents. This paper explores prediction of energy demands based on measurement data and a statistical regression model using a variable frequency drive (VFD) to control reconfigurable vibrating screen (RVS) machine fixed-speed electric agitating motor. The model acceptance criteria provide a prediction method of energy costs against usage in mining of materials through load testing with measured data every 2 minutes for 24 hours. The logged data prediction accuracy reached 98.47% according to material screening rate, showing close alignment to the measured model and 96.97% coefficient of determination, showing the percentage of variation of independent variables (energy cost with VFD) that affect the dependent variable EED. The study will assist in reducing the energy consumption in conformance to South Africa Department of Energy (DoE) vision to support sustainability in manufacturing.
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    Prescriptive modelling system design for an armature multi-coil rewinding cobot machine.
    (Elsevier, 2020-01-01) Matenga, Alice; Murena, Eriyeti; Mpofu, Khumbulani
    Digital transformation has ushered in the digital economy, powered by digital intelligence and quantum computing. The various winding topologies in rotary machines result from multi-variant design specifications and connection types. Rewinding of rotary machines is a behaviour-based decision making process conducted within the shop floor, as the procedure is dependent on multi-input multi-output variables. Due to high data variability in service remanufacturing of armature windings in rotary machines, data abstraction for intelligent automation and analytics leads to increased operational productivity and new insights into market dynamics. In this light, the aim of the paper is to illustrate the design of a prescriptive modelling system of a symmetrical multi-coil winding machine for armature winding. The proposed system is a hybrid least squares support vector machine and adaptive neuro fuzzy inference system for optimizing and maintaining a copper fill factor at 90.7%. A mixed method research was utilized for qualitative and quantitative for the multivariate parameters. The results show that the system through in-slot repetitive orthocyclic winding process, with multi-spindle concentric layering improves the energy efficiency of the induction motors, which in turn lowers winding faults during the remanufacturing process. Streamlining operations through fog computing further enhances system latency and process reliability towards sustainable industrialization.