Browsing by Author "Tayisepi, N."

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    Effectiveness and impact determination for Enterprise Resources Planning Systems: A case for manufacturing entities in Zimbabwe
    (NUST, 2021) Nduna, M.; Goriwondo, W.M.; Tayisepi, N.; Goriwondo, W.M.
    The research looked at utilization and impact of Enterprise Resource Planning (ERP) systems for manufacturing organizations in Zimbabwe. Existing literature on adoption of Enterprise Resource Planning systems, its utilization and benefits realized was reviewed. A survey of eighteen Zimbabwean manufacturing organizations was conducted to establish the utilization of Enterprise Resource Planning systems. The survey centered on operational, strategic, managerial, and Organizational functions and their interaction with the Enterprise Resource Planning systems. The strategic, managerial, and Organizational functions, results reflected positive feedback on the system usage. Operational functions had mixed responses with an overall of 58% being positive and 42% negative. Various impact dimensions were determined and mechanisms of effectiveness in the application of ERP systems established. The research went on to develop a tool using C-Sharp that organizations can use to determine their level of Enterprise Resource Planning system utilization and gave recommendations on how organizations can improve on their Enterprise Resource Planning system utilization for competitive advantage.
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    OPTIMISATION OF MACHINING PARAMETERS FOR SURFACE ROUGHNESS, POWER CONSUMPTION AND OTHER RESPONSES DURING THE CNC LATHE DRY MACHINING OF EN24 ALLOY STEEL
    (The Iraqi Journal for Mechanical and Material Engineering, 2024-06) Tayisepi, N.; Simbanegavi, M.
    Design and analysis of optimisation protocols used for performance enhancement of machining based components manufacturing is currently an active area of machining science. This experimental investigation research deals with determining and optimising the effects of three input parameter metrics on the performance realisation of good surface quality output and energy consumption during the dry machining process of EN 24 steel material by turning on the CNC lathe. The input parameter metrics considered were cutting speed, depth of cut and feed rate. The study, employed Taguchi full factorial design approach in planning the experimental process and estimate the effects of the input metrics on the response; three phase digital energy meter to capture electrical power consumption data online; offline recorded surface roughness data and used Minitab 18 statistical software analysis of variance to assess the influence of cutting parameters on the response parameters, and the Signal to noise ratio main effects plot as the optimisation tool for the various response parameters. The paper aimed to determine the appropriate cutting parameter settings required on the lathe machine in order to produce EN 24 components of better surface quality at minimum energy expenditure. The experimental data analysis results established the optimum operating conditions at varied cutting parameter settings with respect to the different response parameters and the results were presented for the surface roughness, material removal rate and specific cutting energy use.
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    Taguchi Full Factorial Design of Experiments Optimisation of Cutting Parameters for Energy Efficiency and Surface Roughness during the Dry Turning of EN19 Material
    (Scientific Research Publishing, 2024-05-30) Tayisepi, N.; Mnkandla, A.N.; Tigere, G.; Gwatidzo, O.; Mutenhabundo, W.; Ndala, E.; Wagoneka, L.M.
    During metal machining, the satisfactoriness of cost-quality-time matrix convergence effectively depends on the supreme selection of cutting parameters. This study investigated the energy use minimisation and quality surface generation through optimised cutting parameters application, as sustainability enhancement during dry turning of EN19 material. Cutting parameter optimisation is a serious challenge confronting the machining industry as they strive to achieve low energy use and better component quality generation from their operations. The utility material, EN19, is a medium-carbon low alloy steel which typically gets applied in the manufacturing of multiple profiled cylindrical machine tool, rail locomotives and motor vehicle component parts, inter alia. Taguchi Full Factorial experimental plan was used to organise the empirical experiments. ANOVA and the main effects plot signal-to-noise ratio optimisation analysis were utilised in the study to establish the influence of process parameters on the response parameters—surface roughness and energy use. The aim was to investigate and determine the correlation of the machining strategy parameters with the outcome of low energy use and quality surface texture of the components as the cutting parameters were varied, and optimised for minimum surface roughness and energy use. Results of the extensive experimental study, produced optimum cutting speed, rake angle variation and feed rate which respectively influence the response parameters positively for energy use minimisation and improved surface quality. Validation experiments confirmed model findings.