Taguchi Full Factorial Design of Experiments Optimisation of Cutting Parameters for Energy Efficiency and Surface Roughness during the Dry Turning of EN19 Material  

作　　者：Nicholas Tayisepi Albert Nkulumo Mnkandla Godfrey Tigere Oscar Gwatidzo Winnie Mutenhabundo Emmanuel Ndala Lovelace Makakatanwa Wagoneka Nicholas Tayisepi;Albert Nkulumo Mnkandla;Godfrey Tigere;Oscar Gwatidzo;Winnie Mutenhabundo;Emmanuel Ndala;Lovelace Makakatanwa Wagoneka(Department of Industrial and Manufacturing Engineering, National University of Science and Technology, Bulawayo, Zimbabwe;Department of Industrial and Manufacturing Engineering, Harare Institute of Technology, Harare, Zimbabwe)

机构地区：[1]Department of Industrial and Manufacturing Engineering, National University of Science and Technology, Bulawayo, Zimbabwe [2]Department of Industrial and Manufacturing Engineering, Harare Institute of Technology, Harare, Zimbabwe

出　　处：《World Journal of Engineering and Technology》2024年第2期438-454,共17页世界工程和技术（英文）

摘　　要：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.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.

关 键 词：MACHINING Energy Use ANOVA Sustainable Manufacturing Machining Strategy 

分 类 号：TG1[金属学及工艺—金属学]