Logistic regression model for the grooving process of hybrid polyester resin composite materials reinforced with natural fibers and fiberglass

2018 3rd International Conference on Energy Materials and Applications

能源学;材料科学

Pérez, C.F.^1 ; Paredes, J.G.^1 ; Valencia, R.X.^1 ; Vaca, W.H.^1 ; Vásquez, E.P.^1

Technical University of Ambato, Faculty of Civil and Mechanical Engineering, Material and Manufacturing Research Group, Av. Los Chasquis and Rio Payamino, Ambato, Ecuador^1

关键词: Binomial logistic regressions;    Cutting parameters;    Delamination factor;    Diverse fields;    High-speed steels;    Logistic Regression modeling;    Manufacturing process;    Multiple linear regression analysis;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/446/1/012003/pdf
DOI  :  10.1088/1757-899X/446/1/012003

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Greencomposites materials are being integrate into the manufacturing of products in diverse fields. For its assembly, are required secondary manufacturing processes such as the machining of slots and holes. In these processes, the greencomposites present great sensitivity to their manufacture. For the study of this concept, an experimental design was developed to analyze the grooving process and verify the effects of the cutting parameters with two types of tools (HSS and HSC) on surface quality (Ra) and the delamination factor (Fd) of the material. An analysis of the experiment was carried out to verify the significant effects of the different factors involved, in addition a multiple linear regression analysis and binomial logistic regression to predict surface quality and delamination in other conditions. The results obtained show the logistic regression model gave a better prediction for the grooving process of the greencomposites, both for the surface roughness and for the delamination factor. Finally, this study concludes that the carbide tool (HSC) has better surface quality with respect to the conventional tool of high speed steel (HSS).