Performance of Predicting Surface Quality Model Using Softcomputing, a Comparative Study of Results

被引：4

作者：

Flores, Victor ^{[1
]}

Correa, Maritza ^{[2
]}

Quinonez, Yadira ^{[3
]}

机构：

[1] Univ Catolica Norte, Dept Ingn Sistema & Comp, Avda Angamos 0610, Antofagsta, Chile

[2] Univ Autonoma Occidente, Fac Ingn, Dept Operac & Sistemas, Cali, Colombia

[3] Univ Autonoma Sinaloa, Fac Informat Mazatlan, Av Univ & Leonismo Int S-N, Culiacan, Mexico

来源：

NATURAL AND ARTIFICIAL COMPUTATION FOR BIOMEDICINE AND NEUROSCIENCE, PT I | 2017年 / 10337卷

关键词：

High-speed machining; High-speed milling; Softcomputing; Bayesian networks; Predictive models; ARTIFICIAL NEURAL-NETWORKS; MACHINING PROCESS; ROUGHNESS;

D O I：

10.1007/978-3-319-59740-9_23

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data obtained from two machine-tools with different characteristics, but using the same experimental model. In both cases, work pieces (probes) of the same material were machined (steel and aluminum probes) with cutting parameters and characteristics proper of production processes in industries such as aeronautics and automotive. The main objective of this study was to compare surface quality prediction models created in two machining centers to establish differences in outcomes and the possible causes of these differences. In addition, this paper deals with the validation of each model concerning surface quality obtained, along with comparing the quality of the models with other predictive surface quality models based on similar techniques.

引用

页码：233 / 242

页数：10

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