Determination of the important machining parameters on the chip shape classification by adaptive neuro-fuzzy technique

被引：6
作者：
Jovitc, Srdan [1 ]
Arsit, Neboiga [1 ]
Vukojevitc, Vukoje [1 ]
Anicic, Obrad [1 ]
Vujicic, Sladana [2 ]
机构：
[1] Univ Pristina, Fac Tech Sci Kosovska Mitrov, Kneza Milosa 7, Kosovska Mitrovica 38220, Serbia
[2] Univ Kragujevac, Fac Engn, Sestre Janic 6, Kragujevac 34000, Serbia
来源：
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY | 2017年 / 48卷
关键词：
Chip shape; Prediction; Machining parameters; Surface roughness; ANFIS; WEAR; FORM; PREDICTION; NETWORKS; SURFACE; TAPS;
D O I：
10.1016/j.precisioneng.2016.11.001
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The main goal of the study was to analyze the influence of machining parameters on the chip shape classification. Straight turning of mild steel (A500/A500M-13) and AISI 304 stainless steel were performed to monitor the chip shapes. Cutting speed, feed rate, depth of cur and surface roughness of the material were used as inputs. Adaptive neuro-fuzzy inference system (ANFIS) was used in to determine the inputs influence on the chip shape classification. The selection process was performed to estimate the most dominant factors which affect the chip shape classification. According to the results surface roughness has the highest influence on the chip shape classification. The obtained model could be used as optimal parameter settings for the best chip shape classification. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码：18 / 23
页数：6
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