Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5019131 | Precision Engineering | 2017 | 19 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Engineering
Industrial and Manufacturing Engineering
Authors
SrÄan JoviÄ, NebojÅ¡a ArsiÄ, Vukoje VukojeviÄ, Obrad Anicic, SlaÄana VujiÄiÄ,