Optimization of Machining Parameters for Product Quality and Productivity in CNC Machining of Aluminium Alloy

This study focused on optimizing the process of CNC machining to enhance productivity and product quality of surface finish Ra via the process parameters of the cutting speed (vc), feed rate (vf), and cutting depth (doc). Experimentation was performed on workpieces of AA-6061 to investigate the resp...

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Bibliographic Details
Published in:Journal of Mechanical Engineering
Main Author: Armansyah; Nasution S.R.; Dewanto N.D.; Sudianto A.; Saedon J.; Adenan S.
Format: Article
Language:English
Published: UiTM Press 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202666859&doi=10.24191%2fjmeche.v21i3.27351&partnerID=40&md5=5f7b9a96eb47e932dbff4163e6a8572d
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Summary:This study focused on optimizing the process of CNC machining to enhance productivity and product quality of surface finish Ra via the process parameters of the cutting speed (vc), feed rate (vf), and cutting depth (doc). Experimentation was performed on workpieces of AA-6061 to investigate the response Ra through variation of the process parameters to analyze their best fit using RSM with 23 full factorial designs L-8 of DOE. The analysis of variance (ANOVA) was then used to find the major contributors among them that were responsible for the Ra. Based on the result, better Ra was obtained at 0.103 μm using the best fit of vf (150 mm/min), vc (220 m/min), and doc (0.1 mm). ANOVA shows vf contributed better Ra followed by vc and doc respectively. In addition, the level of Ra’s was analyzed through contour plots represented by different colours. It continued to analyze the effect of the process parameters via the main effects plot, Pareto chart, and the contour plot in the predictive desirability model, which indicated that the plots and chart confirmed the vf had more influence compared to others. The study confirmed that the low-level parameters provided better Ra to be used for polishing. © 2024 College of Engineering, Universiti Teknologi MARA (UiTM), Malaysia. https://doi.org/10.24191/jmeche.v21i3.27351
ISSN:18235514
DOI:10.24191/jmeche.v21i3.27351