Modeling and Optimization of Tool Wear and Surface Roughness in Turning of Al/SiCp Using Response Surface Methodology

• Published in: 3D research Vol. 9; no. 4; pp. 1 - 13
• Main Authors: Laghari, Rashid Ali, Li, Jianguang, Xie, Zhengyou, Wang, Shu-qi
• Format: Journal Article
• Language: English
• Published: Seoul 3D Display Research Center 01.12.2018; Springer Nature B.V
• Subjects: 3DR Express; Abrasion; Aerospace industry; Aluminum; Automobile industry; Automotive engineering; Carbide tools; Computer Imaging; Confidence intervals; Cutting parameters; Cutting tools; Engineering; Fuel consumption; Lasers; Mathematical models; Metal matrix composites; Modeling; Optical Devices; Optics; Parameter identification; Particulate composites; Pattern Recognition and Graphics; Photonics; Production planning; Response surface methodology; Signal,Image and Speech Processing; Silicon carbide; Surface properties; Surface roughness; Tool life; Tool wear; Turning (machining); Vision; Wear mechanisms; Weight; Surface roughness; Al/SiCp metal matrix composite; Response surface methodology; ANOVA; Modeling and optimization; Tool life
• ISSN: 2092-6731; 2092-6731
• DOI: 10.1007/s13319-018-0199-2

Nowadays metal matrix composites are widely utilized in major industries such as aerospace and automotive because of their excellent properties in association with non-reinforced. This research work is attempted to analyze the consequence of cutting parameters on tool life and surface quality. The experimental work is consist of turning Al/SiCp (45%SiCp) weight with uncoated Carbide tools and the effect of three machining parameters including depth of cut, feed, and speed. Tool life and surface roughness have considered as process response for investigation. The predictive model has been developing to optimize the machining parameters in accordance to Box–Behnken design in Minitab 17, the contour plots the surface plot and response optimizer have made to study the influence of machining parameters and their interactions. ANOVA was carried out to identify the key factor affecting the tool life and surface roughness. The maximum tool life is 10.511 (min) and least surface roughness was observed 0.044 μm. The abrasion and adhesive have the principle wear mechanism observed in machining process. Response surface methodology (RSM) approach have used to optimize the machining parameters, and the RSM model found more than 95% confidence level.