Design and optimization of machining parameters for effective AISI P20 removal rate during milling operation.
Daniyan, I. A. Tlhabadira, I. Daramola, O. O. Mpofu, K.
Daniyan, I. A.
Tlhabadira, I.
Daramola, O. O.
Mpofu, K.
Citations
Altmetric:
Abstract
Tool geometry and selection of appropriate machining parameters are important considerations that determine the quality of surface finish, rate of tool wear, production cycle time, rate and ease of machinability. In this study, the milling process was designed to optimize the effects of machining parameters namely; the width of cut, cutting force, depth of cut and feed rate for effective AISI P20 removal during milling operation. The numerical design was carried out using the Complete Abaqus Environment (CAE) and the Response Surface Methodology (RSM) while the physical experiment was investigated using the DMU 80 CNC milling machine as well as the dynamometer and dynaware data acquisition system. The design of the numerical experiment consists of four factor-two level factorial of 16 experimental runs. Based on the feasible combination of the machining parameters from numerical experiment, the milling operation of AISI P20 was carried out on the DMU 80 milling machine limited to a maximum load of 900 kg. The resulting values of the cutting force, moment of force and machining time were obtained via the data acquisition system. The analysis of the results led to the formulation of a predictive model that correlates the rate of material removal (RMR) as a function of the independent machining process parameters. The results obtained also indicate that the cutting force, width and depth of cut as well as the feed rate are important parameters that influence the rate of material removal during machining operations, hence, the need for proper process design and control of the milling operation process parameters in order to reduce the total manufacturing time and increase the metal removal rate which is a function of productivity. This will in turn reduce the total manufacturing cost without sacrificing product quality with attendant increase in productivity.
Description
29th CIRP Design 2019 (CIRP Design 2019).
Date
2019-01-01
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Research Projects
Organizational Units
Journal Issue
Keywords
Machinability, Model, Process design, Productivity, RMR