The Effect of Surface Roughness of End-Mills on Optimal Cutting Performance for High-Speed Machining

Abstract

124 INTRODUCTIONDevelopments in machine tools tend towards high speed technology, including high-speed machining (HSM) and high-speed cutting (HSC), especially in high speed end milling applications [1] and [2]. High speed technology applications in machine tools are characterised by a high feeding speed, low axial and radial cutting depth, increased metal removal rate, simplified processing, and reduced costs. The thermal effect of workpieces is insignificant since cutting chips remove most of the heat induced by processing, and hence cutting oil is seldom used. This trend contributes to environmental protection efforts. Only a small amount of cutting fluid is available to lubricate green cutting. The primary deformation zone is significantly heated and bears the cutting force. Therefore, major green cutting methods include tool materials, coating technology, tool geometry design, chip control, coefficient of tool-face friction with the workpiece, and selection of cutting. Green cuttingrelated developments and applications depend on technological advances in machinery and cutting tools.The grinding precision of cutting tools is determined by the surface roughness of the rake face and relief face, in which precision essentially affects the surface roughness of a workpiece and the tool life during high-speed milling. Generally, a cutting tool manufacturer evaluates the quality of grinding, first, with respect to the surface finish of end-mill, and then with by the geometrical profile. The surface finish, which influences the abrasion of the endmill, lubrication, accuracy, and tool life expectancy, depends on the surface roughness of the rake face and relief face.As the most important and the final procedure in manufacturing, grinding of cutting edges is also critical in determining geometrical shapes, cutting performance, wear on the cutting edge, and tool life [3]. Shaji and Radhakrishnan [4] analysed the grinding parameters with respect to surface characteristics, e.g., wheel, workpiece, processing, and mechanical parameters. Yin et al. [5] examined ultraprecision grinding of cemented carbides from a microstructure perspective. Kwak [6] diagnosed errors in surface grinding processing by Taguchi and response surface methods. Nguyen et al. [7] simulated impact parameters of the precision grinding process.Owing to its efficiency and systematic approach, the Taguchi method has been extensively adopted in parameter design and experimental planning [8]. Despite its application in optimizing process parameters [9] to [13], the Taguchi method is unsatisfactory for handling multiple performance characteristics. In this study we attempt to derive an efficient solution to overcome the above problem.Grey relational analysis based on the Taguchi method can be adopted to elucidate the complex relationship among the designated performance

Machining Performance Analysis in End Milling: Predicting Using ANN and a Comparative Optimisation Study of ANN/BB-BC and ANN/PSO

“…The literature approach to reducing manufacturing time has been through the minimisation of product reworks while cost and product quality improvements have been tackled with the use of surface quality parameters. Literature sources, including Para et al [14], Chen et al [18], Kadirgama et al [20], Parmar and Makwara [25] and Kalidass et al [26] largely promote these ideas of time and cost minimisation as well as surface finishing optimisation with the control of specified process parameters.…”

“…Milling has received considerable attention [11,18,20,22] in literature due to the unique problems it solves in view of the limitations of other machining approaches such as cutting, grinding and drilling. For example, steel and super-alloys are difficult-to-cut materials [10,12,20] that cannot be processed through the cutting process.…”

“…Instances are the use of ANN, Taguchi method, principal component analysis, desirability function, response surface method, genetic algorithm, differential evolution, greyrelational analysis and PSO in end milling operations [11][12][13][14][15][16][17][18]. In addition, researchers have noted the significant advantages in combining models.…”

Cutting Tool Condition Monitoring and Validation Using ANN- A Comprehensive Research

“…[15] [20] Various Tool Monitoring Techniques by Researchers: D.E. Dimla Sr.(2004) has done an experimental investigation aimed at identifying and isolating effects of cutting conditions on cutting forces and vibrations from those arising as a result of cutting tool wear.…”

Analysis model of parameters affecting cutting performance in high-speed machining

“…As for tool grinding precision, an endmill relief face roughness of 0.23±0.03 μm could increase workpiece surface roughness [25], as shown in Fig The milling performance indicators were peripheral flank wear rate, end flank wear rate, and metal removal rate, which represented a problem of multiple quality characteristics. Consequently, grey relational grades denoted the multiple quality characteristics.…”