Decision methodology of end-milling conditions using data-mining

The purpose of the present study is to apply data-mining methods to support the decision of reasonable cutting conditions. Although an enormous amount of information is listed in a catalog, it is not possible to know all of it. Seen from the viewpoint of the user, this enormous amount of information becomes a hindrance. For example, even if an expert worker does not look at a catalog, in end-mill processing, he can decide the appropriate processing condition efficiently from experience; however, this type of situation creates difficult problems for an unskilled worker or a skilled worker with little experience. The recommended cutting condition for every type of material is listed in a catalog together with the appropriate tool, but it takes much time and labor to search and examine the catalog to find the right tool, and this process is inefficient. The main subject of our research was to support the processing condition of the end-mill for each precision tool efficiently based on end-mill clusters. Our research applied the techniques of data mining, in particular, non-hierarchy clustering and hierarchy clustering, to catalog data. With these techniques, we applied multiple regression analysis and reached the following main conclusions. As a first step, we paid attention to the shape element of catalog data. In addition to using conventional mining processes, we grouped end-mills from the viewpoint of tool shape, which meant the ratio of dimensions, visually by applying the K-means method. We applied variable cluster analysis next to each cluster and extracted an predictor variable to represent each cluster, and we performed multiple regression analysis and derived a cutting condition decision formula. The cutting condition decision formula provided high accuracy. The accuracy was higher than the results achieved through mining of all data. A more highly precise processing condition decision formula was derived by doing mining again, excluding the peculiar data clusters such as small diameter end-mill. We understood what was effective for cutting condition decision to be factors related to blade length and the ratio of the full length, factors which have not been singled out through background knowledge or expert knowledge, but were noticed as an effect of catalog mining.     

端铣工艺非线性动力学特性的研究

混沌是非线性动力学系统的一种运动形式。作为机械振动理论的一个新的研究分支,它得到了大量的研究。机床—刀具—工件—夹具作为一个复杂的非线性动力学系统,存在大量的非线性因素影响着系统的特性。通过研究系统相空间的Poincaré截面和最大Lyapunov指数,对端铣铣削工艺中的混沌现象进行了研究。     

Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

NC Machining of Freeform Pockets with Arbitrary Wall Geometry Using a Grid-Based Navigation Approach

A tool path must be determined in an efficient manner to generate NC (numerical control) code for machining. This is particularly important when machining freeform pockets with arbitrary wall geometry on a three-axis CNC machine. In this paper, a grid-based 3D navigation algorithm for generating NC tool-path data for both linear interpolation and a combination of linear and circular interpolation is presented for three-axis CNC milling of general pockets with sculptured bottom surfaces. The pocket surface is discretised by defining a grid and the navigation algorithm plans the tool motion. The grid size and the cutter diameter are chosen so that a predefined tolerance for surface roughness is satisfied. The grid-based navigation algorithm is simulated graphically and verified experimentally.     

Optimising Tool Positioning for End-Mill Machining of Free-Form Surfaces on 5-Axis Machines for both Semi-Finishing and Finishing

End-milling of free-form surfaces on 5-axis NC machine tools is a complex problem which has been studied by a large number of research scientists. When end-milling non-convex surfaces there is a risk of interference between the tool and the surface. Using a 5-axis NC machine tool enables the tool to be oriented in any spatial direction to access a given point. This means interference can be avoided and the position of the tool can be optimised so as to minimise residual material. Here, a new method is presented for tool positioning in end-milling of free-form surfaces based on evaluating the interference for a set of test points distributed around the circumference of the tip of the tool. Distinction is made between the cases of semi-finishing and finishing which can be performed using a large diameter flat-end tool and a toroidal tool, respectively. Further, all developments are implemented for interactive use in a CAD/CAM software environment.     

Modeling micro-end-milling operations. Part III: influence of tool wear

The characteristics of the cutting forces were studied at different usage levels and the analytical model of the micro-end-milling operations was modified to represent the tool wear. A new expression was derived from the model to estimate the remaining tool life from experimental data. The parameters of the model are estimated by using genetic algorithms. The difference between the simulated and experimental cutting force profiles for new and worn tools was less than 8%. The remaining tool life was estimated with typically 10% error from the experimental data. Maximum error was 20%. The introduced analytical model and genetic algorithm-based parameter estimation approach is very convenient for on-line tool wear monitoring without extensive experimental study.     

Modeling micro-end-milling operations. Part II: tool run-out

The effect of run-out is clearly noticed in micro-end-milling operations, while the same run-out creates negligible change at the cutting force profile of conventional end-milling operations. In this paper, the cutting force characteristics of micro-end-milling operations with tool run-out are investigated. An analytical cutting force model is developed for micro-end-milling operations with tool run-out. The proposed model has a compact set of expressions to be able to estimate the cutting force characteristics very quickly compared to the numerical approaches. The cutting forces of micro-end-milling operations simulated by the proposed model had good agreement with the experimental data.     

Using two-dimensional vibration cutting for micro-milling

The purpose of this paper is to investigate the effects of assisted vibration cutting (VC) on the micro-milling quality of aluminum alloy Al 6061-T6. The desired vibration is proposed from the workpiece side by a two-dimensional vibrating worktable we developed. The slot produced by end milling is studied by examining its geometrical shape and machining accuracy. Through extensive experiments with end mills of diameter 1 mm, we found that slot oversize, displacement of slot center and slot surface roughness could be improved by imposing VC. The employment of VC increases the number of slots produced within the tolerance when high amplitude and proper frequency are imposed. With the help of Taguchi method and analysis of variance (ANOVA), we analyzed the effect of VC in end milling by investigating the slot-width accuracy. It is found that the use of second directional VC to minimize slot-width oversize in end milling is helpful.     

Parameter Selection Based on Surface Finish in High-Speed End-Milling Using Differential Evolution

Selection of cutting parameters in high-speed machining is one of the most important tasks to achieve the required level of quality. Evolutionary algorithms are often used to obtain the optimal parameters corresponding to a single value of surface finish. In most practical applications, it is necessary to determine the cutting speed, feed, and depth of cut to meet the required surface finish. In the present work, high-speed end-milling has been studied, and an objective function for surface finish is obtained by Response Surface Methodology using results of carefully designed experiments. Testing of differential evolution and genetic algorithms using the classical Himmelblau function reveals that the performance of differential evolution is better. Therefore, differential evolution is used in the present work with a newly proposed objective function, and the machining parameters for the required surface finish are obtained.     

Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process by Artificial Neural Networks: A Case Study

The machining factors affecting the tool wear and the surface roughness produced in the end-milling process are generally the cutting speed, the feed rate, the depth of cut, etc. This article focused on finding an optimal cutting parameter setting of high-purity graphite under dry machining conditions by an artificial neural network and the Sequential Quadratic Programming method [1 Fletcher , R. Practical methods of optimizations . Vol. 1, Unconstrained Optimization, and Vol. 2 Constrained Optimization ; John Wiley and Sons , 1981 . [Google Scholar]]. This algorithm yielded better performance than the traditional methods such as the Taguchi method and the Design of Experiments (DOE) approach. Additionally, the tool worn surfaces after machining were examined with tool electron microscopy (TEM) to observe the tool wear mechanisms.