Using catalog data mining in support of determining micro end-milling conditions
A growing number of requirements are being placed on micro fabrication using end mills with high operating degrees-of-freedom. In particular, when the minor diameter of the end mill is 1.0 mm or less, the handling of tools becomes difficult because of the influence of the characteristic size effect and bending of the cutting portion. Furthermore, it is hard for engineers to derive the cutting conditions that can serve as indexes in the early stage of end-milling with small minor diameters. To solve this problem, in this research, on a basis of workpiece material characteristics and tool shape parameters, a system that can make instantaneous decisions on the tool shape and cutting conditions to be used was built, and its usefulness was evaluated. This system was developed by applying data mining techniques together with non-hierarchical and hierarchical clustering methods on tool catalog data. The results of the analysis found that the characteristics of the workpiece material were significant in making decisions regarding the cutting conditions for the minor diameter of the end mill. Moreover, the cutting speed was found to have a significant effect on the tool shape parameters, whereas differences as to the processing method (side milling or slotting) did not have a significant effect.
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.
Micromilling of hardened tool steels
Miniaturized parts are increasingly demanded in different fields like medical, transportation, environmental, and communication industries. In order to manufacture these parts in an economical way, mass replication methods, such as micro injection molding, have to be applied. Currently, Electro Discharge Machining (EDM) process is mainly used to manufacture the needed moulds for micro injection molding in industries. In order to achieve the final shape, several electrodes have to be made by milling with different levels of geometrical accuracy. Besides, the material removal rate of EDM is relatively low. This results in a long throughput-time and high manufacturing cost. Therefore, industries are looking for alternative technologies to overcome these drawbacks; micromilling is one of the promising technologies. The advantages of the micromilling technology include the applicability of a broad range of materials including hardened tool steels, the capability of manufacturing three dimensional geometries, accurate machining of complex features, and it is economical for small and medium lot sizes, etc. However, although micromilling in principle is a good alternative for the EDM process, it is found in research that some challenges have to be overcome before this technology is ready to be adopted in industrial applications. The literature survey shows that the fundamental micro cutting mechanism has been well investigated and understood through the study of micro orthogonal cutting and ultraprecision machining. Issues related to the application of micromilling have however not yet been well studied. Besides, inconsistent observations are commonly seen in literature. This is because observed results in micromilling are highly dependent on the experimental conditions. Based on the literature survey and initial micromilling tests, the general goal of this research has been defined as to develop and describe a reliable micromilling process for precision machining of hardened tool steels. It was decided to first improve lives of micro endmills to achieve a reliable cutting, and then to improve the performance of the process through process planning. In this research, experiments were mainly done with O 0.5 mm square endmills on hardened tool steels (AISI H11, H13, etc.). Experimental investigations were done to identify the main problems in micromilling. It was observed that the used commercial micro tools suffered severe wear, the tool life was too short to conduct a successful task, and the workpiece quality was not achieving the requirements. Investigations were conducted to understand tool wear types and mechanism. The factors which influence the tool performance were analyzed. It was found that the geometry of commercial tools is mainly derived from macro endmills, with which the cutting edge corners have the highest stress level. The machining parameters and tool paths are two factors that have significant effect on the tool performance; however, there was no good method available for the planning of the micromilling process. The geometry of micro endmills was studied theoretically by means of analytical modeling and FEA method. Having understood the relationship between geometrical features of the cutting tool and their influence on the tool performance (stiffness and strength of the cutting edge corners), the geometry of the micro endmill can be designed specifically for a given application to achieve the desired performance. This method was demonstrated by designing the micro square endmill especially for hard milling applications. The newly designed tools were manufactured and validated through experiments in comparison with the commercial tools. The experimental results have shown that the new designs have improved the tool performance as expected. The planning of the micromilling process has been divided into two parts. In the first part, design of experiments has been used to understand the relationship between input variables (machining parameters and tool paths) and process response (tool wear and surface finish). With this method, the significant variables can be identified by means of ANOVA analysis, and the cutting conditions can be planned accordingly to optimize the process output. For example, to have a long tool life is important for the roughing operation, and to achieve a good surface finish is of interest for the finishing operation. In the second part of the process planning, a knowledge-based method is used to plan cutting conditions for the machining of micro features with high aspect ratios. The selection of machining parameters was done by means of a force model, which describes the relation between machining parameters and average forces. The tool paths were tested by a FEM model. An improved tool path was proposed to overcome the disadvantage of the conventional tool path. Experiments were done with conditions chosen by the theoretical analysis, and the results proved the validity of the developed method. Micro ribs with aspect ratios higher than 50 could be machined successfully.