Papers by Author: Hiroyuki Kodama

Abstract: In this study, we perform the end-mill process of a difficult-to-cut material (JIS SUS310 stainless steel) and observe it with high performance infrared thermography. Considering the rotating angle of end-mill tool, a pixel temperature in each frame is investigated to obtain the tool temperature variation after cutting of each tooth in end-mill process. The tool temperature distribution can be analyzed at each rotating tool position in end-mill process from imageries, considering the relationship between the time duration of each frame and the rotating speed of an end-mill tool. Moreover, the tool/holder shape and the number of cutting teeth can be seen to affect the cutting temperature because the tool heat capacity and the heat input are different. The examination and analytical results show this method to be effective to estimate the tool temperature in the end-mill process sufficiently.

Abstract: Chatter vibration in cutting processes usually leads to surface finish degradation, tool damage, cutting noise, energy loss, etc. Self-excited vibration particularly seems to be a problem that is easily increased to large vibration. The regenerative effect is considered as one of the causes of chatter vibration. Although the chatter vibration occurs in various types of processing, the end-milling is a typical process that seems to cause the chatter vibration due to a lack of rigidity of one or more parts of the machine tools, cutting tool, and work-piece. The aim of our research is to propose a simple method to control chatter vibration of the end-milling process on the basis of a coupling model integrating the related various elements. In this study, hammering tests were carried out to measure the transfer function of a machine tool and cutting tool system, which seems to cause vibration. By comparing these results, finite elemental method (FEM) analysis models were constructed. Additionally, cutting experiments were carried out to confirm the chatter vibration frequencies in end-milling with a machining center. In the hammering tests, impulse hammer and multiple acceleration pick-ups are connected to a multi-channel FFT analyzer and estimate the natural frequencies and natural vibration modes. A simplified FEM model is proposed by circular section stepped beam elements on the basis of the hammering test results, considering a coupling effect. In comparisons of the calculated results and hammering test results, the vibration modes are in good agreement. As a result, the proposed model accurately predicts the chatter vibration considering several effects among the relating elements in end-milling. Moreover, it can be seen that the chatter vibration is investigated from a viewpoint of the integrating model of the end-milling process.

Abstract: Data-mining methods using hierarchical and non-hierarchical clustering are proposed that will help engineers determine appropriate drilling conditions. We have constructed a system that uses clustering techniques and tool catalog data to support the determination of drilling conditions for printed wiring boards (PWBs). Variable cluster analysis and the K-means method were used together to identify tool shape parameters that have a linear relationship with the drilling conditions listed in the catalogs. The response surface method and significant tool shape parameters obtained by clustering were used to derive drilling condition decision equations, which were used to determine the indicative drilling conditions for PWBs. Comparison of the conditions recommended by toolmakers demonstrated that our proposed system can be used to determine the drilling condition for PWBs. We carried out the drilling experiments in accordance with the catalog conditions and mining conditions, and estimated the board temperature around a drilled hole, the drilling forces, and the roughness of the drilled hole wall.

Abstract: Data-mining methods using hierarchical and non-hierarchical clustering are proposed that will help engineers determine appropriate end-milling conditions. We have constructed a system that uses clustering techniques and tool catalog data to support the determination of end-milling conditions for different types of difficult-to-cut materials such as austenitic stainless steel, Ni-base superalloy, and titanium alloy. Variable cluster analysis and the K-means method were used together to identify tool shape parameters that have a linear relationship with the end-milling conditions listed in the catalogs. The response surface method and significant tool shape parameters obtained by clustering were used to derive end-milling condition decision equations, which were used to determine the indicative end-milling conditions for each material. Comparison with the conditions recommended by toolmakers demonstrated that our proposed system can be used to determine the cutting speeds for various difficult-to-cut materials.

Abstract: Machining is often performed by a machining center using various cutting tools and conditions for different shapes and materials. Recent improvements in CAM system make it easier for even unskilled engineers to generate NC programs. In the NC program, the end-milling conditions are decided by engineers. However, engineers need to decide the order of the process, cutting tool selection, and the end-milling conditions on the basis of their expertise and background knowledge because the CAM system cannot automatically decide. Data-mining methods were used to support decisions about end-milling conditions. Our aim was to extract new knowledge by applying data-mining techniques to a tool catalog. We used both hierarchical and non-hierarchical clustering of catalog data and also used applied multiple regression analysis. We focused on the shape element of catalog data and we visually grouped ball end-mills from the viewpoint of tool shape, which here meant the ratio of dimensions, by using the k-means method. We also found an expression for calculating end-milling conditions, and we compared the calculated with the catalog values.

Abstract: We proposed the data-mining methods using hierarchical and non-hierarchical clustering methods to help engineers decide appropriate end-milling conditions. The aim of our research is to construct a system that uses clustering techniques and tool catalog data to support the decision of end-milling conditions for difficult-to-cut materials. We used variable cluster analysis and the K-means method to find tool shape parameters that had a linear relationship with the end-milling conditions listed in the catalog. We used the response surface method and significant tool shape parameters obtained by clustering to derive end-milling condition. Milling experiments using a square end mill under two sets of end-milling conditions (conditions derived from the end-milling condition decision support system and conditions suggested by expert engineers) for difficult-to-cut materials (austenite stainless steel) showed that catalog mining can be used to derive guidelines for deciding end-milling conditions.

Abstract: The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).