Materials Science Forum Vols. 471-472 (2004) pp 32-36 online at http://www.scientific.net © (2004) Trans Tech Publications, Switzerland Materials Science Forum Vols. *** (2004) pp.32-36 Online at available since 2004/Dec/15 online http://scientific.net
2004 Trans Tech Publications, Switzerland
Development of a Cutting Database System with Prediction and Analysis Functions Y. Wan1,a, Z.Q. Liu1,b, X. Ai1,c and J.G. Liu1,d 1
School of Mechanical Engineering, Shandong University, Jinan 250061, PR China
a
[email protected],
[email protected],
[email protected],
[email protected]
Keywords: Cutting database, Tool wear, Surface integrity
Abstract. Cutting tool and machining parameters selection are central activity in process planning, which was traditionally performed by numerical control programmers or machine tool operators. The surface integrity has great effect on part quality and the sudden tool failure increases the machining costs greatly. The present paper details the development of a cutting database system with surface integrity prediction and tool failure analysis functions (CUT-P&A). The design and implement of this system has been presented. The system includes three main modules: cutting database, premature tool failure analysis and surface integrity prediction. The functions of this system include cutting tool selection and machining parameters recommendation, prediction of surface integrity and premature tool wear analysis. A case has been studied to explain the application of the system. The wide application of this system will be helpful for machining tool programmers, the improvement of machined part quality and the reduction of machine cost. Introduction Over the past few decades, the range of engineering materials increased greatly and meanwhile various cutting tools that are capable of machining these materials appeared. Traditionally, the experienced operator is a determinant factor in ensuring that the correct tool and parameters are used. However, this pattern is not acceptable any longer for the following reasons. (1) The CNC machine tools and cutting tools are expensive, so the incorrect use of them will lead to bad results. Lacking skilled operators is a serious problem in many workshops. Considerable skill and experience require years of practice. (2) High-speed machining goes mainstream [1]. It needs new guideline that is different from conventional machining. (3) For a given machining condition, it is difficult for shop floor worker to estimate the surface integrity before Fig.1 Framework of the CUT-P&A System machining. (4) The inappropriate use of cutting tool may result in sudden tool failure and costs a lot.
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The complex task requires the development of sophisticated programs that supported by database, which contains information on the manufacturing resources [2]. After their investigation, CIRP (International Institution for Production Research) announced that applying cutting database could decrease about 10% of the producing cost. A number of cutting database systems have been developed to select cutting tool for a specific operation and give the optimum machining condition, including ATS (Automatic tool selection) and knowledge-based system [3-5]. However, most published work seldom concerned with the surface integrity and tool wear except providing cutting tool selection and cutting parameters. In this paper, a cutting database system with analysis and prediction function (CUT-P&A) is proposed. CUT-P&A System Framework of CUT-P&A System. User-friendly interfaces were developed (user interaction and system outputs) as well as main modules such as cutting database, surface integrity prediction module and premature tool failure analysis module, as shown in Fig.1. Cutting Database Module. Cutting database module consists of several databases concerning workpiece material, tool material, cutting tool and cutting parameters. The source of cutting data includes literature data (Cutting handbooks, cutting tool catalogues and journals with printed or electronic form), data from shop floor and experimental data. Experimental trials provided information that was not already available and verified some uncertain data. Cutting data have been evaluated by experts before they are input into database. Fig.2 is the flow chart for the CUT-P&A system. It illustrates how this system runs. In the following modules, the structure and application of the system will be explained in detail. The first group (workpiece materials database) describes the workpiece information including the general workpiece. Workpiece have Fig.2 Flow chart for the CUT-P&A System been classified and sub classified according to their components and property. Because the same workpiece is named differently in
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different countries, this system converts them into identified code automatically. For instance, AISI 1045 steel in USA is the same material named 45# steel in China, while in this system they have the same identification code: 010-012. Except the code, hardness of the workpiece should be known, which can be input or chosen from the hardness table. Like the coding of workpiece material, different hardness units (such as Rockwell_Rc unit, Rockwell_RB unit, Vickers_HV unit, etc) will be converted into Brinel Unit automatically. The hardness of the workpiece takes great effect on tool life, so when machining parameters are calculated, the hardness of workpiece should be considered. The second group is tool material group. Tool materials include diamond (ND, PCD), Polycrystalline Cubic Boron, Ceramics, Cermets, Cemented carbide and high-speed steel. The tool material should be matched with workpiece material from physical, mechanical and chemical properties. The selection of the cutting tool is mainly based on these rules. For example, the situation that workpiece and cutting tool have the same element should be avoided. The third group consists of various set of cutting tools for different machining processes such as turning tools, face milling cutters, end mill cutters, drills, etc. The specified cutting tool type is introduced in it. The last group consists of machining parameters: cutting speeds, feed rates and depth of cut for various workpiece materials and cutting tools, as well as coolant, cutting requirement. Instead of just retrieving cutting data from the database, this system can calculate and modify the machining parameters according to the user’s input on the basis of metal cutting principle. The hardness of the workpiece affects the tool life directly, so when the tool life has been set, the cutting speed will be modified according to the hardness coefficient. On the other hand, the spindle rotational speed, the cutting force and the power are calculated. Prediction Module. Surface integrity, which includes surface roughness, work hardening and residual stress, has a great effect on the operation performance of machined parts. Analyzing the influence factors and their contributions is useful to obtain the required surface integrity [6]. The surface roughness is generally dependent on the cutting tool geometry, the tool material, the workpiece geometry, the cutting conditions, the cutter wear, the rigidity of the system constituted by machine-tool, tool holder, cutter and fixture of workpiece, etc. In this module, the surface roughness is calculated in theory. Due to tensile stress will decrease the fatigue strength of parts while compressive stress will improve it. The measures to reduce residual tensile stress and increase compressive stress can be retrieved in this module. Also, work hardening will reduce the surface quality and affect the fatigue strength. The measures to avoid it can be achieved. However, increasing cutting speed will reduce the surface roughness at the same time, it will decrease work hardening and residual tensile stress. So high-speed machining has been widely recognized as one of the key processes in manufacturing industries in recent years. Analysis Module Tool failure，which includes tool wear and tool fracture, takes an important role in accuracy and surface integrity, even results in cutting chatter and destroy the machine tool. The wear reasons are complex for the mechanical, physical and chemical factors [7,8]. The wear and fracture patterns consist of crater wear, flank wear, cracking and chipping, breakage，etc. The premature tool failure is so complicated for the operator that it is difficult to find the true reasons. In this module, with the guide of bitmap pictures, the user can identify the wear patterns easily and correct of the misuse of cutting tool and cutting parameters. Case Study A case is studied here to show the utilization of the cutting database module. Cylindrical steel (AISI 1045, the code is 01-012) stick is supposed to be turned. The input has several consequent steps, which can be seen in Fig.2.
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In the user interface, there are many buttons, text, and bitmaps in user interface, which makes it
Fig.3 The interface of turning operation convenient to operate. Fig.3 shows the parameters input and cutting tool selection of high-speed turning. As a result, machining parameters and values have been retrieved from databases or calculated by program as well as the surface integrity. After cutting, it is assumed that a severe crater wear of cutter is found, like the picture in Fig.4. The results and causes are analyzed and the solutions
Fig.4 Tool failure patterns analysis
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are provided. This system works well in our experiments and waits for the further application in practical machining. Conclusions A cutting database with analysis and prediction functions has been presented. The developed CUT--P&A system comprised of three modules: cutting database, premature tool failure analysis and surface integrity prediction. This system is highly suitable for the selection of cutting tool and machining conditions in conventional and high-speed machining. In addition to the above function, this system can analyze the premature tool failure and predict the surface integrity. This system has been tested with a case and the results are consistent with our experiments. The practical use of it will bring out great of outcome in machined part quality and cost reducing. Acknowledgements This project is supported by National Natural Science Foundation of China (through grant no. 50375089). References [1] S. Ashley: Mechanical Engineering (1995), p. P56 [2] M.V. Ribero and N.L. Coppini: J. of Materials Processing Technology Vols. 92-93 (1999), p. 371 [3] K.O. Edalew, H.S. Abdalla and R.J. Nash: Material & Design Vol. 22 (2001), p. 337 [4] P.G. Maropoulos and S. Hinduja: Proc. Inst. Mech. Eng. (Part B) Vol. 204 (1990), p. 43 [5] M. Bala and T.C. Chang: Int J Prod Res Vol. 29(1991), p. 2163 [6] P.V.S. Suresh, P.Venkateswara Rao and S.G. Deshmukh: International Journal of Machine Tool & Manufacture Vol. 42(2002), p. 675 [7] M. Alauddin, M.A.E. Baradie and M.S.J. Hashmi: Journal of Materials Processing Technology Vol. 55 (1995), p. 123 [8] M. Nouari, G. List and F. Girot, et al: Wear Vol. 255 (2003), p. 1359.
