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Virtual CNC machine tool modeling and machining simulation in high speed milling
Chapter 6
Virtual CNC machine tool modeling and machining simulation in high speed milling Anthony Chukwujekwu Okafor Computer Numerical Control and Virtual Manufacturing Laboratory, Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO, United States
Chapter outline 1 Introduction 163 2 Virtual modeling of machine tools and their simulation 164 2.1 Real Bridgeport CNC mill 164 2.2 Physical vertical machining center—Cincinnati Milacron Sabre 750 168 2.3 CNC programs simulation 174 3 Modeling and prediction of cutting forces 174 3.1 Introduction 176
3.2
4
5
Mechanics of wavy-edge bull-nose helical end mill 178 3.3 Cutting force simulation and prediction 180 Cutting force simulation results 180 4.1 Validation of mechanistic cutting force model 181 4.2 Results and discussions 181 Conclusions and future work 184 5.1 Conclusion 184 5.2 Future work 184
1 Introduction Virtual machining (VM) provides an environment to model and simulate machining processes in the computer [1]. Machining simulation is used to optimize processes to improve quality and flexibility or to evaluate machining parameters and make manufacturing decisions. VM generally include well-designed graphical user interface for selecting and inputting required machining parameters, mechanistic cutting force models, and a post-process tool like graphical multimedia results indicators and report generators. To a layperson, VM means making and analyzing parts in the computer, not on actual machine tool [2]. Iwata et al. [3] described architecture for modeling and simulation of virtual manufacturing systems. High Speed Machining. http://dx.doi.org/10.1016/B978-0-12-815020-7.00006-0 Copyright © 2020 Elsevier Inc. All rights reserved.
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Virtual CNC assumes that imitation-real machining can be conceptualized prior to commencement of real machining on physical CNC machine tool. A computer is used to create 3D model of the machine tool, to simulate and visualize their machining operation [4]. Effects of machining parameters can be simulated using virtual machine tools (VCNC-MTs) and actual CNC programs. The benefits of virtual CNC simulation include detecting before machining, any strange behavior that could occur during machining, such as tool collision, axis drive overtravel, etc. Yao et al. [5] described theoretical basis for developing virtual turning and milling. Fang et al. reported using immersion VR to develop virtual manual and CNC lathe, but it does not incorporate collision detection [6]. Suh et al. described how to simulate CNC milling machine on the web using virtual reality modeling language, COSMO player, and JAVA applet [7]. This chapter will give the reader an overview of research results on virtual modeling of CNC milling machine (Bridgeport mill), vertical machining center (Cincinnati Milacron Sabre 750), hereafter referred to VMC, and CNC turret lathe (Okuma LB 15), and their use for simulation of CNC programs in milling and turning Inconel and titanium alloys. The research on the development of mechanistic cutting force model to be integrated with virtual machine tool model for simulation and training is also presented. The chapter sections are as follows. Section 2 describes virtual modeling of machine tools and simulation; Section 2.1 describes real Bridgeport CNC mill; Section 2.1.1 describes virtual modeling of Bridgeport CNC mill; Section 2.1.2 describes virtual Bridgeport components assembly; Section 2.1.3 describes assigning kinematics to the Bridgeport CNC mill axes; and Section 2.1.4 describes virtual controller development. Section 2.2 describes real vertical machining center (Cincinnati Milacron Sabre 750); Section 2.2.1 describes virtual modeling of vertical machining center; and Section 2.3 describes the simulation of CNC programs. Section 3 describes mechanistic modeling and prediction of cutting forces; Section 4 presents results of cutting force simulation; and Section 5.0 summarizes the chapter along with mentioning future trends. After reading this chapter, the reader should be able to understand the concept of virtual manufacturing and VM, cutting force prediction using mechanistic cutting force model.
2 Virtual modeling of machine tools and their simulation Fig. 6.1 shows the schematic diagram of virtual modeling of machine tools (Bridgeport mill, Okuma LB15 turret lathe, and Cincinnati Milacron Sabre 750 VMC), which includes modeling major component parts of machine tool, controller, their assembly, assigning kinematics to the axes, and machining simulation.
2.1 Real Bridgeport CNC mill The real Bridgeport CNC mill is a 3-axis (actually a 2-1/2 axis) milling machine. A picture of the real Bridgeport CNC mill is shown in Fig. 6.2 [6].
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FIGURE 6.1 Flowchart for virtual modeling of machine tools.
2.1.1 Creating virtual model of Bridgeport CNC mill Deneb Robotics Virtual NC software, now DELMIA from Dassault 3D-Systems was used to model components of the real Bridgeport CNC Mill. DELMIA is menu driven with 3D CAD system. CAD primitives, such as block, cylinder, etc., were used to create the machine tool components. The following virtual
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FIGURE 6.2 Real Bridgeport CNC Mill.
components created are: Fig. 6.3, virtual Bridgeport mill base; Fig. 6.4, virtual bridge Bridgeport knee; Fig. 6.5, virtual Bridgeport Y-axis (saddle); Fig. 6.6, virtual Bridgeport X-axis (table); Fig. 6.7, virtual Bridgeport mill head; Fig. 6.8, assembled virtual tool and tool holder; and Fig. 6.9, virtual workpiece. The details of the modeling procedure are given in Okafor et al. (2010) [1], and Vishnu (2000) [8].
2.1.2 Assembly of virtual components of Bridgeport mill Assembling various Bridgeport mill components to build a device, in this case, virtual Bridgeport CNC mill is accomplished using BUILD and SETUP menu of the virtual NC software. The detailed steps are given in Okafor et al. [1,2]. 2.1.3 Assigning kinematics to virtual Bridgeport mill axes The Bridgeport mill axes movement during VM simulation is referred to as kinematic. The following are defined: axes translational or rotational motion, degrees of freedom, speeds and accelerations, travel limits, etc., according to machine specification. The assembled virtual Bridgeport mill with clamped workpiece in the machine vice is shown in Fig. 6.10. The table axis changes color to cherry whenever
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FIGURE 6.3 Virtual Bridgeport mill base.
FIGURE 6.4 Virtual Bridgeport knee.
it overshoots its travel limit as shown in the figure alerting that the X-axis over travel has occurred.
2.1.4 Virtual controller for Bridgeport CNC mill Virtual controller is a post processor that can reproduce all functionality of real Bridgeport CNC mill controller. The virtual controller reads CNC program blocks and produces a list of commands that are used to perform the simulation.
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FIGURE 6.5 Virtual Bridgeport Y-axis (saddle).
FIGURE 6.6 Virtual Bridgeport X-axis (table).
The virtual controller is divided into three units: the declaration, code definition, and function definition. Details are given in Okafor et al. (2010) [1] and Vishnu (2000) [8].
2.2 Physical vertical machining center—Cincinnati Milacron Sabre 750 The Physical Cincinnati Milacron Sabre 750 VMC is a 3-axis VMC that has three sliding axes and an optional fourth rotary axis under computer numerical control, equipped with automatic tool changer drum with 21 tool holding
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FIGURE 6.7 Virtual mill Bridgeport mill head.
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FIGURE 6.8 Assembled tool and tool holder.
FIGURE 6.9 Virtual workpiece.
capacity. It has many milling and drilling canned cycles. The machine has an Acramatic 2100E open architecture controller; and it has the capability to be programmed for probing inspection of machined parts dimensions. It utilizes an executive memory that runs on Window NT operating system. The machine was bought without the fourth axis so the fourth axis is not modeled. The machine is used conducting research on machining and machine tool metrology,
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FIGURE 6.10 Assembled virtual Bridgeport mill alerting X-axis over travel.
and for laboratory instruction on CNC programming and machining for the courses; ME5653, CNC of manufacturing processes, and ME6653, advanced CNC and engineering metrology; taught to undergraduate and graduate students in Mechanical and Aerospace Engineering Department at Missouri S&T. The VMC consists of the following assemblies: “main base unit,” which is bolted to the floor; “column unit” with slide ways, which is clamped to the base unit; “spindle carrier unit”, which carries traveling spindle that provides Z-axis movement; “saddle unit”, which provides Y-axis travel; “table”, which provides X-axis travel and also the work surface; and the tool “storage unit”. Fig. 6.11 shows a digital picture of the physical Cincinnati Milacron Sabre 750 VMC. For this research, outer cover of the VMC is not modeled so as to give a clear view and understanding of every machine component motion during simulation. Virtual modeling, assembly, and simulation were done as per flowchart shown in Fig. 6.1.
2.2.1 Virtual modeling of Cincinnati Sabre 750 VMC Virtual components of Cincinnati Milacron VMC were created using Deneb Robotics virtual NC software, now DELMIA, following similar procedure used for Bridgeport. The following virtual components were created: Fig. 6.12, main base unit and column unit; Fig. 6.13, spindle carrier unit and spindle; Fig. 6.14,
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FIGURE 6.11 Physical Cincinnati Milacron Sabre 750 VMC.
FIGURE 6.12 Mill base and column unit of Cincinnati Milacron Sabre 750 VMC. (A) Virtual main base unit and (B) column unit.
FIGURE 6.13 Spindle carrier and spindle of Cincinnati Milacron Sabre 750 VMC. (A) Spindle carrier unit (Z-axis) and (B) spindle.
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FIGURE 6.14 Saddle and table unit of Cincinnati Milacron Sabre 750 VMC. (A) Saddle unit (Y-axis) and (B) table unit (X-axis).
FIGURE 6.15 Tool drum and tool holder of Cincinnati Milacron Sabre 750 VMC. (A) Tool drum and (B) tool holder.
FIGURE 6.16 Tool assembly and tools in tool drum. (A) Tool assembly and (B) tools in tool drum.
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FIGURE 6.17 Assembly process of Cincinnati Milacron Sabre 750 VMC. (A) Assembled base and column units and (B) assembled base and saddle units.
FIGURE 6.18 Final assembled Cincinnati Milacron Sabre 750 VMC.
saddle unit (Y-axis) and table unit (X-axis); Fig. 6.15 tool drum and tool holder; Fig. 6.16, tool assembly and tools in tool drum; and Fig. 6.17, assembly process.
2.2.2 Assembly of Cincinnati Milacron Sabre 750 VMC Assembling the various Cincinnati Milacron VMC components to build a device is accomplished using BUILD and SETUP menu bars, by attaching the virtual components to each other and defining their relationships. The assembled base and column units, and assembled base and saddle units are shown in Fig. 6.14. Fig. 6.18 shows the final assembled virtual Cincinnati Milacron Sabre 750 VMC with clamped workpiece in the machine vice, and all tools in their respective tool drum and the three axes assigned their kinematics.
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FIGURE 6.19 Virtual Cincinnati Milacron Sabre 750 VMC indicating axis over travel.
2.2.3 Assignment of axes kinematics to the Cincinnati Milacron VMC The three X-, Y-, and Z-axis of Cincinnati Milacron VMC were assigned a translational motion, using the procedure similar to that for Bridgeport. Fig. 6.19 shows Virtual Cincinnati Milacron Sabre 750 VMC, indicating X-axis over travel by the table color turning to pink. 2.3 CNC programs simulation CNC programs written for the courses; ME5653, CNC of manufacturing processes and ME6653, advanced CNC and engineering metrology; for senior undergraduate and graduate students in Mechanical and Aerospace Engineering Department at Missouri S&T were successfully simulated using the developed virtual Bridgeport CNC Mill, Virtual Cincinnati Milacron Sabre 750 VMC, and Virtual Okuma LB 15 CNC Turret Lathe. Fig. 6.20 shows virtual workpiece clamped the virtual Bridgeport mill virtual vice. Fig. 6.21A shows virtual Bridgeport mill with cutting tool in motion, and Fig. 6.21B shows machined part after the simulation. Fig. 6.22 shows virtual workpiece clamped in virtual vice on virtual Cincinnati Milacron Sabre 750 VMC. Fig. 6.23, Okafor, shows virtual Cincinnati Milacron VMC with tool in cutting motion.
3 Modeling and prediction of cutting forces Modeling cutting forces in milling operations is important for predicting cutting forces that would be generated, power and torque required, and machine tool vibration for a given set of machining parameters prior to actual machining, proper
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FIGURE 6.20 Virtual workpiece clamped on Bridgeport virtual vice.
FIGURE 6.21 Virtual Bridgeport CNC mill simulation. (A) Cutting tool in motion and (B) machined component part.
selection of tools and fixtures for successful and optimum milling operation. High cutting force will induce large deformation of the workpiece and cutting tool leading to bad quality of machined components, severe tool wear, and breakage. Real time measurement of cutting forces is time consuming and very costly. Fast and low cost method is needed for their prediction. This section presents progress of research conducted by the author in cutting force modeling and prediction to be integrated with virtual machine tool for machining simulation and learning in the web.
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FIGURE 6.22 Close view of virtual workpiece clamped on Cincinnati Milacron VMC virtual vise.
FIGURE 6.23 Virtual Cincinnati Milacron Sabre 750 VMC simulation showing tool in cutting motion.
3.1 Introduction Milling processes are used extensively in automotive, aerospace, and tool and die manufacturing industries to make prismatic components. It is categorized as peripheral and face/end milling. For peripheral milling process the cuter axis is parallel to the milled surface, while in face/end milling, the cutter axis is perpendicular to milled surface. Methods reported in the literature for modeling cutting
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forces in milling include: empirical, analytical, and mechanistic [9,10]. Empirical method correlates experimentally measured cutting response variables like cutting force components and tool wear with machining input variables using empirical functions, which is time consuming and very costly. Analytical method modulates physical mechanics of cutting using numerical algorithms, which does not characterize completely the situation at the flank and rake face of the cutting edges, like high strain rates and temperature gradients [9]. Mechanistic modeling method, derives total cutting forces, that is, the summation of the cutting force that is proportional to differential cross-sectional area of undeformed chip, and the edge force that is proportional to differential cutting edge length. Cutting force is responsible for the effect due to shearing, whereas the edge force is responsible for the effect due to ploughing [11–13]. The proportionality constants associated with shearing effect are referred to as cutting force coefficients in the tangential, radial, and axial directions of the cutter diameter (ktc, krc, kac, respectively), and the proportionality constants associated with ploughing effect are referred to as edge force coefficients in the tangential, radial, and axial directions (kte, kre, kae, respectively). They account the end mill geometry and mechanical properties of the machined workpiece [10,11]. Several investigations have been conducted on mechanistic cutting force prediction [14], including early study by Martellotti in 1941 [15,16] on kinematics of end milling, chip formation, and surface finish. Zheng et al. [17] developed a mechanistic model to predict cutting forces in peripheral milling of Aluminum 7075-T6 using helical end-mill, which only accounts for shearing effect and dry machining. Budak et al. [18] mechanistic cutting force model added edge force coefficients to account for the ploughing effect and shearing effect using orthogonal cutting data and oblique cutting analysis, which they claim could replace experiments for the determination of cutting and edge force coefficients. Altintas and Lee [19] analyzed Budak et al.’s model by adding differential edge length to the ploughing effect and used it predicts cutting forces in end milling with helical ball end mill. Engin and Altintas [20,21] developed a generalized geometric model that represents different shapes of end mill, which accounts for shearing and ploughing effects. Their model considered only end mill envelope geometry but not cutting edge geometry. Zhang et al. [22] developed mechanistic cutting force model for serrated-edge end mills, accounted only shearing effect. Merdol and Altintas [23] investigated the effects of serrated end mill profile on chip load, cutting force, power, and vibration. Aderoro and Wen [24] predicted cutting force coefficients, using arbitrary Lagrangian formulation and finite element analysis. It was also reported that the determination of force coefficients does not require experiments [18]. Gradisek et al. [11] used semi empirical mechanistic cutting force model for the identification of force coefficients for general end mills from radial immersion cutting tests of varying sizes. Similarly, during high speed end milling of titanium alloy, a mechanistic identification of cutting and edge force coefficients for simulation of cutting forces was also done [12].
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In this section, the results of mechanistic cutting force model development for wavy edge bull-nose helical end mill (WEBNHE) are presented. WEBNHE was conceived to reduce self-excited vibrations and improve machining dynamics. Thus a need has been established for mechanistic cutting force model that can accounts for the effects of cooling and lubrication as well. Okafor and Sultan [25] developed mechanistic cutting force model for WEBNHE for milling Inconel 718. Sultan and Okafor [26] used the developed and experimentally validated cutting force model to investigate the effect of end mill geometric parameters on cutting forces in end milling Inconel 718, using MQL cooling Strategy. WEBNHE developed by the author incorporates the effects of emulsion and MQL cooling, and it was used to predict cutting forces in end milling of Inconel-718.
3.2 Mechanics of wavy-edge bull-nose helical end mill A photograph of the investigated WEBNHE is shown in Fig. 6.24. Fig. 6.25 shows WEBNHE performing up milling operation. Previous studies [11,12,17,20,21,24–26] show that cutting forces in end milling are proportional to undeformed chip instantaneous cross-sectional area that is time dependent. Differential tangential, radial, and axial cutting force
FIGURE 6.24 Photograph of WEBNHE.
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FIGURE 6.25 WEBNHE in partial radial immersion up-milling, indicating machining parameters (aa, ar, ns, f, φ s, and φ e).
components (dFt, dFr, and dFa) acting at point Pij along the cutting edge are calculated using the following equation:
) dFr (φ j , zt ) = kre dS + krc hc ⋅ db (6.1) dFa (φ j , zt ) = kae dS + kac hc ⋅ db dFt (φ j , zt = kte dS + ktc hcij ⋅ db ij
ij
Eq. (6.1) neglects dynamic and run out of the end mill. The six constants of proportionality as shown in Eq. (6.1) are Ktc, Krc, Kac, Kte, Kre, and Kae. As mentioned previously, they incorporate the effect of tool geometry, and properties of the tool and workpiece material [10,12,17,20,21,25,26]. These coefficients were determined mechanistically from end milling test by the author’s MS student Ameen (2014) [27]. Differential cutting force components dFt, dFr, and dFa are projected on the three perpendicular machine tool axes x, y, and z. The following matrix transformation is used for this projection:
( ) ( )
dFx − cos φij dF = sin φ y ij dF z 0
( ) ( ) − cos (φij ) sin ( kij ) − cos ( kij ) − sin φij sin kij
( ) ( ) − cos (φij ) cos ( kij ) − sin ( kij ) − sin φij cos kij
dFt dF r dFa (6.2)
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Total cutting force acting at each cutting edge is calculated by integrating differential cutting force components dFx, dFy, and dFz along the wavy cutting edges to obtain the following equation:
( ) Fy (φij , kij ) Fz (φij , kij ) Fx φij , kij
=
− dFt cos φij − dFr sin φij sin ( k ) − dFa sin φij dz j =1 Nt aa ∑ ∫0 − dFt sin φij − dFr cos φij sin ( k ) − dFa cos φij dz j =1 Nt aa − dFfr cos kij − dFa sin ( k ) dz ∑ ∫0 0 j =1 Nt
aa
∑ ∫0
( )
( )
( )
( )
( )
( )
( )
(6.3) The previous force prediction model is simulated in MATLAB. The detailed derivation of the previous cutting force model are given in Okafor and Sultan [25,26].
3.3 Cutting force simulation and prediction MATLAB code was developed for simulation of WEBNHE geometric model, and another MATLAB code was developed for simulation and prediction of cutting force components.
3.3.1 MATLAB simulation of WEBNHE geometry The developed MATLAB code was used to simulate WEBNHE geometric model. The geometric simulation involves: (1) the definition of geometric parameters, creating primary points; (2) the rotation of primary points to create knots; (3) the definition of other geometric and cutting parameters; (4) the calculation of spline constants; (5) cubic spline approximation of wavy edge profile; (6) the transformation of linear distances to polar coordinates; and (7) the creation of all cutting edges. The detail procedure is given in Okafor and Sultan [25]. 3.3.2 Cutting force simulation and prediction The geometric model is defined first using the identified six cutting and edge force coefficients, and table feed (f ). Then the feed per tooth (ft) is calculated using specified table feed and tooth pitch in polar coordinates before cutting force simulation and prediction.
4 Cutting force simulation results Cutting force prediction results are presented as follows.
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4.1 Validation of mechanistic cutting force model Machining experiments on Inconel 718 workpiece materials were performed on Cincinnati Milacron VMC to validate the model. Six flutes WEBNHE of 31.75 mm diameter with 3.048 mm corner radius were used for up milling operations at two spindle speeds, 93 and 62 rev/min. Axial and radial depths of cut, and feedrate were kept constant at 18.75 mm, 1.75 mm, and 18.41.85 mm/min, respectively, using emulsion and MQL cooling strategies. Cutting force components, Fx, Fy, and Fz, were acquired using Kistler 9272 four-component dynamometer and Tektronix TDS 420A digitizing oscilloscope at 1 kHz sampling frequency, 5000 sampling points. The acquired cutting force signals were analyzed. The identified cutting and edge force coefficients using emulsion cooling at 93 and 62 rpm are shown in Table 6.1.
4.2 Results and discussions Comparative plots of predicted and measured cutting forces Fx, Fy, Fz, FR at 93 and 62 rpm spindle speed are shown in Figs. 6.26 and 6.27, respectively. Predicted and measured cutting force components perpendicular to feed, Fx, and feed direction, Fy, are in good agreement in magnitude and shape. High spindle speed generated lower cutting force components than low spindle speed. At 93 rpm spindle speed, the perpendicular to feed force, Fx, varied from 802 to 461 N, whereas the feed force, Fy, varied from 2787 to 1532 N. The percentage prediction error for the maximum force Fx was 11.38% and −0.46% for Fy, whereas the prediction error for the minimum force Fx was 15.25% and 17.84% for Fy. The percentage error for Fz was small bit larger than for Fx and Fy. Similar observations were reported in the literature [23–25]. At 62 rpm spindle speed, Fx component varied from 1099 to 906 N, whereas feed force, Fy, varied from 3305 to 2564 N. The prediction error for the maximum force Fx was −0.09% and 13.96% for feed force, Fy; while the prediction error for the minimum force Fx it was 13.25% and 0.35% for feed force Fy. Again, the percentage prediction error was a bit larger than for Fx and Fy components. Fig. 6.28 shows average predicted maximum cutting force components and resultant cutting force at 62 and 93 rpm spindle speed using MQL cooling, and Fig. 6.29 shows the average predicted maximum cutting force components and TABLE 6.1 Cutting force and edge force coefficients for end milling with WEBNHE under emulsion cooling strategy at 93 and 62 rpm spindle speed. Cutting ktc krc speed (rpm) (N/mm2) (N/mm2)
kac (N/mm2)
kte (N/mm)
kre (N/mm)
kae (N/mm)
93
6489.7
−7951.9
179.057
−21.384
9.6
−3.296
62
3172.2
−5135.1
−746.5
40.2797
−45.6
3.3919
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FIGURE 6.26 Predicted and measured cutting force components at 93 rpm.
FIGURE 6.27 Predicted and measured cutting force components at 62 rpm.
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(
)
(
)
FIGURE 6.28 Average predicted maximum cutting force components Fxmax , Fymax , Fzmax and resultant cutting force F Rmax at 62 and 93 rpm under MQL cooling strategy.
(
)
FIGURE 6.29 Average predicted maximum cutting force components Fxmax , Fymax , Fzmax and resultant cutting force F Rmax generated under MQL and emulsions cooling strategies at 93 rpm spindle speed.
(
)
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resultant cutting force generated using MQL and Emulsion cooling strategies at 93 rpm spindle speed.
5 Conclusions and future work 5.1 Conclusion Mathematical model representation of WEBNHE geometry and its mechanistic cutting force model incorporating the effects of emulsion and MQL cooling strategies were developed and used for predicting cutting forces in the high speed end milling of Inconel 718. The following conclusions can be drawn: 1. Mechanistic cutting force model for WEBNHE incorporating the effects of cutting force and edge force coefficients, and cooling strategies (conventional emulsion and MQL cooling) was successfully developed and experimentally validated for predicting cutting forces in end milling Inconel-718. 2. WEBNHE geometry is represented by a mathematical model, using polar coordinate and cubic spline approximation to represent the geometries of the envelope and the wavy-cutting edges, respectively. 3. MATLAB codes were developed and used to simulate mathematical model of WEBNHE geometry and to simulate cutting forces in high speed end milling of Inconel 718. 4. Predicted cutting force components, Fx, Fy, and Fz, were very much in agreement with measured values in both shape and magnitude. 5. High spindle speed of 93 rpm of WEBNHE generates lower cutting forces than low spindle speed of 62 rpm. 6. MQL generates lower cutting force than emulsion cooling.
5.2 Future work Future work will be to integrate cutting force models and virtual CNC machine tool models and implement them on the web for long distance education; and also to integrate cutting force prediction model and virtual CNC machine tools for research, education and training. The proposed overall structure for webbased implantation has been created as shown in Fig. 6.30. The structure include user control panel, virtual controller, material removal simulation, cutting force prediction, machining process estimation, and libraries of machine tools, cutting tools, workpiece shape, and materials.
Acknowledgments Financial support from National Science Foundation under grant No. CMMI800871 is gratefully acknowledged. Graduate Research Assistantship and Graduate Teaching Assistantship from the Intelligent Systems Center and from Mechanical and Aerospace Engineering Department, respectively, at Missouri University of Science and technology are also gratefully acknowledged. The author thanks former graduate students, Vinay R. Talekar, V. Irigireddy,
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FIGURE 6.30 Proposed structure for web-based implementation of virtual CNC machine tools model integration with cutting force prediction model.
R. Gulati, and Abdulhakim Ali Sultan who performed virtual modeling and simulation for Bridgeport and Cincinnati Milacron Sabre 750 VMC and cutting force prediction models.
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[10] O. Gonzalo, et al. Prediction of specific force coefficients from a FEM cutting model, Int. J. Adv. Manuf. Tech. 43 (3–4) (2009) 348–356. [11] J. Gradisek, M. Kalveram, K. Weinert, Mechanistic identification of specific force coefficients for a general endmill, Int. J. Mach. Tool. Manuf. 44 (2004) 401–414. [12] V. Talekar. Determination of Specific Cutting Force Coefficients for Web-based Cutting Force Modeling and Simulation in Machining Titanium Alloys. MS thesis. Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, 2011. [13] J.J. Wang, C.M. Zheng, Identification of shearing and ploughing cutting constants from average forces in ball endmilling, Int. J. Mach. Tool. Manu. 42 (2002) 695–705. [14] M. Fontaine, et al. Modeling of cutting forces in ball end-milling with tool-surface inclination, Part I: predictive force model and experimental validation, J. Mat.. Process Tech. 189 (2007) 73–84. [15] M. Martellotti, An analysis of the milling process, Transact. ASME 63 (1941) 677–700. [16] M. Martellotti, An analysis of the milling process, Part II: Down-milling, Transact. ASME 67 (1945) 233–251. [17] L. Zheng, et al. Three dimensional cutting force analysis in endmilling, Int. J. Mech. Sci. 38 (3) (1996) 259–269. [18] E. Budak, et al. Prediction of milling force coefficients from orthogonal cutting data, J. Manuf. Sci. E.-T. ASME 118 (1996) 216–224. [19] Y. Altinta, P. Lee, Mechanics and dynamics of ball endmilling, J. Manuf. Sci. E.-T. ASME 120 (1998) 684–692. [20] S. Engin, Y. Altintas, Generalized modeling of milling mechanics and dynamics: Part I - Helical endmills, Int. J. Mach. Tool. Manu. 41 (15) (2001) 2195–2212. [21] S. Engin, Y. Altintas, Generalized modeling of milling mechanics and dynamics: Part II – Inserts cutters, Int. J. Mach. Tool. Manu. 41 (15) (2001) 2213–2231. [22] Z. Zhang, et al. A cutting force model for a waved-edge end-milling cutter, Int. J. Adv. Manuf. Tech 21 (2003) 403-L410. [23] S. Merdol, Y. Altintas, Mechanics and dynamics of serrated cylindrical and tapered endmills, J. Manuf. Sci. E.-T. ASME 126 (2004) 317–326. [24] O. Adetoro, P. Wen, Prediction of mechanistic cutting force coefficients using ALE formulation, Int. J. Mach. Tool. Manuf. 46 (2010) 79–90. [25] A. Chukwujekwu Okafor, A.A. Sultan, Development of a mechanistic cutting force model for wavy-edge bull-nose helical end-milling of Inconel 718 under emulsion cooling strategy, Appl. Mathemat. Model. 40 (2016) 2637–2660, doi: 10.1016/j.apm.2015.09.040. [26] A.A. Sultan, A.C. Okafor, Effects of geometric parameters of wavy-edge bull-nose helical end-mill on cutting force prediction in end-milling of Inconel 718 under MQL cooling strategy, J. Manuf. Processes 23 (2016) 102–114, doi: 10.1016/j.mapro.2016.05.015. [27] M. S. Ameen. Mechanistic Identification of Specific Cutting Force Coefficients in End-milling Inconel 718 Under Four Cooling Strategies. MS thesis. MO, Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, 2014.
Virtual CNC machine tool modeling and machining simulation in high speed milling Anthony Chukwujekwu Okafor Computer Numerical Control and Virtual Manufacturing Laboratory, Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO, United States
Chapter outline 1 Introduction 163 2 Virtual modeling of machine tools and their simulation 164 2.1 Real Bridgeport CNC mill 164 2.2 Physical vertical machining center—Cincinnati Milacron Sabre 750 168 2.3 CNC programs simulation 174 3 Modeling and prediction of cutting forces 174 3.1 Introduction 176
3.2
4
5
Mechanics of wavy-edge bull-nose helical end mill 178 3.3 Cutting force simulation and prediction 180 Cutting force simulation results 180 4.1 Validation of mechanistic cutting force model 181 4.2 Results and discussions 181 Conclusions and future work 184 5.1 Conclusion 184 5.2 Future work 184
1 Introduction Virtual machining (VM) provides an environment to model and simulate machining processes in the computer [1]. Machining simulation is used to optimize processes to improve quality and flexibility or to evaluate machining parameters and make manufacturing decisions. VM generally include well-designed graphical user interface for selecting and inputting required machining parameters, mechanistic cutting force models, and a post-process tool like graphical multimedia results indicators and report generators. To a layperson, VM means making and analyzing parts in the computer, not on actual machine tool [2]. Iwata et al. [3] described architecture for modeling and simulation of virtual manufacturing systems. High Speed Machining. http://dx.doi.org/10.1016/B978-0-12-815020-7.00006-0 Copyright © 2020 Elsevier Inc. All rights reserved.
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Virtual CNC assumes that imitation-real machining can be conceptualized prior to commencement of real machining on physical CNC machine tool. A computer is used to create 3D model of the machine tool, to simulate and visualize their machining operation [4]. Effects of machining parameters can be simulated using virtual machine tools (VCNC-MTs) and actual CNC programs. The benefits of virtual CNC simulation include detecting before machining, any strange behavior that could occur during machining, such as tool collision, axis drive overtravel, etc. Yao et al. [5] described theoretical basis for developing virtual turning and milling. Fang et al. reported using immersion VR to develop virtual manual and CNC lathe, but it does not incorporate collision detection [6]. Suh et al. described how to simulate CNC milling machine on the web using virtual reality modeling language, COSMO player, and JAVA applet [7]. This chapter will give the reader an overview of research results on virtual modeling of CNC milling machine (Bridgeport mill), vertical machining center (Cincinnati Milacron Sabre 750), hereafter referred to VMC, and CNC turret lathe (Okuma LB 15), and their use for simulation of CNC programs in milling and turning Inconel and titanium alloys. The research on the development of mechanistic cutting force model to be integrated with virtual machine tool model for simulation and training is also presented. The chapter sections are as follows. Section 2 describes virtual modeling of machine tools and simulation; Section 2.1 describes real Bridgeport CNC mill; Section 2.1.1 describes virtual modeling of Bridgeport CNC mill; Section 2.1.2 describes virtual Bridgeport components assembly; Section 2.1.3 describes assigning kinematics to the Bridgeport CNC mill axes; and Section 2.1.4 describes virtual controller development. Section 2.2 describes real vertical machining center (Cincinnati Milacron Sabre 750); Section 2.2.1 describes virtual modeling of vertical machining center; and Section 2.3 describes the simulation of CNC programs. Section 3 describes mechanistic modeling and prediction of cutting forces; Section 4 presents results of cutting force simulation; and Section 5.0 summarizes the chapter along with mentioning future trends. After reading this chapter, the reader should be able to understand the concept of virtual manufacturing and VM, cutting force prediction using mechanistic cutting force model.
2 Virtual modeling of machine tools and their simulation Fig. 6.1 shows the schematic diagram of virtual modeling of machine tools (Bridgeport mill, Okuma LB15 turret lathe, and Cincinnati Milacron Sabre 750 VMC), which includes modeling major component parts of machine tool, controller, their assembly, assigning kinematics to the axes, and machining simulation.
2.1 Real Bridgeport CNC mill The real Bridgeport CNC mill is a 3-axis (actually a 2-1/2 axis) milling machine. A picture of the real Bridgeport CNC mill is shown in Fig. 6.2 [6].
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FIGURE 6.1 Flowchart for virtual modeling of machine tools.
2.1.1 Creating virtual model of Bridgeport CNC mill Deneb Robotics Virtual NC software, now DELMIA from Dassault 3D-Systems was used to model components of the real Bridgeport CNC Mill. DELMIA is menu driven with 3D CAD system. CAD primitives, such as block, cylinder, etc., were used to create the machine tool components. The following virtual
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FIGURE 6.2 Real Bridgeport CNC Mill.
components created are: Fig. 6.3, virtual Bridgeport mill base; Fig. 6.4, virtual bridge Bridgeport knee; Fig. 6.5, virtual Bridgeport Y-axis (saddle); Fig. 6.6, virtual Bridgeport X-axis (table); Fig. 6.7, virtual Bridgeport mill head; Fig. 6.8, assembled virtual tool and tool holder; and Fig. 6.9, virtual workpiece. The details of the modeling procedure are given in Okafor et al. (2010) [1], and Vishnu (2000) [8].
2.1.2 Assembly of virtual components of Bridgeport mill Assembling various Bridgeport mill components to build a device, in this case, virtual Bridgeport CNC mill is accomplished using BUILD and SETUP menu of the virtual NC software. The detailed steps are given in Okafor et al. [1,2]. 2.1.3 Assigning kinematics to virtual Bridgeport mill axes The Bridgeport mill axes movement during VM simulation is referred to as kinematic. The following are defined: axes translational or rotational motion, degrees of freedom, speeds and accelerations, travel limits, etc., according to machine specification. The assembled virtual Bridgeport mill with clamped workpiece in the machine vice is shown in Fig. 6.10. The table axis changes color to cherry whenever
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FIGURE 6.3 Virtual Bridgeport mill base.
FIGURE 6.4 Virtual Bridgeport knee.
it overshoots its travel limit as shown in the figure alerting that the X-axis over travel has occurred.
2.1.4 Virtual controller for Bridgeport CNC mill Virtual controller is a post processor that can reproduce all functionality of real Bridgeport CNC mill controller. The virtual controller reads CNC program blocks and produces a list of commands that are used to perform the simulation.
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FIGURE 6.5 Virtual Bridgeport Y-axis (saddle).
FIGURE 6.6 Virtual Bridgeport X-axis (table).
The virtual controller is divided into three units: the declaration, code definition, and function definition. Details are given in Okafor et al. (2010) [1] and Vishnu (2000) [8].
2.2 Physical vertical machining center—Cincinnati Milacron Sabre 750 The Physical Cincinnati Milacron Sabre 750 VMC is a 3-axis VMC that has three sliding axes and an optional fourth rotary axis under computer numerical control, equipped with automatic tool changer drum with 21 tool holding
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FIGURE 6.7 Virtual mill Bridgeport mill head.
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FIGURE 6.8 Assembled tool and tool holder.
FIGURE 6.9 Virtual workpiece.
capacity. It has many milling and drilling canned cycles. The machine has an Acramatic 2100E open architecture controller; and it has the capability to be programmed for probing inspection of machined parts dimensions. It utilizes an executive memory that runs on Window NT operating system. The machine was bought without the fourth axis so the fourth axis is not modeled. The machine is used conducting research on machining and machine tool metrology,
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FIGURE 6.10 Assembled virtual Bridgeport mill alerting X-axis over travel.
and for laboratory instruction on CNC programming and machining for the courses; ME5653, CNC of manufacturing processes, and ME6653, advanced CNC and engineering metrology; taught to undergraduate and graduate students in Mechanical and Aerospace Engineering Department at Missouri S&T. The VMC consists of the following assemblies: “main base unit,” which is bolted to the floor; “column unit” with slide ways, which is clamped to the base unit; “spindle carrier unit”, which carries traveling spindle that provides Z-axis movement; “saddle unit”, which provides Y-axis travel; “table”, which provides X-axis travel and also the work surface; and the tool “storage unit”. Fig. 6.11 shows a digital picture of the physical Cincinnati Milacron Sabre 750 VMC. For this research, outer cover of the VMC is not modeled so as to give a clear view and understanding of every machine component motion during simulation. Virtual modeling, assembly, and simulation were done as per flowchart shown in Fig. 6.1.
2.2.1 Virtual modeling of Cincinnati Sabre 750 VMC Virtual components of Cincinnati Milacron VMC were created using Deneb Robotics virtual NC software, now DELMIA, following similar procedure used for Bridgeport. The following virtual components were created: Fig. 6.12, main base unit and column unit; Fig. 6.13, spindle carrier unit and spindle; Fig. 6.14,
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FIGURE 6.11 Physical Cincinnati Milacron Sabre 750 VMC.
FIGURE 6.12 Mill base and column unit of Cincinnati Milacron Sabre 750 VMC. (A) Virtual main base unit and (B) column unit.
FIGURE 6.13 Spindle carrier and spindle of Cincinnati Milacron Sabre 750 VMC. (A) Spindle carrier unit (Z-axis) and (B) spindle.
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FIGURE 6.14 Saddle and table unit of Cincinnati Milacron Sabre 750 VMC. (A) Saddle unit (Y-axis) and (B) table unit (X-axis).
FIGURE 6.15 Tool drum and tool holder of Cincinnati Milacron Sabre 750 VMC. (A) Tool drum and (B) tool holder.
FIGURE 6.16 Tool assembly and tools in tool drum. (A) Tool assembly and (B) tools in tool drum.
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FIGURE 6.17 Assembly process of Cincinnati Milacron Sabre 750 VMC. (A) Assembled base and column units and (B) assembled base and saddle units.
FIGURE 6.18 Final assembled Cincinnati Milacron Sabre 750 VMC.
saddle unit (Y-axis) and table unit (X-axis); Fig. 6.15 tool drum and tool holder; Fig. 6.16, tool assembly and tools in tool drum; and Fig. 6.17, assembly process.
2.2.2 Assembly of Cincinnati Milacron Sabre 750 VMC Assembling the various Cincinnati Milacron VMC components to build a device is accomplished using BUILD and SETUP menu bars, by attaching the virtual components to each other and defining their relationships. The assembled base and column units, and assembled base and saddle units are shown in Fig. 6.14. Fig. 6.18 shows the final assembled virtual Cincinnati Milacron Sabre 750 VMC with clamped workpiece in the machine vice, and all tools in their respective tool drum and the three axes assigned their kinematics.
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FIGURE 6.19 Virtual Cincinnati Milacron Sabre 750 VMC indicating axis over travel.
2.2.3 Assignment of axes kinematics to the Cincinnati Milacron VMC The three X-, Y-, and Z-axis of Cincinnati Milacron VMC were assigned a translational motion, using the procedure similar to that for Bridgeport. Fig. 6.19 shows Virtual Cincinnati Milacron Sabre 750 VMC, indicating X-axis over travel by the table color turning to pink. 2.3 CNC programs simulation CNC programs written for the courses; ME5653, CNC of manufacturing processes and ME6653, advanced CNC and engineering metrology; for senior undergraduate and graduate students in Mechanical and Aerospace Engineering Department at Missouri S&T were successfully simulated using the developed virtual Bridgeport CNC Mill, Virtual Cincinnati Milacron Sabre 750 VMC, and Virtual Okuma LB 15 CNC Turret Lathe. Fig. 6.20 shows virtual workpiece clamped the virtual Bridgeport mill virtual vice. Fig. 6.21A shows virtual Bridgeport mill with cutting tool in motion, and Fig. 6.21B shows machined part after the simulation. Fig. 6.22 shows virtual workpiece clamped in virtual vice on virtual Cincinnati Milacron Sabre 750 VMC. Fig. 6.23, Okafor, shows virtual Cincinnati Milacron VMC with tool in cutting motion.
3 Modeling and prediction of cutting forces Modeling cutting forces in milling operations is important for predicting cutting forces that would be generated, power and torque required, and machine tool vibration for a given set of machining parameters prior to actual machining, proper
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FIGURE 6.20 Virtual workpiece clamped on Bridgeport virtual vice.
FIGURE 6.21 Virtual Bridgeport CNC mill simulation. (A) Cutting tool in motion and (B) machined component part.
selection of tools and fixtures for successful and optimum milling operation. High cutting force will induce large deformation of the workpiece and cutting tool leading to bad quality of machined components, severe tool wear, and breakage. Real time measurement of cutting forces is time consuming and very costly. Fast and low cost method is needed for their prediction. This section presents progress of research conducted by the author in cutting force modeling and prediction to be integrated with virtual machine tool for machining simulation and learning in the web.
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FIGURE 6.22 Close view of virtual workpiece clamped on Cincinnati Milacron VMC virtual vise.
FIGURE 6.23 Virtual Cincinnati Milacron Sabre 750 VMC simulation showing tool in cutting motion.
3.1 Introduction Milling processes are used extensively in automotive, aerospace, and tool and die manufacturing industries to make prismatic components. It is categorized as peripheral and face/end milling. For peripheral milling process the cuter axis is parallel to the milled surface, while in face/end milling, the cutter axis is perpendicular to milled surface. Methods reported in the literature for modeling cutting
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forces in milling include: empirical, analytical, and mechanistic [9,10]. Empirical method correlates experimentally measured cutting response variables like cutting force components and tool wear with machining input variables using empirical functions, which is time consuming and very costly. Analytical method modulates physical mechanics of cutting using numerical algorithms, which does not characterize completely the situation at the flank and rake face of the cutting edges, like high strain rates and temperature gradients [9]. Mechanistic modeling method, derives total cutting forces, that is, the summation of the cutting force that is proportional to differential cross-sectional area of undeformed chip, and the edge force that is proportional to differential cutting edge length. Cutting force is responsible for the effect due to shearing, whereas the edge force is responsible for the effect due to ploughing [11–13]. The proportionality constants associated with shearing effect are referred to as cutting force coefficients in the tangential, radial, and axial directions of the cutter diameter (ktc, krc, kac, respectively), and the proportionality constants associated with ploughing effect are referred to as edge force coefficients in the tangential, radial, and axial directions (kte, kre, kae, respectively). They account the end mill geometry and mechanical properties of the machined workpiece [10,11]. Several investigations have been conducted on mechanistic cutting force prediction [14], including early study by Martellotti in 1941 [15,16] on kinematics of end milling, chip formation, and surface finish. Zheng et al. [17] developed a mechanistic model to predict cutting forces in peripheral milling of Aluminum 7075-T6 using helical end-mill, which only accounts for shearing effect and dry machining. Budak et al. [18] mechanistic cutting force model added edge force coefficients to account for the ploughing effect and shearing effect using orthogonal cutting data and oblique cutting analysis, which they claim could replace experiments for the determination of cutting and edge force coefficients. Altintas and Lee [19] analyzed Budak et al.’s model by adding differential edge length to the ploughing effect and used it predicts cutting forces in end milling with helical ball end mill. Engin and Altintas [20,21] developed a generalized geometric model that represents different shapes of end mill, which accounts for shearing and ploughing effects. Their model considered only end mill envelope geometry but not cutting edge geometry. Zhang et al. [22] developed mechanistic cutting force model for serrated-edge end mills, accounted only shearing effect. Merdol and Altintas [23] investigated the effects of serrated end mill profile on chip load, cutting force, power, and vibration. Aderoro and Wen [24] predicted cutting force coefficients, using arbitrary Lagrangian formulation and finite element analysis. It was also reported that the determination of force coefficients does not require experiments [18]. Gradisek et al. [11] used semi empirical mechanistic cutting force model for the identification of force coefficients for general end mills from radial immersion cutting tests of varying sizes. Similarly, during high speed end milling of titanium alloy, a mechanistic identification of cutting and edge force coefficients for simulation of cutting forces was also done [12].
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In this section, the results of mechanistic cutting force model development for wavy edge bull-nose helical end mill (WEBNHE) are presented. WEBNHE was conceived to reduce self-excited vibrations and improve machining dynamics. Thus a need has been established for mechanistic cutting force model that can accounts for the effects of cooling and lubrication as well. Okafor and Sultan [25] developed mechanistic cutting force model for WEBNHE for milling Inconel 718. Sultan and Okafor [26] used the developed and experimentally validated cutting force model to investigate the effect of end mill geometric parameters on cutting forces in end milling Inconel 718, using MQL cooling Strategy. WEBNHE developed by the author incorporates the effects of emulsion and MQL cooling, and it was used to predict cutting forces in end milling of Inconel-718.
3.2 Mechanics of wavy-edge bull-nose helical end mill A photograph of the investigated WEBNHE is shown in Fig. 6.24. Fig. 6.25 shows WEBNHE performing up milling operation. Previous studies [11,12,17,20,21,24–26] show that cutting forces in end milling are proportional to undeformed chip instantaneous cross-sectional area that is time dependent. Differential tangential, radial, and axial cutting force
FIGURE 6.24 Photograph of WEBNHE.
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FIGURE 6.25 WEBNHE in partial radial immersion up-milling, indicating machining parameters (aa, ar, ns, f, φ s, and φ e).
components (dFt, dFr, and dFa) acting at point Pij along the cutting edge are calculated using the following equation:
) dFr (φ j , zt ) = kre dS + krc hc ⋅ db (6.1) dFa (φ j , zt ) = kae dS + kac hc ⋅ db dFt (φ j , zt = kte dS + ktc hcij ⋅ db ij
ij
Eq. (6.1) neglects dynamic and run out of the end mill. The six constants of proportionality as shown in Eq. (6.1) are Ktc, Krc, Kac, Kte, Kre, and Kae. As mentioned previously, they incorporate the effect of tool geometry, and properties of the tool and workpiece material [10,12,17,20,21,25,26]. These coefficients were determined mechanistically from end milling test by the author’s MS student Ameen (2014) [27]. Differential cutting force components dFt, dFr, and dFa are projected on the three perpendicular machine tool axes x, y, and z. The following matrix transformation is used for this projection:
( ) ( )
dFx − cos φij dF = sin φ y ij dF z 0
( ) ( ) − cos (φij ) sin ( kij ) − cos ( kij ) − sin φij sin kij
( ) ( ) − cos (φij ) cos ( kij ) − sin ( kij ) − sin φij cos kij
dFt dF r dFa (6.2)
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Total cutting force acting at each cutting edge is calculated by integrating differential cutting force components dFx, dFy, and dFz along the wavy cutting edges to obtain the following equation:
( ) Fy (φij , kij ) Fz (φij , kij ) Fx φij , kij
=
− dFt cos φij − dFr sin φij sin ( k ) − dFa sin φij dz j =1 Nt aa ∑ ∫0 − dFt sin φij − dFr cos φij sin ( k ) − dFa cos φij dz j =1 Nt aa − dFfr cos kij − dFa sin ( k ) dz ∑ ∫0 0 j =1 Nt
aa
∑ ∫0
( )
( )
( )
( )
( )
( )
( )
(6.3) The previous force prediction model is simulated in MATLAB. The detailed derivation of the previous cutting force model are given in Okafor and Sultan [25,26].
3.3 Cutting force simulation and prediction MATLAB code was developed for simulation of WEBNHE geometric model, and another MATLAB code was developed for simulation and prediction of cutting force components.
3.3.1 MATLAB simulation of WEBNHE geometry The developed MATLAB code was used to simulate WEBNHE geometric model. The geometric simulation involves: (1) the definition of geometric parameters, creating primary points; (2) the rotation of primary points to create knots; (3) the definition of other geometric and cutting parameters; (4) the calculation of spline constants; (5) cubic spline approximation of wavy edge profile; (6) the transformation of linear distances to polar coordinates; and (7) the creation of all cutting edges. The detail procedure is given in Okafor and Sultan [25]. 3.3.2 Cutting force simulation and prediction The geometric model is defined first using the identified six cutting and edge force coefficients, and table feed (f ). Then the feed per tooth (ft) is calculated using specified table feed and tooth pitch in polar coordinates before cutting force simulation and prediction.
4 Cutting force simulation results Cutting force prediction results are presented as follows.
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4.1 Validation of mechanistic cutting force model Machining experiments on Inconel 718 workpiece materials were performed on Cincinnati Milacron VMC to validate the model. Six flutes WEBNHE of 31.75 mm diameter with 3.048 mm corner radius were used for up milling operations at two spindle speeds, 93 and 62 rev/min. Axial and radial depths of cut, and feedrate were kept constant at 18.75 mm, 1.75 mm, and 18.41.85 mm/min, respectively, using emulsion and MQL cooling strategies. Cutting force components, Fx, Fy, and Fz, were acquired using Kistler 9272 four-component dynamometer and Tektronix TDS 420A digitizing oscilloscope at 1 kHz sampling frequency, 5000 sampling points. The acquired cutting force signals were analyzed. The identified cutting and edge force coefficients using emulsion cooling at 93 and 62 rpm are shown in Table 6.1.
4.2 Results and discussions Comparative plots of predicted and measured cutting forces Fx, Fy, Fz, FR at 93 and 62 rpm spindle speed are shown in Figs. 6.26 and 6.27, respectively. Predicted and measured cutting force components perpendicular to feed, Fx, and feed direction, Fy, are in good agreement in magnitude and shape. High spindle speed generated lower cutting force components than low spindle speed. At 93 rpm spindle speed, the perpendicular to feed force, Fx, varied from 802 to 461 N, whereas the feed force, Fy, varied from 2787 to 1532 N. The percentage prediction error for the maximum force Fx was 11.38% and −0.46% for Fy, whereas the prediction error for the minimum force Fx was 15.25% and 17.84% for Fy. The percentage error for Fz was small bit larger than for Fx and Fy. Similar observations were reported in the literature [23–25]. At 62 rpm spindle speed, Fx component varied from 1099 to 906 N, whereas feed force, Fy, varied from 3305 to 2564 N. The prediction error for the maximum force Fx was −0.09% and 13.96% for feed force, Fy; while the prediction error for the minimum force Fx it was 13.25% and 0.35% for feed force Fy. Again, the percentage prediction error was a bit larger than for Fx and Fy components. Fig. 6.28 shows average predicted maximum cutting force components and resultant cutting force at 62 and 93 rpm spindle speed using MQL cooling, and Fig. 6.29 shows the average predicted maximum cutting force components and TABLE 6.1 Cutting force and edge force coefficients for end milling with WEBNHE under emulsion cooling strategy at 93 and 62 rpm spindle speed. Cutting ktc krc speed (rpm) (N/mm2) (N/mm2)
kac (N/mm2)
kte (N/mm)
kre (N/mm)
kae (N/mm)
93
6489.7
−7951.9
179.057
−21.384
9.6
−3.296
62
3172.2
−5135.1
−746.5
40.2797
−45.6
3.3919
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FIGURE 6.26 Predicted and measured cutting force components at 93 rpm.
FIGURE 6.27 Predicted and measured cutting force components at 62 rpm.
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(
)
(
)
FIGURE 6.28 Average predicted maximum cutting force components Fxmax , Fymax , Fzmax and resultant cutting force F Rmax at 62 and 93 rpm under MQL cooling strategy.
(
)
FIGURE 6.29 Average predicted maximum cutting force components Fxmax , Fymax , Fzmax and resultant cutting force F Rmax generated under MQL and emulsions cooling strategies at 93 rpm spindle speed.
(
)
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resultant cutting force generated using MQL and Emulsion cooling strategies at 93 rpm spindle speed.
5 Conclusions and future work 5.1 Conclusion Mathematical model representation of WEBNHE geometry and its mechanistic cutting force model incorporating the effects of emulsion and MQL cooling strategies were developed and used for predicting cutting forces in the high speed end milling of Inconel 718. The following conclusions can be drawn: 1. Mechanistic cutting force model for WEBNHE incorporating the effects of cutting force and edge force coefficients, and cooling strategies (conventional emulsion and MQL cooling) was successfully developed and experimentally validated for predicting cutting forces in end milling Inconel-718. 2. WEBNHE geometry is represented by a mathematical model, using polar coordinate and cubic spline approximation to represent the geometries of the envelope and the wavy-cutting edges, respectively. 3. MATLAB codes were developed and used to simulate mathematical model of WEBNHE geometry and to simulate cutting forces in high speed end milling of Inconel 718. 4. Predicted cutting force components, Fx, Fy, and Fz, were very much in agreement with measured values in both shape and magnitude. 5. High spindle speed of 93 rpm of WEBNHE generates lower cutting forces than low spindle speed of 62 rpm. 6. MQL generates lower cutting force than emulsion cooling.
5.2 Future work Future work will be to integrate cutting force models and virtual CNC machine tool models and implement them on the web for long distance education; and also to integrate cutting force prediction model and virtual CNC machine tools for research, education and training. The proposed overall structure for webbased implantation has been created as shown in Fig. 6.30. The structure include user control panel, virtual controller, material removal simulation, cutting force prediction, machining process estimation, and libraries of machine tools, cutting tools, workpiece shape, and materials.
Acknowledgments Financial support from National Science Foundation under grant No. CMMI800871 is gratefully acknowledged. Graduate Research Assistantship and Graduate Teaching Assistantship from the Intelligent Systems Center and from Mechanical and Aerospace Engineering Department, respectively, at Missouri University of Science and technology are also gratefully acknowledged. The author thanks former graduate students, Vinay R. Talekar, V. Irigireddy,
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FIGURE 6.30 Proposed structure for web-based implementation of virtual CNC machine tools model integration with cutting force prediction model.
R. Gulati, and Abdulhakim Ali Sultan who performed virtual modeling and simulation for Bridgeport and Cincinnati Milacron Sabre 750 VMC and cutting force prediction models.
References [1] A. Chukwujekwu Okafor, Vinay R. Talekar, V. Irigireddy, R. Gulati, Development of webbased virtual CNC milling machine tool with mechanistic cutting force models for education and learning,’ in: Proceedings of ASME 2010 World Conference on Innovative Virtual Reality, WINVR2010, 12–14 May, 2010, Ames, Iowa, USA, pp. 1–11. [2] C.R. McLean, Modeling production systems using virtual reality techniques, in: IEEE International Conference on Systems, Man, and Cybernetics, 1998, Vol. 1, pp. 344–347. [3] K. Iwata, M. Onosato, K. Teramoto, S. Osaki, Modeling and simulation architecture for virtual manufacturing systems, CIRP Annal. 44 (1) (1995) 399–402. [4] https://www.3ds.com/products-services/delmia/disciplines/industrial-engineering/tag/288346/ [5] Y. Yao, Y. Yao, J. Li, C. Liu, A virtual machining based training system for numerically controlled machining, Comput. Appl. Eng. Educ. 15 (2007) 64–72. [6] X. Daniel Fang, S. Luo, N.J. Lee, F. Jin, Virtual machining lab for knowledge learning and skills training, Comput. Appl. Eng. Edu. 6 (1998) 89–97. [7] S. Suh, Y. Seo, T. Choi, G. Jeong, D. Kim, Modeling and implementation of internet-based virtual machine tools, Int. J. Adv. Manuf. Technol. 21 (2003) 516–522. [8] V.V.V. Irigireddy. Virtual Modeling and Simulation of Vertical Machining Center and CNC Milling Machine for Training and Instruction. MS thesis. Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, 2000. [9] S. Jayaram, et al. Estimation of the specific cutting pressures for mechanistic cutting force models, Int. J. Mach. Tool. Manu. 41 (2001) 265–281.
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