Adaptive self-learning controller design for feedrate maximisation of machining process

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1 o Achievements in Materials and Manuacturing Engineering VOLUME 31 ISSUE 2 December 28 Adaptive sel-learning controller design or eedrate maximisation o machining process F. Cus, U. Zuperl*, E. Kiker, M. Milelner Faculty o Mechanical Engineering, University o Maribor, Smetanova 17, 2 Maribor, Slovenia * Corresponding author: uros.zuperl@uni-mb.si Received ; published in revised orm ABSTRACT Analysis and modelling Purpose: O this paper: The purpose o this paper is to built an adaptive control system which controlling the cutting orce and maintaining constant roughness o the surace being milled by digital adaptation o cutting parameters. Design/methodology/approach: The paper discusses the use o combining the methods o neural networks, uzzy logic and PSO evolutionary strategy (Particle Swarm Optimization) in modeling and adaptively controlling the process o end milling. An overall approach o hybrid modeling o cutting process (ANis-system), used or working out the CNC milling simulator has been prepared. The basic control design is based on the control scheme (UNKS) consisting o two neural identiicators o the process dynamics and primary regulator. Findings: The research has shown that neural control scheme has signiicant advantages over conventional controllers. The experimental results show that not only does the milling system with the design controller have high robustness, and global stability but also the machining eiciency o the milling system with the adaptive controller is much higher than or traditional CNC milling system. Experiments have conirmed eiciency o the adaptive control system, which is relected in improved surace quality and decreased tool wear. Research limitations/implications: The proposed architecture or on-line determining o optimal cutting conditions is applied to ball-end milling in this paper, but it is obvious that the system can be extended to other machines to improve cutting eiciency. In this way the system compensates all disturbances during the cutting process: tool wear, non-homogeneity o the workpiece material, vibrations, chatter etc. Practical implications: The results o experiments demonstrate the ability o the proposed system to eectively regulate peak cutting orces or cutting conditions commonly encountered in end milling operations. Applicability o methodology o adaptive adjustment o cutting parameters is experimentally demonstrated and tested on a 4-axis CNC milling machine Heller. The high accuracy o results within a wide range o machining parameters indicates that the system can be practically applied in industry. Originality/value: By the hybrid process modeling and eed-orward neural control scheme (UNKS) the combined system or o-line optimization and adaptive adjustment o cutting parameters is built. Keywords: Artiicial Intelligence Methods; Machining; Force control; Adaptive control with optimisation Copyright by International OCSCO World Press. All rights reserved. 28 Research paper 469

2 Journal o Achievements in Materials and Manuacturing Engineering Volume 31 Issue 2 December Introduction The use o computer numerical control (CNC) machining centers has expanded rapidly through the years. A great advantage o the CNC machining center is that it reduces the skill requirements o machine operators. However, a common drawback o CNC end milling is that its operating parameter such as spindle speed or eedrate is prescribed conservatively either by a part programmer or by a relatively static database in order to preserve the tool. As a result, many CNC systems run under ineicient operating conditions. For this reason, CNC machine tool control systems, which provides on-line adjustment o the operating parameters, is being studied with interest. These systems can be classiied into three types: a geometric adaptive compensation (GAC) system; an adaptive control optimization (ACO) system; and an adaptive control constraints (ACC) system. GAC systems enhance part precision by applying real time geometric error compensation or imprecision caused by varying machine temperature, imprecise machine geometry, tool wear and other actors [1]. However, due to the diiculty in on-line measurement o tool wear and machine tool temperature, there are no commercial GAC systems available [12]. ACO systems and ACC systems enhance productivity by applying an adaptive control technique to vary then machining variables in real time [8]. ACO systems set up the most eective cutting condition or the present cutting environment. For this purpose, ACO systems require on-line measurement o tool wear. Due to this reason, ew, i any, ACO systems are used in practice [4, 5, 15]. ACC systems increase productivity by maximizing one or many machining variables within a prescribed range bounded by process and system constraints [17]. The most commonly used constraints in ACC systems are the cutting orce, spindle current and cutting torque. The operating parameters are usually eedrate and spindle speed. Unortunately, adaptive control alone cannot eectively control cutting orces. There is no controller that can respond quickly enough to sudden changes in the cut geometry to eliminate large spikes in cutting orces. Thereore, we implement on-line adaptive control in conjunction with o-line optimization. The optimization is perormed with algorithm developed by researchers [3]. In our AC system, the eedrate is adjusted on-line in order to maintain a constant cutting orce in spite o variations in cutting conditions. The paper is organised as ollows. The ollowing section briely describes the overall cutting orce control strategy. Section our covers the CNC milling simulator. Section ive describes the experimental equipement o adaptive control system. Finally, sections 6 and 7 present experimental results, conclusions, and recommendations or uture research. 2. System or o-line 2. System optimization or o-line and optimization adaptive and adaptive cutting cutting orce orce control The overall orce control strategy consists o optimizing the eedrates o-line, and then applying on-line adaptive control during the machining process. The basic idea o this design is to merge the o-line cutting condition optimization algorithm and adaptive orce control (Figure 1). Based on this new combined control system, very complicated processes can be controlled more easily and accurately compared to standard approaches. The objective o the developed combined control system is keeping the metal removal rate (MRR) as high as possible and maintaining cutting orce as close as possible to a given reerence value. Combined control system is automatically adjusted to instant cutting conditions by adaptation o eedrate. When spindle loads are low, the system increases eeds above and beyond pre-programmed values, resulting in considerable reductions in machining time and production costs. When spindle loads are high the eed rates are lowered, saeguarding cutting tool rom damage and breakage. Sequence o steps or on-line optimization o the milling process is presented below: 1. The recommended cutting conditions are determined by ANis (adaptive neuro-uzzy inerence system) models, which are basic elements o the sotware or selecting the recommended cutting conditions. 2. Optimization o recommended cutting conditions by PSO optimization. 3. The pre-programmed eed rates determined by o-line optimization algorithm are sent to CNC controller o the milling machine. 4. The measured cutting orces are sent to neural control scheme. 5. Neural control scheme adjusts the optimal eedrates and sends it back to the machine. 6. Steps 1 to 3 are repeated until termination o machining. The adaptive orce controller adjusts the eedrate by assigning a eedrate override percentage to the CNC controller on a 4-axis Heller, based on a measured peak orce (Figure 1). The actual eedrate is the product o the eedrate override percentage (DNCFRO) and the programmed eedrate. I the sotware or optimization o cutting conditions was perect, the optimized eedrate would always be equal to the reerence peak orce. In this case the correct override percentage would be 1%. In order or the controller to regulate peak orce, orce inormation must be available to the control algorithm at every 2 ms. Data acquisition sotware (LabVIEW) and the algorithm or processing the cutting orces are used to provide this inormation. The optimization time by the use o o-line optimization algorithm based on eedorward neural network, is equal to.1s. The combined control system returns the cutting orce value to the desired value level within our or less iteration at the latest. 3. Sel- learning control control scheme scheme The undamental control principle is based on the Feedorward neural control scheme (UNKS) consisting o three parts (Figure 2). The irst part is the loop known as external eedback (conventional control loop). The eedback control is based on the error between the measured (F m ) and desired (F re ) cutting orce. The primary eedback controller is a neural network (NM-R) which imitates the work o division controller. The second part is the loop connected with neural network 1 (NM-1), which is internal model o process dynamics. It acts as the process dynamics identiier. This part represents an internal eedback loop which is much aster than the external eedback loop as the latter usually has sensory delays. 47 Research paper F. Cus, U. Zuperl, E. Kiker, M. Milelner

3 Analysis and modelling ANis models PSO optimization o cutting conditions Recommended cutting conditions Optimal cutting conditions DNC CNC machining process simulator Feed drive model Cutting orce model F re Adaptive controller DNCFRO CNC-controller F m On-line adjustment o cutting parameters Milling process Algorithm or processing o cutting orcess F x,f y,f z Data DATA aquisition AQUISITION Fig. 1. Feedrate override percentage in closed loop system Part-2 Process dynamics identiier NM-1 F m * F m F re F re Primary controller NM-R F re Machine tool F m F m Part-1 * External eedback loop F re Process inverse dynamics identiier NM-2 Part-3 * Weight adjustment Adaptive controller Fig. 2. Feed-orward neural control scheme (UNKS) Adaptive sel-learning controller design or eedrate maximisation o machining process 471

4 Journal o Achievements in Materials and Manuacturing Engineering Volume 31 Issue 2 December 28 Y axis correction.2 Desired eedrate Feed drive dynamics s.64s 1 1 s Slide position Feedrate mm/tooth - Cutting dynamics F y F x Cutting orcess X axis correction One tooth period delay Max.2 One tooth period delay Fig. 3. CNC milling simulator The third part o the system is neural network 2 (NM-2). The NM-2 learns the process inverse dynamics. The UNKS operates according to the ollowing procedure. The sensory eedback is eective mainly in the learning stage. This loop provides a conventional eedback signal to control the process. During the learning stage, NM-2 learns the inverse dynamics. As learning proceeds, the internal eedback gradually takes over the role o the external eedback and primary controller. Then, as learning proceeds urther, the inverse dynamics part will replace the external eedback control. The inal result is that the plant is controlled mainly by NM-1 and NM-2 since the process output error is nearly zero. This is an adaptive control system controlling the cutting orce and maintaining constant roughness o the surace being milled by digital adaptation o cutting parameters. In this way it compensates all disturbances during the cutting process: tool wear, non-homogeneity o the workpiece material, vibrations, chatter etc. 4. CNC milling simulator A CNC milling simulator is used to evaluate the controller design beore conducting experimental tests. The CNC milling simulator tests the system stability and tunes the control scheme parameters. The simulator consists o a neural orce model, a eed drive model and model o elasticity (Figure 3). The neural model predicts cutting orces based on cutting conditions and cut geometry as described by Balic [2] and Zhang et al. [16]. The eed drive model simulates the machine response to changes in desired eedrate. The elasticity model [13] represents the delection between the tool and the workpiece. Model is adapted rom [6] The system elasticity is modeled as static delection o the cutter [7] The eed drive drive model model The eed drive model was determined experimentally by examining responses o the system to step changes in the desired eed velocity. The best model it was ound to be a second-order system with a natural requency o 3 Hz and a settling time o.4 sec. Comparison o experimental and simulation results o a velocity step change rom 7 mm/sec to 22 mm/sec is shown on Figure 4. The eed drive model, neural orce model and elasticity model are combined to orm the CNC milling simulator. Simulator input is the desired eedrate and the output is the X, Y resultant cutting orce. Feedrate [mm/s] Model Experiment Time [s] Fig. 4. Comparison o actual and simulated eedrate The cut geometry is deined in the neural orce model. The simulator is veriied by comparison o experimental and model simulation results. A variety o cuts with eedrate changes were made or validation. The measured and simulation resultant orce or step change in eedrate rom.5 mm/tooth to 2 mm/tooth is presented in Figure 5. The experimental results correlate well with model results in terms o average and peak orce. The obvious discrepancy may be due to inaccuracies in the neural orce model, and unmodeled system dynamics. 472 Research paper F. Cus, U. Zuperl, E. Kiker, M. Milelner

5 F [N] F [N] Analysis and modelling as / time [s] Simulirana rezultirajo a rezalna sila Simulated resultant cutting orce as / time [s] Fig. 5. Comparison o simulated and experimental resultant orce Simulator o o cutting cutting dynamics dynamics To realise the on-line modelling o cutting process, a standard BP neural network (UNM) is used based on the popular back propagation learning rule. During preliminary experiments it proved to be suiciently capable o extracting the orce dynamics model directly rom experimental machining data. It is used to simulate the dynamics o cutting process. The UNM or modelling needs eight input neurons: or ederate (), cutting speed (vc), radial and axial depth o cut (A D / R D ), type o machined material, hardness o the machined material, cutting tool diameter (D), and tool geometry. The ANN registers the input data only in the numerical orm thereore the inormation about the tool, cutting geometry and material must be transormed into numerical code. The geometry o the cutter is indicated with an 8-digit systematization code containing the data on the cutting edge shape, rake angle, ree angle, tip radius, base material, cutting coating and length o the cutting edge. The output rom the UNM are cutting orce components, thereore three output neurons are necessary. For simpliication o the milling simulator the neural network is so adapted that during prediction overlooks all input parameters except eeding. During simulation most input vector parameters do not change (e.g. cutter diameter and geometry, material etc.). The detailed topology o the used NN with optimal training parameters is shown in Figure 6. Optimal UNM coniguration contains 5, 3 and 7 neurons in hidden lazers. UNM Training o UNM Hidden layers 1 2 3?? Start Yes Training? No Actual eedrate Input vector v c A D R D Tool geometry D Material HB Objective values F x F y F z Preparation o data or training / testing Training set-input vector Experiment plan Milling process Measured cutting orcess Fig. 6. Comparison o simulated and experimental resultant orce Adaptive sel-learning controller design or eedrate maximisation o machining process 473

6 Journal o Achievements in Materials and Manuacturing Engineering Volume 31 Issue 2 December Experimental equipment and data and acquisition data acquisition system system The data acquisition equipment consists o dynamometer, ixture and sotware module. The cutting orces were measured with a piezoelectric dynamometer (Kistler 9255) mounted between the workpiece and the machining table. The interace hardware module consists o a connecting plan block, analogue signal conditioning modules and a 16 channel A/D interace board (PC-MIO-16E-4). In the A/D board, the analogue signal will be transormed into a digital signal so that the LabVIEW sotware is able to read and receive the data. With this program, the three axis orce components can be obtained simultaneously, and can be displayed on the screen or urther analysis. The eedrate override percentage variable DNCFRO is available to the control system at a requency o 1 khz Communication between the control system and the CNC machine controller is accomplished over RS-232 protocol. 6. Experimental testing o o adaptive control adaptive system control system To examine the stability and robustness o the proposed control strategy, the system is irst analysed by simulations using LabVIEW s simulation package Simulink [14]. Then the system is veriied by two experiments on a CNC milling machine or Ck 45 and 16MnCrSi5 XM steel workpieces with variation o axial cutting depth (Experiment 1- prismatic workpiece; experiment 2- workpiece with irregular proile, see Table 1). Details o the experimental conditions and the dimensions o the workpiece are shown in Table 1. Feedrates or each cut are irst optimized o-line, and then machining runs are made with controller action. The irst test is conventional cutting with the constant eedrate (Test_A). In the second test, the proposed combined system was applied in milling to demonstrate its perormance (Test_B). Table 1. Plan o experiments: a) Cutting conditions or prismatic workpiece; b) Cutting conditions or irregular workpiece proile a) Experiment 1: Prismatic Workpiece Test_B Proposed adaptive control system b) Cutting conditions: eedrate:.8mm/zob, cutting speed: v = 8m/min, pre-programmed axial depth o cut A D = 2 mm, radial depth o cut R D = 4mm, F re = 28N, result: Figure 7a. Starting eedrate:.8mm/zob, allowable adjusting rate:.8.2mm/zob, cutting speed: v = 8m/min, axial depth o cut A D = 2 11mm, radial depth o cut: R D = 4mm, F re = 28N, result: Figure 7b. milling direction Experiment 2: Irregular workpiece proile A D =5 mm A D =3 mm A D =1.5 mm A D =3 mm A D =5 mm Test_A Constant eedrate Test_B Proposed adaptive control system Cutting conditions: eedrate: 3mm/s, spindle speed: 24min -1, axial depth o cut A D = 2 5mm, radial depth o cut: R D = 16mm, F re = 65N. Starting Feedrate: 2.5mm/s, allowable adjusting rate o ederate: mm/s, spindle speed: 24min -1, axial depth o cut A D = 2 5mm, radial depth o cut: R D = 16mm, F re = 65N, result: Figure: Research paper F. Cus, U. Zuperl, E. Kiker, M. Milelner

7 Analysis and modelling The parameters or adaptive control are the same as or the experiments in the conventional milling. Figure 7 is the response o the cutting orce and the eedrate when the cutting depth is changed (experiment-1). It shows the experimental result where the eedrate is adjusted on-line to maintain the maximal cutting orce at the desired value. The second experiment is machining o irregular workpiece consisting o ive straight cuts with dierent axial and radial depths o cut. The results o the second experiment using optimized eedrates and UNKS are presented in Figure Results and and discussion discussion Comparing the Figure 7a to Figure 7b, the cutting orce or the neural control milling system is maintained at about 25N, and the eedrate o the adaptive milling system is close to that o the conventional milling rom point C to point D. From point A to point C the eedrate o the adaptive milling system is higher than or the classical CNC system, so the milling eiciency o the adaptive milling is improved. The time analysis or conventional and adaptive control system has been curried out. By adaptive control system o time saving o 4% with one cut was reached. The complete machining requires 15 cuts; thus machining o a simple workpiece is shortened or 155 seconds. The second experiment with small and large step changes is run to test system stability over a range o cutting conditions. The system remains stable in all experiments, with little degradation in perormance. In the second experiment, the adaptive controller increases the eedrates to obtain peak orces close to 65N. The slower response o the neural control scheme is noticeable at the beginning o cut one and three. The results reached are in accordance with the objectives o researches, according to which the controlled cutting orce must not deviate rom the desired value or more than 1%. As compared to most o the existing end milling control systems [9, 1], the proposed adaptive system has the ollowing advantages: 1. the computational complexity o UNKS does not increase much with the complexity o process; 2. the learning ability o UNKS is more powerul than that o conventional adaptive controller; 3. UNKS has a generalisation capability [11]; 4. insensitive to changes in workpiece geometry, cutter geometry, and workpiece material; 5. cost-eicient and easy to implement; and 6. mathematically modeling-ree Fig. 7. Experiment-2; Machining o irregular proile by o-line optimizing o cutting conditions and adaptive adjusting o eedrate 1.25 Resultant cutting orce 1 F [kn].5 Fre [mm/s] Time [s] Fig. 8. Experiment-2; Machining o irregular proile by o-line optimizing o cutting conditions and adaptive adjusting o eedrate Adaptive sel-learning controller design or eedrate maximisation o machining process 475

8 Journal o Achievements in Materials and Manuacturing Engineering Volume 31 Issue 2 December Conclusions In this paper, a hybrid adaptive control algorithm that controls eedrate is proposed to regulate the cutting orce. On the basis o the cutting process modeling, o-line optimization and eed-orward neural control scheme (UNKS) the combined system or o-line optimization and adaptive adjustment o cutting parameters is built. This is an adaptive control system controlling the cutting orce and maintaining constant roughness o the surace being milled by digital adaptation o cutting parameters. In order to check the applicability o the adaptive control algorithm, cutting experiments were carried out under various cutting conditions, dierent tool diameters and dierent work materials. The results show that the developed adaptive control algorithm has good stability and excellent applicability behavior. Reerences [1] J. Balic, A new NC machine tool controller or step-bystep milling, International Journal o Advanced Manuacturing Technology 18 (21) [2] J. Balic, Optimization o cutting process by GA approach, Robotics and Computer-Integrated Manuacturing 19 (23) [3] C. Chen, M. Zhibin, An intelligent approach to nonconstant eed rate determination or high-perormance 2D CNC milling, International Journal o Manuacturing Technology and Management 9 (26) [4] F. Cus, U. Zuperl, E. Kiker, M. Milelner, Adaptive controllers design or eedrate maximization o machining, Journal o Materials Processing Technology (25) [5] F. Cus, U. Zuperl, E. Kiker, M. Milelner, Adaptive controller design or eedrate maximization o machining process, Journal o Achievements in Materials and Manuacturing Engineering 17 (26) [6] L.A. Dobrza ski, K. Go ombek, J. Kopac, M. Sokovic, Eect o depositing the hard surace coatings on properties o the selected cemented carbides and tool cermets, Journal o Materials Processing Technology (24) [7] L.A. Dobrza ski, A. liwa, W. Kwa ny, Employment o the inite element method or determining stresses in coatings obtained on high-speed steel with the PVD process, Journal o Materials Processing Technology (25) [8] W. Grzesik, J. Rech, T. Wanat, Surace integrity o hardened steel parts in hybrid machining operations, Journal o Achievements in Materials and Manuacturing Engineering 18 (26) [9] J. Kopac, Modern machining o die and mold tools, Proceedings o the 11 th Scientiic International Conerence Achievements in Mechanical and Materials Engineering AMME`22, Gliwice-Zakopane, [1] J. Kopac, Advanced tool materials or high-speed machining, Proceedings o the 12 th Scientiic International Conerence Achievements in Mechanical and Materials Engineering AMME'23, Gliwice-Zakopane, 23, [11] J. Kopac, Inluence o high speed cutting on the structure o machined high speed steel material, Proceedings o 11 th Scientiic International Conerence Contemporary Achievements in Mechanics, Manuacturing and Materials Science CAM3S'25, Gliwice-Zakopane, 25, 4-44 (CD-ROM). [12] Y. Liu, L. Zuo, C. Wang, Intelligent adaptive control in milling process, International Journal o Computer Integrated Manuacturing 12 (1999) [13] M. Sokovic, M. Cedilnik, J. Kopac, Use o 3D-scanning and reverse engineering by manuacturing o complex shapes, Proceedings o the 13 th Scientiic International Conerence Achievements in Mechanical and Materials Engineering AMME'25, Gliwice-Wis a, 25, [14] A. Stoic, J. Kopac, G. Cukor, Testing o machinability o 4CrMnMo7 steel using genetic algorithm, Proceedings o the 13 th Scientiic International Conerence Achievements in Mechanical and Materials Engineering AMME'25, Gliwice-Wis a, 25, [15] G. Stute, F.R. Goetz, Adaptive Control System or Variable Gain in ACC Systems, Proceedings o the Sixteenth International Machine Tool Design and Research Conerence, Manchester, England, 1995, [16] S. Zhang, A. Xing, L. Jianeng, F. Xiuli, Failure analysis on clamping bolt o milling cutter or high-speed machining, International Journal o Machining and Machinability o Materials 1 (26) [17] U. Zuperl, F. Cus, M. Milelner, Fuzzy control strategy or an adaptive orce control in end-milling, Journal o Materials Processing Technology (25) Research paper READING DIRECT: