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Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor
Materials Today: Proceedings xxx (xxxx) xxx
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Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor Varun Jain, Sharad K. Pradhan ⇑ Mechanical Engg. Department, NITTTR Bhopal, 462002, India
a r t i c l e
i n f o
Article history: Received 6 August 2019 Accepted 27 September 2019 Available online xxxx Keywords: Vibration measurement Arduino MPU6050 Ball nose end mill Milling machine condition monitoring Optimization
a b s t r a c t Optimization of parameters like tool path, cutter geometry, feed rate optimization, tool path and feed rate integration for sculptured surface machining has been an active research zone. The next step is to investigate the effect of vibration in ball end mill tool during milling machining of the sculptured surface. Milling machining process which is characterized by interrupted cutting, the problems of vibration occur in the machine-tool-work piece fixation device. In this work, experimental measurement of time domain signals amplitude of vibration (Acceleration) using Arduino Uno (microcontroller board) with MPU6050 (MEMS accelerometer) during machining of the surface of a selected sculpture is performed. These signals are then used to calculate the frequency of acquired signals using NI DIAdem software. The study shows the optimum values of different parameters under consideration. Such an analysis can also be helpful in fault diagnosis of CNC milling machine parts of sculptured surfaces. The investigations indication that feed is the most governing factor affecting the surface texture, and the cutting speed is the key factor affecting tool vibrations amplitudes. The results of experimental investigations are in agreement with the mathematical model generated using. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
1. Introduction Yang et al. [1] has analysed Instantaneous cutting forces on ball end milling and developed the cutting force model using orthogonal machining data. Lee et al. [2] predicted cutting forces in 3D using the geometry and kinematics of the ball nose end milling. Feng et al. [3] developed the cutting force mathematical model and analysed the cutter run out to estimate the empirical parameters included in the test model formulation. Choi et al. [4] generated the 2D cutterlocation data to minimize the problem of cutter location in any process optimization. After the prediction of cutting forces with a different method of simulation, different empirical formula and experiments reported in [1,2,7,8,10]. Lee [5] presented the geometry analysis and algorithms of permissible tool orientation surface machining for 5-axis. Jun et al. [6] proposed the algorithm for interference handling which can deal with any type of surfaces but the algorithm cannot protect the original part surface from gouging it simply protects the tangent plane model. Lee [7,8] studied the ⇑ Corresponding author. E-mail address: [email protected] (S.K. Pradhan).
non-isoparametric 5-axis tool path planning for oblique cutting. Yang et al. [9] carried out the manufacturability analysis using visible direction and visibility cone combined with convex hull computation algorithm. This analysis can help in the programming of cutter path generation and computer-aided planning. Kim et al. [10] mapped the different methods of cutting force of machining sculptured surface using Z-map. Other have also tried the mapping using different models as non-isoparametric [7,8], theoretical model [11], linear decomposition method [12], (Z-buffer approach) [15] and mechanistic model [15,16]. Wang et al. [12] found out shearing cutting constants in ball end milling using explicit analysis and linear decomposition of local elements of cutting forces. Chiou et al. [13] generated 5 axis tool path and optimal cutting direction for the sculptured surface. Developed, the new machining potential filed (MPF) approach. Ip et al. [14] developed a fuzzy logic approach for optimal material removal rate (MRR) which helps in calculating the individual parameter of machining. Guzel et al. [17] used AI7039 as the material to show the increase in efficiency in sculptured surface machining using the force system model. Shamoto et al. [18] proposed the new method of vibration cutting for sculptured mirror surface. 3 DOF ultrasonic vibration tool was devel-
https://doi.org/10.1016/j.matpr.2019.09.222 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Fig. 1. Experimental setup.
Fig. 3. The physical connection of Arduino UNO and MPU6050 sensor.
Fig. 2. Placement of sensor on CNC machine.
oped to generate arbitrary ultrasonic elliptical vibration in 3D. Hardened die steel was used as the test material. Lacalle et al. [19] used the ball burnishing process to improve the form quality of the final tool (moulds and dies). Fontaine et al. [20] investigated the wavelike form of machining. He used thermo-mechanical modelling for oblique cutting. Arizmendi et al. [21] present the experimental validation of prediction of the topography in ball-end milling surfaces. While working on the vibration of ball nose end mill tool it becomes very necessary to know the devices available to measure the physical quantity such as vibration. In the literature survey, it was found that many costly instruments are available in the market which is not easily available. Chaurasiya [22] shows the recent trends of vibration measuring sensors and also listed the working of different sensors. Ozturk et al. [23] showed the dynamics and stability of 5 axis milling to avoid the problems, frequency and used time domain
Fig. 4. Developed Vibration sensor and National Instruments vibration sensor calibration.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Fig. 5. Impact graph generated by MPU 6050.
Fig. 6. Impact graph generated By NI device.
Table 1 Optimization through ANOVA method. Performance parameter
Ball Dia. (mm)
Percentage contribution of ball dia.
Speed RPM
Percentage contribution of speed
DOC (mm)
Percentage contribution of DOC
Feed (mm/rev)
Percentage contribution of Feed
Vibration amplitude SF MRR
5 5 4
52.775 93.394 50.779
5000 6000 3500
21.241 4.055 12.812
0.040 0.035 0.040
6.247 0.377 21.942
1200 1500 1500
19.737 2.174 14.467
models for investigation of stability. Mahesh et al. [24] combined the two methods fuzzy logic and Taguchi method for maximizing the MRR and the optimization surface roughness. The material used by AI 7075 T6 aerospace aluminium alloy. Kuram et al. [25] have applied the grey relational study on micro-milling of AI7075 material with multi-parameter optimization. Chen et al. [26] used the P20 die steel as the experiment material and showed that the surface inclination angle had a non-repetitive effect on cutting forces values. Wojciechowski et al. [27] found out that a = 0° gives the highest mean force results. From the above literature sources, we found that for vibration the main problem during the ball end milling surface is caused due to
the tool interaction with workpiece material which leads to the high cutting forces, poor finish. It is seen that the reported ball end milling do not focus on process dynamics and manhined surface roughness. It is unavoidable to machine the complex surfaces that have a variable curvature, which might cause a decline in surface quality within the actual cutting method. Tool and Spindle vibration amplitude measurements are of huge importance in the development of high-speed milling. Measurements of vibrations amplitude and cutting forces on the still spindle head are used widely procedure. The milling machine vibration results depend on relative movement between the tool and workpiece. With caution to measure vibration amplitude on the rotating tool as close as possible.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Table 2 Input and output process variables and signal to noise ratio. Exp No
In-Put Process Variables Ball Dia (mm)
Speed (RPM)
DOC (mm)
Feed (mm/ rev)
FFT (STB) m (Z direction)
SF (STB)
MRR (LTB) g/ sec
s/n ratio for FFT
1 2 3 4 5 6 7 8 9
4 4 4 5 5 5 6 6 6
3500 5000 6000 3500 5000 6000 3500 5000 6000
0.030 0.035 0.040 0.035 0.040 0.030 0.040 0.030 0.035
1000 1200 1500 1500 1000 1200 1200 1500 1000
0.1593435 0.0113540 0.0124492 0.0381367 0.0207976 0.0116834 0.0283841 0.0712646 0.1171535
4.309 3.924 3.209 3.495 4.527 3.346 9.451 9.215 8.366
0.2343 0.2534 0.2849 0.2486 0.2597 0.2362 0.2777 0.2373 0.2358
15.95331 38.897 38.09716 28.37313 33.63974 38.64864 30.9385 22.94252 18.6249
Out Put Factors
Signal to Noise Ratio s/n ration for SF 12.68753 11.87458 10.12739 10.86894 13.11621 10.49052 19.50956 19.28991 18.45036
s/n ratio for MRR 12.6048937 11.9239343 10.9050993 12.0909758 11.7105111 12.5344354 11.1297469 12.4950436 12.5501423
Fig. 7. Best fit for Linear + Interaction RSM for the Surface finish.
Fig. 8. Graphical representation of Response Surface Method (RSM) optimization Result.
This paper focuses on the utilization of the MPU6050 (MEMS accelerometer sensor) for measuring the vibration of the milling process is demonstrated. Therefore, the aim of this study concen-
trates on the optimum choice of milling parameters for sculptured surface machining so as to attenuate the vibrations and cutting forces, consequently minimizing the machined surface roughness.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Fig. 9. Graphical representation of ANOVA (Analysis of Variance) optimization Result.
Fig. 10. Workpiece at three stages a) Before machining b) After Roughing c) After Finishing.
2. Experimental setup In present work, (RSM) response surface methodology is used to develop the mathematical models of tool vibration, surface finish (SF), and tool wear. The main objective of developing mathematical models with respect to machining responses and their factors is to estimate the optimized values of the machining parameters. After identification of process and performance parameters, the fractional factorial design is implemented to establish a Taguchi orthogonal array (OA) L9 and experiments are conducted on CNC milling machine as per the ball mill tool path generated using manufacturing simulation module of Unigraphics UG-NX CADM software. L9 design involves the numerical elimination of minor parameters, thus reducing experimental runs without losing useful information. The measurement of the magnitude of vibrations during milling was done using MUP6050 accelerometer. The selection
of the variables as input parameters was done on the basis of their significant influence on the tool and the surface finish. Four process parameters which have dominating influence on the milling of sculptured surface are Feed, Cutter diameter, Speed, and DOC (Depth of cut). Three levels of each input parameter were selected on the basis of machine capabilities and a literature survey Fig. 1. A DECKEL MOHA DMU 60 Mono Block was used in the experiment. AISI 304N stainless steel is used as the test material for the milling operation of sculpture surface. The selection of this material is based on the wide-ranging use of SS material in industry for making dies. The hardness of 304N SS is 25–32 HRC. The work piece is of 50 mm diameter and 10 mm thickness. Based on literature survey and cutting tool materials catalogues, either high-speed steel (HSS) (wrought or sintered) or cemented carbide tools can be used for machining stainless steels. Selected tool material is Cemented Carbide for milling of selected work piece
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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material. In this experimental work Fig. 10, End mill of 5 mm dia. is used in the roughing process and Ball nose end mill tool of 4 mm, 5 mm, 6 mm diameters are used for finishing the operation. The material of the ball nose end mill tool is Cemented Carbide. Vibrations are measured only during the finishing operation. In addition, a low-cost accelerometer was developed and calibrated for the 5 axis milling machine using MPU6050 and Arduino Uno as shown in Fig. 3. The MPU accelerometer 6050 is a 6 DOF or a six-axis IMU sensor. The sensor gives six values as output. Three values from the accelerometer and other for the gyroscope. Arduino Uno was used as the DAQ (Data Acquisition System) for the sensor. MPU 6050 sensor was calibrated (Figs. 4–6) to collect the vibrations during machining in the form of magnitude of Acceleration (m/s2). Hence, it measured the resulting tool vibration (Accelerometer) in the feed ( Z direction) and radial directions. The accelerometer was mounted on the small acrylic sheet and the acrylic sheet was mounted near the tool on the workpiece using C-clamp (Fig. 2) Arduino Uno is used as the DAQ (Data Acquisition) system and controller board for the sensor. Program code is loaded on the Arduino Uno board which control input and output of the sensor. Connection of Arduino UNO board and MPU 6050 sensor is shown in Fig. 3. The output of the sensor was collected using PuTTY software and all the data collected by the sensor was directly stored on the computer attached to it.
metical means roughness (Ra) was measured by surface roughness tester (Mitutoyo SJ-201) after milling experiments. The sampling length was adjusted to 4 mm. Two half was measured separately and the average of two measurement values of surface roughness was taken as the final value for further calculation. RSM and ANOVA methods are used to perform the optimization of selected input parameters. 3.1. Response surface method (RSM) In present work custom response surface method (RSM) has been used for the analysis of data and determination of governing equations because the number of experiments is as per L9 orthogonal array. The final mathematical equations in terms of inputs and responses that foretell the result with acceptable accuracy have been obtained from MINITAB 18. Optimum comparative graph for three different outputs are shown in Figs. 8 and 9. Following Regression Equation in uncoded units obtained from RSM. 3.1.1. For vibration amplitude s/n ratio for FFT = 398.0 + 56.38 Ball Dia (mm) + 0.01989 Speed (RPM) 3096 DOC (mm) + 0.4556 Feed (mm/rev) 5.978 Ball Dia (mm) * Ball Dia (mm) 0.000002 Speed (RPM)*Speed (RPM) + 56199 DOC (mm)*DOC (mm) 0.000177 Feed (mm/rev) * Feed (mm/rev).
3. Experimental design and optimization To conduct CNC milling operation spindle speed, feed and depth of cut and ball end mill dia. are selected as input process parameters. An L9 Taguchi orthogonal array (fractional factorial design) for four factorials and there three levels (Table 1) used to conduct the machining operation appling CAM software. L9 design statistical eliminates the minor parameters, thus reducing experimental runs without losing the useful information (see Table 2). The experimental work consisted of three performance parameters viz. vibration measurement, material removal rate and surface roughness measurement. The experiment was performed with three ball end mill high-speed tool under dry conditions (without coolant). Accelerometer (MPU-6050 sensor) along with Arduino UNO microcontroller (Fig. 2) was placed near the workpiece using Cclamp in the machine which provided vibration amplitudes (acceleration) of cutting tool in x, y, and z-axes (as shown in Fig. 11). Arith-
3.1.2. For surface roughness s/n ration for SF = 10–30.8 Ball Dia (mm) + 0.0044 Speed (RPM) + 2880 DOC (mm) + 0.0126 Feed (mm/rev) + 0.00392 Ball Dia (mm) * Speed (RPM) + 167 Ball Dia (mm) * DOC (mm) 0.663 Speed (RPM)*DOC (mm). 3.1.3. For material removal rate s/n ratio for MRR = 12.09 + 1.690 Ball Dia (mm) + 0.001035 Speed (RPM) 442 DOC (mm) 0.000036 Feed (mm/rev) 0.000619 Ball Dia (mm) * Speed (RPM) + 39.4 Ball Dia (mm)*DOC (mm) + 0.06049 Speed (RPM)*DOC (mm). Various Combination of equations like Linear, Linear + Interaction, Linear + Squares, and Full Quadratic have been used check the usefulness of these mathematical equations so as to select the mathematical model which closely foretell the behaviour of the process input parameters on performance response. The research
Fig. 11. Vibration amplitude for the machine parameters (Dia = 5, N = 5000, DOC = 0.040, f = 1000).
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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concludes that the following three mathematical models in terms of actual factors give the best fit. As a sample graph, Fig. 7 shows the best fit for Linear + Interaction RSM for the Surface finish. As a result of the study, the optimal graph was generated by two techniques RSM and ANOVA. Figs. 8 and 9 represent the optimal value. Full Quadratic RSM for time response vibration amplitude (m/ s2). Linear + Interaction RSM for the Surface finish Linear + Interaction RSM for Material Removal Rate Fig. 8 shows the optimum value obtained through RSM to conduct the machining operation. Value for different inputs are Ball dia. = 6 mm, Speed = 3500 rpm, DOC = 0.030 mm, Feed = 1500 mm. Fig. 9 show the optimum value obtained through ANOVA to conduct the machining operation. Value for different inputs are Ball dia. = 6 mm, Speed = 3500 rpm, DOC = 0.030 mm, Feed = 1500 mm. 4. Conclusion Through this research attempt, it is established that a Low-Cost vibration measuring setup comprises of MPU-6050 sensor and
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Arduino UNO can be used for measurement of milling machine vibrations during sculpture machining through Ball mill. From SN ratio for time response amplitude of vibration, it can be concluded that the best combination for the optimum value of time response amplitude of vibration are (Ball dia. = 5 mm, Speed = 5000, DOC = 0.040 and feed = 1200), for Surface Finish the optimum values are (Ball dia. = 5 mm, Speed = 6000, DOC = 0.035 and feed = 1500), for Material Removal Rate the optimum values are (Ball dia. = 4 mm, Speed = 3500, DOC = 0.040 and feed = 1500) and finally from Taguchi method the optimum values for all three performance parameters are (Ball dia. = 5 mm, Speed = 5000, DOC = 0.040 and feed = 1500). Using ANOVA the optimum values are (Ball dia. = 6, Speed = 3500, DOC = 0.030, Feed = 1500). The optimum values obtained from RSM through are (Ball dia = 6, Speed = 3500, DOC = 0.030, Feed = 1500). Comparing optimum values obtained from different optimization techniques it is clear that they are in very good match with each other and the predicted model can be utilized for performing sculpture machining of same material with other geometries and sizes also to produce quality components.
Appendix A Graphical abstract
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Appendix B 9 workpiece after machining
Appendix C Surface roughness test certificate
Fig. Surface roughness testing on Work piece using Mitutoyo surface tester Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Appendix D Arduino code for developed sensor #include #include unsigned long time; #define MPU6050_AFS_SEL_16G MPU6050_AFS_SEL_3; #include const int MPU_addr = 0x68; // I2C address of the MPU-6050 double AcX,AcY,AcZ, Xms2,Yms2,Zms2; void setup() { Wire.begin(); Wire.beginTransmission(MPU_addr); Wire.write(0x6B); // PWR_MGMT_1 register Wire.write(0); // set to zero (wakes up the MPU-6050) Wire.endTransmission(true); Serial.begin(115200); } void loop() { Wire.beginTransmission(MPU_addr); Wire.write(0x3B); // starting with register 0x3B (ACCEL_XOUT_H) Wire.endTransmission(false); Wire.requestFrom(MPU_addr,14,true); // request a total of 14 registers AcX = Wire.read() 8|Wire.read(); // 0x3B (ACCEL_XOUT_H) & 0x3C (ACCEL_XOUT_L) AcY = Wire.read() 8|Wire.read(); // 0x3D (ACCEL_YOUT_H) & 0x3E (ACCEL_YOUT_L) AcZ = Wire.read() 8|Wire.read(); // 0x3F (ACCEL_ZOUT_H) & 0x40 (ACCEL_ZOUT_L) Xms2 = (AcX/16384)*9.81; Yms2 = (AcY/16384)*9.81; Zms2 = (AcZ/16384)*9.81; Serial.print(‘‘Time: ”);time = millis();Serial.print(time); Serial.print(‘‘ , AcX = ”); Serial.print(Xms2); Serial.print(‘‘ , AcY = ”); Serial.print(Yms2); Serial.print(‘‘ , AcZ = ”); Serial.print(Zms2); Serial.println(); }
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Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor Varun Jain, Sharad K. Pradhan ⇑ Mechanical Engg. Department, NITTTR Bhopal, 462002, India
a r t i c l e
i n f o
Article history: Received 6 August 2019 Accepted 27 September 2019 Available online xxxx Keywords: Vibration measurement Arduino MPU6050 Ball nose end mill Milling machine condition monitoring Optimization
a b s t r a c t Optimization of parameters like tool path, cutter geometry, feed rate optimization, tool path and feed rate integration for sculptured surface machining has been an active research zone. The next step is to investigate the effect of vibration in ball end mill tool during milling machining of the sculptured surface. Milling machining process which is characterized by interrupted cutting, the problems of vibration occur in the machine-tool-work piece fixation device. In this work, experimental measurement of time domain signals amplitude of vibration (Acceleration) using Arduino Uno (microcontroller board) with MPU6050 (MEMS accelerometer) during machining of the surface of a selected sculpture is performed. These signals are then used to calculate the frequency of acquired signals using NI DIAdem software. The study shows the optimum values of different parameters under consideration. Such an analysis can also be helpful in fault diagnosis of CNC milling machine parts of sculptured surfaces. The investigations indication that feed is the most governing factor affecting the surface texture, and the cutting speed is the key factor affecting tool vibrations amplitudes. The results of experimental investigations are in agreement with the mathematical model generated using. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
1. Introduction Yang et al. [1] has analysed Instantaneous cutting forces on ball end milling and developed the cutting force model using orthogonal machining data. Lee et al. [2] predicted cutting forces in 3D using the geometry and kinematics of the ball nose end milling. Feng et al. [3] developed the cutting force mathematical model and analysed the cutter run out to estimate the empirical parameters included in the test model formulation. Choi et al. [4] generated the 2D cutterlocation data to minimize the problem of cutter location in any process optimization. After the prediction of cutting forces with a different method of simulation, different empirical formula and experiments reported in [1,2,7,8,10]. Lee [5] presented the geometry analysis and algorithms of permissible tool orientation surface machining for 5-axis. Jun et al. [6] proposed the algorithm for interference handling which can deal with any type of surfaces but the algorithm cannot protect the original part surface from gouging it simply protects the tangent plane model. Lee [7,8] studied the ⇑ Corresponding author. E-mail address: [email protected] (S.K. Pradhan).
non-isoparametric 5-axis tool path planning for oblique cutting. Yang et al. [9] carried out the manufacturability analysis using visible direction and visibility cone combined with convex hull computation algorithm. This analysis can help in the programming of cutter path generation and computer-aided planning. Kim et al. [10] mapped the different methods of cutting force of machining sculptured surface using Z-map. Other have also tried the mapping using different models as non-isoparametric [7,8], theoretical model [11], linear decomposition method [12], (Z-buffer approach) [15] and mechanistic model [15,16]. Wang et al. [12] found out shearing cutting constants in ball end milling using explicit analysis and linear decomposition of local elements of cutting forces. Chiou et al. [13] generated 5 axis tool path and optimal cutting direction for the sculptured surface. Developed, the new machining potential filed (MPF) approach. Ip et al. [14] developed a fuzzy logic approach for optimal material removal rate (MRR) which helps in calculating the individual parameter of machining. Guzel et al. [17] used AI7039 as the material to show the increase in efficiency in sculptured surface machining using the force system model. Shamoto et al. [18] proposed the new method of vibration cutting for sculptured mirror surface. 3 DOF ultrasonic vibration tool was devel-
https://doi.org/10.1016/j.matpr.2019.09.222 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Fig. 1. Experimental setup.
Fig. 3. The physical connection of Arduino UNO and MPU6050 sensor.
Fig. 2. Placement of sensor on CNC machine.
oped to generate arbitrary ultrasonic elliptical vibration in 3D. Hardened die steel was used as the test material. Lacalle et al. [19] used the ball burnishing process to improve the form quality of the final tool (moulds and dies). Fontaine et al. [20] investigated the wavelike form of machining. He used thermo-mechanical modelling for oblique cutting. Arizmendi et al. [21] present the experimental validation of prediction of the topography in ball-end milling surfaces. While working on the vibration of ball nose end mill tool it becomes very necessary to know the devices available to measure the physical quantity such as vibration. In the literature survey, it was found that many costly instruments are available in the market which is not easily available. Chaurasiya [22] shows the recent trends of vibration measuring sensors and also listed the working of different sensors. Ozturk et al. [23] showed the dynamics and stability of 5 axis milling to avoid the problems, frequency and used time domain
Fig. 4. Developed Vibration sensor and National Instruments vibration sensor calibration.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Fig. 5. Impact graph generated by MPU 6050.
Fig. 6. Impact graph generated By NI device.
Table 1 Optimization through ANOVA method. Performance parameter
Ball Dia. (mm)
Percentage contribution of ball dia.
Speed RPM
Percentage contribution of speed
DOC (mm)
Percentage contribution of DOC
Feed (mm/rev)
Percentage contribution of Feed
Vibration amplitude SF MRR
5 5 4
52.775 93.394 50.779
5000 6000 3500
21.241 4.055 12.812
0.040 0.035 0.040
6.247 0.377 21.942
1200 1500 1500
19.737 2.174 14.467
models for investigation of stability. Mahesh et al. [24] combined the two methods fuzzy logic and Taguchi method for maximizing the MRR and the optimization surface roughness. The material used by AI 7075 T6 aerospace aluminium alloy. Kuram et al. [25] have applied the grey relational study on micro-milling of AI7075 material with multi-parameter optimization. Chen et al. [26] used the P20 die steel as the experiment material and showed that the surface inclination angle had a non-repetitive effect on cutting forces values. Wojciechowski et al. [27] found out that a = 0° gives the highest mean force results. From the above literature sources, we found that for vibration the main problem during the ball end milling surface is caused due to
the tool interaction with workpiece material which leads to the high cutting forces, poor finish. It is seen that the reported ball end milling do not focus on process dynamics and manhined surface roughness. It is unavoidable to machine the complex surfaces that have a variable curvature, which might cause a decline in surface quality within the actual cutting method. Tool and Spindle vibration amplitude measurements are of huge importance in the development of high-speed milling. Measurements of vibrations amplitude and cutting forces on the still spindle head are used widely procedure. The milling machine vibration results depend on relative movement between the tool and workpiece. With caution to measure vibration amplitude on the rotating tool as close as possible.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Table 2 Input and output process variables and signal to noise ratio. Exp No
In-Put Process Variables Ball Dia (mm)
Speed (RPM)
DOC (mm)
Feed (mm/ rev)
FFT (STB) m (Z direction)
SF (STB)
MRR (LTB) g/ sec
s/n ratio for FFT
1 2 3 4 5 6 7 8 9
4 4 4 5 5 5 6 6 6
3500 5000 6000 3500 5000 6000 3500 5000 6000
0.030 0.035 0.040 0.035 0.040 0.030 0.040 0.030 0.035
1000 1200 1500 1500 1000 1200 1200 1500 1000
0.1593435 0.0113540 0.0124492 0.0381367 0.0207976 0.0116834 0.0283841 0.0712646 0.1171535
4.309 3.924 3.209 3.495 4.527 3.346 9.451 9.215 8.366
0.2343 0.2534 0.2849 0.2486 0.2597 0.2362 0.2777 0.2373 0.2358
15.95331 38.897 38.09716 28.37313 33.63974 38.64864 30.9385 22.94252 18.6249
Out Put Factors
Signal to Noise Ratio s/n ration for SF 12.68753 11.87458 10.12739 10.86894 13.11621 10.49052 19.50956 19.28991 18.45036
s/n ratio for MRR 12.6048937 11.9239343 10.9050993 12.0909758 11.7105111 12.5344354 11.1297469 12.4950436 12.5501423
Fig. 7. Best fit for Linear + Interaction RSM for the Surface finish.
Fig. 8. Graphical representation of Response Surface Method (RSM) optimization Result.
This paper focuses on the utilization of the MPU6050 (MEMS accelerometer sensor) for measuring the vibration of the milling process is demonstrated. Therefore, the aim of this study concen-
trates on the optimum choice of milling parameters for sculptured surface machining so as to attenuate the vibrations and cutting forces, consequently minimizing the machined surface roughness.
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Fig. 9. Graphical representation of ANOVA (Analysis of Variance) optimization Result.
Fig. 10. Workpiece at three stages a) Before machining b) After Roughing c) After Finishing.
2. Experimental setup In present work, (RSM) response surface methodology is used to develop the mathematical models of tool vibration, surface finish (SF), and tool wear. The main objective of developing mathematical models with respect to machining responses and their factors is to estimate the optimized values of the machining parameters. After identification of process and performance parameters, the fractional factorial design is implemented to establish a Taguchi orthogonal array (OA) L9 and experiments are conducted on CNC milling machine as per the ball mill tool path generated using manufacturing simulation module of Unigraphics UG-NX CADM software. L9 design involves the numerical elimination of minor parameters, thus reducing experimental runs without losing useful information. The measurement of the magnitude of vibrations during milling was done using MUP6050 accelerometer. The selection
of the variables as input parameters was done on the basis of their significant influence on the tool and the surface finish. Four process parameters which have dominating influence on the milling of sculptured surface are Feed, Cutter diameter, Speed, and DOC (Depth of cut). Three levels of each input parameter were selected on the basis of machine capabilities and a literature survey Fig. 1. A DECKEL MOHA DMU 60 Mono Block was used in the experiment. AISI 304N stainless steel is used as the test material for the milling operation of sculpture surface. The selection of this material is based on the wide-ranging use of SS material in industry for making dies. The hardness of 304N SS is 25–32 HRC. The work piece is of 50 mm diameter and 10 mm thickness. Based on literature survey and cutting tool materials catalogues, either high-speed steel (HSS) (wrought or sintered) or cemented carbide tools can be used for machining stainless steels. Selected tool material is Cemented Carbide for milling of selected work piece
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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material. In this experimental work Fig. 10, End mill of 5 mm dia. is used in the roughing process and Ball nose end mill tool of 4 mm, 5 mm, 6 mm diameters are used for finishing the operation. The material of the ball nose end mill tool is Cemented Carbide. Vibrations are measured only during the finishing operation. In addition, a low-cost accelerometer was developed and calibrated for the 5 axis milling machine using MPU6050 and Arduino Uno as shown in Fig. 3. The MPU accelerometer 6050 is a 6 DOF or a six-axis IMU sensor. The sensor gives six values as output. Three values from the accelerometer and other for the gyroscope. Arduino Uno was used as the DAQ (Data Acquisition System) for the sensor. MPU 6050 sensor was calibrated (Figs. 4–6) to collect the vibrations during machining in the form of magnitude of Acceleration (m/s2). Hence, it measured the resulting tool vibration (Accelerometer) in the feed ( Z direction) and radial directions. The accelerometer was mounted on the small acrylic sheet and the acrylic sheet was mounted near the tool on the workpiece using C-clamp (Fig. 2) Arduino Uno is used as the DAQ (Data Acquisition) system and controller board for the sensor. Program code is loaded on the Arduino Uno board which control input and output of the sensor. Connection of Arduino UNO board and MPU 6050 sensor is shown in Fig. 3. The output of the sensor was collected using PuTTY software and all the data collected by the sensor was directly stored on the computer attached to it.
metical means roughness (Ra) was measured by surface roughness tester (Mitutoyo SJ-201) after milling experiments. The sampling length was adjusted to 4 mm. Two half was measured separately and the average of two measurement values of surface roughness was taken as the final value for further calculation. RSM and ANOVA methods are used to perform the optimization of selected input parameters. 3.1. Response surface method (RSM) In present work custom response surface method (RSM) has been used for the analysis of data and determination of governing equations because the number of experiments is as per L9 orthogonal array. The final mathematical equations in terms of inputs and responses that foretell the result with acceptable accuracy have been obtained from MINITAB 18. Optimum comparative graph for three different outputs are shown in Figs. 8 and 9. Following Regression Equation in uncoded units obtained from RSM. 3.1.1. For vibration amplitude s/n ratio for FFT = 398.0 + 56.38 Ball Dia (mm) + 0.01989 Speed (RPM) 3096 DOC (mm) + 0.4556 Feed (mm/rev) 5.978 Ball Dia (mm) * Ball Dia (mm) 0.000002 Speed (RPM)*Speed (RPM) + 56199 DOC (mm)*DOC (mm) 0.000177 Feed (mm/rev) * Feed (mm/rev).
3. Experimental design and optimization To conduct CNC milling operation spindle speed, feed and depth of cut and ball end mill dia. are selected as input process parameters. An L9 Taguchi orthogonal array (fractional factorial design) for four factorials and there three levels (Table 1) used to conduct the machining operation appling CAM software. L9 design statistical eliminates the minor parameters, thus reducing experimental runs without losing the useful information (see Table 2). The experimental work consisted of three performance parameters viz. vibration measurement, material removal rate and surface roughness measurement. The experiment was performed with three ball end mill high-speed tool under dry conditions (without coolant). Accelerometer (MPU-6050 sensor) along with Arduino UNO microcontroller (Fig. 2) was placed near the workpiece using Cclamp in the machine which provided vibration amplitudes (acceleration) of cutting tool in x, y, and z-axes (as shown in Fig. 11). Arith-
3.1.2. For surface roughness s/n ration for SF = 10–30.8 Ball Dia (mm) + 0.0044 Speed (RPM) + 2880 DOC (mm) + 0.0126 Feed (mm/rev) + 0.00392 Ball Dia (mm) * Speed (RPM) + 167 Ball Dia (mm) * DOC (mm) 0.663 Speed (RPM)*DOC (mm). 3.1.3. For material removal rate s/n ratio for MRR = 12.09 + 1.690 Ball Dia (mm) + 0.001035 Speed (RPM) 442 DOC (mm) 0.000036 Feed (mm/rev) 0.000619 Ball Dia (mm) * Speed (RPM) + 39.4 Ball Dia (mm)*DOC (mm) + 0.06049 Speed (RPM)*DOC (mm). Various Combination of equations like Linear, Linear + Interaction, Linear + Squares, and Full Quadratic have been used check the usefulness of these mathematical equations so as to select the mathematical model which closely foretell the behaviour of the process input parameters on performance response. The research
Fig. 11. Vibration amplitude for the machine parameters (Dia = 5, N = 5000, DOC = 0.040, f = 1000).
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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concludes that the following three mathematical models in terms of actual factors give the best fit. As a sample graph, Fig. 7 shows the best fit for Linear + Interaction RSM for the Surface finish. As a result of the study, the optimal graph was generated by two techniques RSM and ANOVA. Figs. 8 and 9 represent the optimal value. Full Quadratic RSM for time response vibration amplitude (m/ s2). Linear + Interaction RSM for the Surface finish Linear + Interaction RSM for Material Removal Rate Fig. 8 shows the optimum value obtained through RSM to conduct the machining operation. Value for different inputs are Ball dia. = 6 mm, Speed = 3500 rpm, DOC = 0.030 mm, Feed = 1500 mm. Fig. 9 show the optimum value obtained through ANOVA to conduct the machining operation. Value for different inputs are Ball dia. = 6 mm, Speed = 3500 rpm, DOC = 0.030 mm, Feed = 1500 mm. 4. Conclusion Through this research attempt, it is established that a Low-Cost vibration measuring setup comprises of MPU-6050 sensor and
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Arduino UNO can be used for measurement of milling machine vibrations during sculpture machining through Ball mill. From SN ratio for time response amplitude of vibration, it can be concluded that the best combination for the optimum value of time response amplitude of vibration are (Ball dia. = 5 mm, Speed = 5000, DOC = 0.040 and feed = 1200), for Surface Finish the optimum values are (Ball dia. = 5 mm, Speed = 6000, DOC = 0.035 and feed = 1500), for Material Removal Rate the optimum values are (Ball dia. = 4 mm, Speed = 3500, DOC = 0.040 and feed = 1500) and finally from Taguchi method the optimum values for all three performance parameters are (Ball dia. = 5 mm, Speed = 5000, DOC = 0.040 and feed = 1500). Using ANOVA the optimum values are (Ball dia. = 6, Speed = 3500, DOC = 0.030, Feed = 1500). The optimum values obtained from RSM through are (Ball dia = 6, Speed = 3500, DOC = 0.030, Feed = 1500). Comparing optimum values obtained from different optimization techniques it is clear that they are in very good match with each other and the predicted model can be utilized for performing sculpture machining of same material with other geometries and sizes also to produce quality components.
Appendix A Graphical abstract
Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Appendix B 9 workpiece after machining
Appendix C Surface roughness test certificate
Fig. Surface roughness testing on Work piece using Mitutoyo surface tester Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222
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Appendix D Arduino code for developed sensor #include
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Please cite this article as: V. Jain and S. K. Pradhan, Vibration monitoring of ball nose end mill tool during milling of sculptured surfaces using MUP6050 sensor, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.222