Investigation of progressive tool wear for determining of optimized
machining parameters in turning
Mustafa Kuntog˘lu
⇑, Hacı Sag˘lam
Selcuk University, Technology Faculty, Mechanical Engineering Department, Konya, Turkey
a r t i c l e i n f o
Article history:Received 11 October 2017
Received in revised form 6 March 2019 Accepted 3 April 2019
Available online 11 April 2019 Keywords: Anova Optimization Tool wear Tool breakage Taguchi method
a b s t r a c t
On-line monitoring of tool wear and tool breakage are very important to reduce production costs through the optimization of machining parameters. Increasing cutting forces affect workpiece quality and tool condition that is the ultimate aim of production line and progressive tool wear which can trigger the tool breakage. Taguchi method is extensively used for determining number of experiment while variance analysis (ANOVA) deals with which parameter/s is/are effective on output. This study contains experi-ments and optimization processes during turning of AISI 1050 material with 3 input parameters (cutting speed, feed rate, tool tip) using Taguchi method. In order to determine the condition of the cutting tool, measurement of tangential cutting force and acoustic emission (AE) were carried out during metal removing. ANOVA results showed that cutting speed is the most effective about %45 and tool tip is the second about %35 on tool wear. On the other hand, the effect of feed rate on tangential cutting force (%88) and cutting speed on AE (%80) is remarkably higher than the other two parameters. In order to obtain the minimum tool wear value, the optimum cutting parameters have been selected as v1= 135 m/min, f2= 0,214 mm/rev, T2= P25. By implemented sensor system tool breakage can be
suc-cessfully detected and used for producing high quality materials with low costs.
Ó 2019 Elsevier Ltd. All rights reserved.
1. Introduction
Tool wear is a natural process that develops on cutting tool dur-ing machindur-ing operations because of mechanical, chemical and thermal loads. Several wear mechanisms appear in cutting zone due to this external effects i.e. flank wear (VB) and crater wear which were highly investigated in the past studies. The considered two types of wear as a criteria for tool life provides predictable and reliable information. Flank wear can be used as first tool life crite-ria[1]and will be the main subject investigated in this study.
In the abrasive wear mechanism, the abrasive particles rup-tured from workpiece cause a friction effect in the area between tool and workpiece [2]. The occurred mechanism triggers flank wear development which develops on the clearance face of the cut-ting tool[3]. According to Diniz and Oliveira[4], the reason of flank wear is abrasive and adhesive wear mechanisms for turning of AISI 1045 steel. Increasing tool wear can transform into tool breakage without an on-line assistance of monitoring system. Tool breakage may cause a catastrophic effect on tool holder, or workpiece. The signs of tool failure can be seen as a result of increasing cutting
forces and power consumption, reducing surface quality and dimensional accuracy. When the product quality is considered as the main goal of the manufacturing then tool wear plays an impor-tant role.
It is required that progressive tool wear should be monitored to keep the wear level in safe area and to prevent it from severe wear. The difficulty of on-line tool wear prediction is originated from the existence of a hard-to-reach cutting zone in high temperatures. Thus, receiving information from the cutting zone is performed using sensor systems. In the past, using cutting forces and AE sig-nals, tool wear and tool breakage detections have been performed. Çakır and Isik[5]used tangential cutting force signals for monitor-ing of the development of tool wear and tool breakage durmonitor-ing turn-ing of AISI 1050 steel with coated and uncoated carbide tools. The developed software can detect the tool breakage with the 74–84% success rate and increase rate of the FCwas calculated as 28–38%.
In other work[6]cutting force measurement was performed for the prediction of tool wear evolution. It was found a relationship between wear curve and cutting force variations that shows the transitions in tool wear evolution. Maruda et al.[7]compared dif-ferent cutting conditions for reducing tool wear using lubrication and additive during turning AISI 1045 steel. Using lubrication and additive contained lubrication reduced tool wear in rate of 25% and 40% respectively. Caggiano et al.[8]used a sensor based https://doi.org/10.1016/j.measurement.2019.04.022
0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.
⇑ Corresponding author.
E-mail addresses:mkuntoglu@selcuk.edu.tr(M. Kuntog˘lu),hsaglam@selcuk.edu. tr(H. Sag˘lam).
Contents lists available atScienceDirect
Measurement
monitoring system which provides information about tool wear development during turning of Ti6Al4V. The developed system can be useful when integrated with numerical controller for recon-struction of tool wear. Bhuiyan et al.[9]performed an experimen-tal study based on the measurement of AE signal for separating the chip formation and plastic deformation and tool wear processes. It is indicated that in the turning operation there is frequency band between 68 kHz and 635 kHz for chip formation and it is separated from plastic deformation and tool wear processes. Neslusan et al.
[10]presented a new method to prevent catastrophic tool failure and recognize different phases of tool wear using two different AE sensors having the frequency ranges between 15 and 180 kHz and 100–1000 kHz. The suggested system is based on sensing dif-ferent cases i.e. cracking and plastic deformation in hard turning. According to results, developed system can be used successfully at low removal rates. Jemielniak and Otman [11] proposed a method based on statistical analysis of AERMSsignal which provide
significant information about tool breakage and chipping for suc-cessful monitoring. The achieved results showed good relationship between tool breakage and AE signal. Dolinsek and Kopac[12]used cutting tool as a part of the monitoring system by means of consid-ering the cutting fluid as transmission medium which transfers the AE signals. It is indicated that tool wear changes can be character-ized for different tool materials with this study. Zheng et al. inves-tigated the effects of cutting parameters on cutting forces, tool life and wear mechanisms in turning of 300 M. The results showed that cutting force and tool flank wear is highly affected by cutting speed which provides significant information for optimization during turning operation[13].
One of the most common machining operation is turning in modern-day industrial applications. Besides, in academic studies turning is preferred for the investigation of different tool-workpiece materials for optimizing the machining parameters to obtain better results i.e. low cost, high quality and productivity. Some of them are summarized for the explanation and comparison with study presented: The study of Mandal et al.[14]presented Taguchi method based on L9orthogonal array to find out the best
cutting conditions and the most effective parameter on flank wear in turning of AISI 4340 steel. The results showed that depth of cut, cutting speed and feed rate has 46%, 34% and 15% contribution on flank wear respectively. Manivel and Gandhinathan[15] carried out an experimental work for optimization on hard turning opera-tion using ANOVA and S/N ratio analysis. They found that cutting speed has highest contribution with 50% followed by feed rate with 30%. Debnath et al.[16]carried out an experimental work for deter-mining of the best parameters in CNC turning of mild steel. Accord-ing to the results, cuttAccord-ing speed has highest contribution with 43% but feed rate have the least contribution with 7% on tool wear. Sel-varaj et al.[17]applied Taguchi optimization method for machining of special stainless steel. It is found that cutting speed and feed rate affect tool wear 91–92% and 7–8% respectively. In these works, it was seen that Taguchi based optimization methods have been used successfully for reduced production time and costs. Ramesh et al.
[18]investigated optimum turning parameters of Mg alloy using ANOVA. The results of analysis showed that cutting speed and feed rate have close contributions to flank wear with 39–29% respec-tively. Kıvak [19] studied the machinability of Hadfield steel to determine effective parameters and used two different coated car-bide inserts to compare PVD and CVD coatings. It is understood from the paper that cutting speed has higher effect on flank wear. Coating material is the second effective parameter with 23% contri-bution followed by feed rate with 16%. Bagaber et al.[20]analyzed the machining parameters for optimization of tool wear and power consumption in turning of stainless steel 316. According to results feed rate has no important effect on tool wear and cutting speed has highest effect with 39% contribution. Parida and Maity used
Monel-400 as workpiece material in turning operation to analyze the effects of cutting parameters on flank wear. Anova results showed that cutting speed is an effective factor compared to feed rate[21]. Mia et al. performed an experimental turning operation to analyze the effect of cutting parameters on tool flank wear in turning of AISI 1060 steel. Based on Taguchi design, the authors showed that with 29% contribution rate cutting speed has an impor-tant effect on tool wear compared with feed rate 1%[22]. It can be seen there are considerably much paper exist in literature and pub-lished last years. The cited works showed that ANOVA and Taguchi methods have been used for optimization of various pair of tool-workpiece material. Cutting speed always has higher influence than feed rate and in the work[17]different types of tool based on coat-ing materials stand between feed rate and cuttcoat-ing speed.
Zebala and Kowalczyk[23]used Taguchi design and ANOVA for the investigation of the effects of cutting parameters on cutting force during turning of WC-Co material with different content of cobalt. ANOVA results indicated that depth of cut has higher effect for two shafts approximately 59% and 80%. On the other hand, cut-ting speed and feed rate has 30–6% percent contribution on main cutting force for first shaft and 2–14% for second shaft, respec-tively. Bensouilah et al.[24]focused the effect of cutting parame-ters on cutting force components in turning of AISI D3 cold work tool steel. Using Taguchi’s L16 orthogonal array and ANOVA, the
most effective parameters are determined. Depth of cut clearly has higher percent contribution approximately 80% on the other hand feed rate and cutting speed have smaller effect i.e. 15% and 3% respectively. Zerti et al.[25]performed an experimental study under dry cutting conditions to optimize parameters during turn-ing AISI D3 steel. Analysis results showed that tangential cuttturn-ing force is effected by depth of cut and feed rate with 60.9% and 19.5% contribution rate. In the past works, depth of cut clearly has dominant effect on tangential cutting force followed by feed rate while cutting speed has no notable influence.
There are several types of significant input parameters i.e. tool geometry, cutting fluid, coating layer, tool-workpiece material and output parameters i.e. surface roughness, cutting forces and power consumption etc. that affect machining quality. Mostly used machining parameters were cutting speed, feed rate and depth of cut as it is summarized before. It can be seen rarely in the literature that the investigation of the effectiveness of tool tip on tool wear. In this study, for filling this literature gap, tangential cutting force and AE based sensor system was adopted on a lathe and it is con-ducted for the investigation of influence level of the machining parameters on progressive tool wear and monitoring of tool break-age. It can be possible for determining of machining parameters and enable obtaining minimum tool wear. During dry turning of the AISI 1050 workpiece material the effect of cutting speed, feed rate and tool tip were analyzed with ANOVA. Besides, the opti-mization of tangential cutting force and AE were performed using the same method. Constituted monitoring system can provide on-line information about tool condition to prevent tool breakage as a result of excess tool wear.
2. Material and methods
The selection methodology of cutting tool bits, workpiece mate-rial and experimental design have great importance for contribu-tion and comparison with previous studies. The detailed explanation is made in this chapter.
2.1. Workpiece material and cutting tools
In this study, AISI 1050 carbon steel which has Ø50x400 mm in length was chosen as sample material because of the properties
like common usage in industrial applications and hard-to-wear structure. The chemical composition of the workpiece material is given inTable 1. Experiments were carried out on a conventional lathe (T-165-MF). The tool holder (TAKIMSASß R/L 140 0 20 16) has 60° approaching angle and integrated to dynamometer. The cutting tool geometry (TCMT 16T304) and three tool type was cho-sen as P10, P25 and P35 (BOHLER). In order to determine cutting parameters, manufacturer’s handbook and machine tool operation range were compared. The initial values were chosen as constant 2 mm depth of cut, three cutting speeds (135, 194 and 207 m/min) and three feed rates (0.171, 0.214 and 0.256 mm/rev). 2.2. Experimental study
Experiments were conducted under dry cutting conditions. The experimental setup includes machine tool, measuring devices and a computer. The experimental setup is shown inFig. 1. Multiple sensor system was integrated to conventional lathe containing AE sensor (KISTLER 8152B111/121) and dynamometer (TELC 3D). The dynamometer measures 3 cutting force components (Ft, Fr,
Fz) and sends 10 data directly to PC. By means of processing and
digitizing of signals by software, the assessment and interpretation of cutting forces can be performed easily. On the other hand, the Root Mean Square (RMS) value of AE sensor signal is transferred to PC via data acquisition card (NATIONAL INSTRUMENTS 6036-E) allowing measurements from 50 kHz to 400 kHz. Flank wear measurement was carried out with toolmakers microscope (MITU-TOYO TM-500). Using each cutting tool tip for 12 times, a new steel bar was machined for collecting data about cutting forces and AE signals. For each test, 12 specimens were machined using one tool tip under the same cutting conditions to investigate developing tool wear and possible tool breakage. At the end of 3 passes, the operation was stopped and flank wear was measured 4 times for every experiment.
2.3. Taguchi method and experimental design
The design and optimization are fully accepted process steps for analyzing and better construction of experimental studies in engi-neering applications. Taguchi experimental design is widely used to determine the number of experiment which directly affects labor, production cost and energy consumption. The primary advantage of this method is the guaranteed design quality provid-ing by itself [15,24,26]. Robust and reliable design provide enhanced product quality and efficiency.
Taguchi method uses signal-to-noise (S/N) ratio and objective function to reduce the effect of noise factors with determining the quality characteristics[26,27]. Objective function or loss func-tion calculates the deviafunc-tion from optimum value of performance characteristics[27]. Also, orthogonal arrays ensure decreasing the number of experiments with reducing the effect of noise or uncon-trollable factors[24,26]. In Taguchi design, there are three types S/ N ratio which determine the aim of the experiment i.e. larger is better, smaller is better and nominal is the best. The calculation for each type is defined with these equations:
For smaller is better:
S=N ¼ 10 log1=nð
R
y2Þ ð1ÞFor larger is better:
S=N ¼ 10 log1=nð
R
y2Þ ð2ÞFor nominal is the best:
S=N ¼ 10 log y=s2
y ð3Þ
In this paper, in order to decide the number of experiments, Taguchi L9 orthogonal array was used. The cutting parameters
and levels are presented inTable 2and the Taguchi design is shown inTable 3. It can be seen that there are three factors and their levels exist in design table. Taguchi provided the optimized parameters with minimum experiment. Considering the response parameters and their effects on machining operation, tool wear, AE and tangen-tial cutting force should be as low as possible. From that, smaller is better type was chosen as quality characteristic. Every experiment and flank wear measurement were performed three times and aver-age value was considered as the ultimate result.
3. Results and discussion
The statistical analysis results of response parameters and cal-culated ANOVA and S/N ratio results are presented inTables 4–6. The bold numbers represent the most significant factor level affect-ing flank wear, cuttaffect-ing force and acoustic emission in Table 4. Besides, the experimental results of flank wear, tangential cutting force and acoustic emission with effects of cutting parameters dur-ing 12 passes are shown onFigs. 3, 5 and 6. The cutting speed, feed rate and tool type were selected as cutting parameters and designed with L9 orthogonal array to determine optimal cutting
parameters. The additional experiments were conducted and com-pared with predicted and experimental results for optimum and random cutting conditions (Table 7).
3.1. Analysis of variance results for VB, AE and FC
ANOVA demonstrates the importance of relationships between factors and responses with several statistical parameters.Table 5
includes the ANOVA results of flank wear, tangential cutting force and acoustic emission and corresponding ANOVA parameters. P value shows statistical significance of the control factors. The higher the percent contribution refers to the higher effect on response parameter. According to results, the percent contribution of cutting speed, feed rate and tool type were found as 44%, 18% and 34% respectively on flank wear. Feed rate (%88) is the most dominant factor on tangential cutting force followed by tool type (%9). P-values show that both feed rate (0.002) and tool type (0.015) have important effect on tangential cutting force. It is observed that the cutting speed has highest effect on tool flank wear (44%) and acoustic emission (79%) and P-value (0.039 < 0.05, 0.023 < 0.05) confirms the result. The mentioned results indicated that obtained evaluation values are compatible with past works. Lastly, some contribution values are neglected in the scope of this study because of lower effects that can be noticed for future works.
3.2. Analysis of S/N ratio results for VB, AE and FC
The aim of the Taguchi method is determining optimum param-eters using orthogonal array and S/N ratio. This methodology enables to obtain maximum information with minimum experi-ment. The calculation of S/N ratio was performed using ‘‘smaller is better” type quality characteristic for all responses. Table 6
shows the experimental design of L9orthogonal array and S/N ratio
values for all response parameters. The results indicated that for flank wear, tangential cutting force and acoustic emission are the experimental mean values for each line. S/N ratio should have the highest value to accept the related parameter as optimum.
Table 1
The chemical composition of AISI 1050 carbon steel.
C Mn PMAX SMAX
Table 6shows only the results L9orthogonal array for S/N ratio but
optimum parameters are presented in Table 4. The random and optimum valued experiment results can be seen inTable 7.
According to these results, the optimum cutting conditions for flank wear are the cutting speed v1 = 135 m/min (S/N =4.109), feed rate f2 = 0.214 mm/rev (S/N =4.789), tool type T2 = P25 (S/ N =4.593) and minimum flank wear VB = 1.44 mm (S/
N =3.16725). For tangential cutting force, the dominant factors are found as cutting speed v3 = 207 m/min (S/N =55.15), feed rate f1 = 0.171 mm/rev (S/N =53.84), tool type T3 = P35 (S/ N =54.87) and minimum FC= 456 N (S/N =53.2756). Similarly,
the optimized factors for acoustic emission are cutting speed v1 = 135 m/min (S/N =146.3), feed rate f2 = 0.214 mm/rev (S/ N=149.5), tool type T3 = P35 (S/N = 149.2) and minimum AE = 13.9 mV (S/N =145.197).
3.3. Evaluation of the experimental results
Developing tool wear and possible tool breakage at future phases have negative effects on machining quality. Therefore, con-stitution of experimental design, selection of cutting parameters and monitoring of cutting area gain importance to take
precau-Fig. 1. Experimental setup.
Table 2
Cutting parameters and factor levels.
Symbol Parameters Level 1 Level 2 Level 3
v Cutting Speed (m/min) 135 194 207
f Feed Rate (mm/rev) 0.171 0.214 0.256
tions, reduce labor and eventually enhance cutting performance. In order to understand how the cutting parameters influence perfor-mance characteristics, both statistical and experimental work were performed. Consequently, the impact levels obtained for each response parameter and optimized parameters have been found. In the orthogonal array design, mean values were used and it is important to see the same effects during on-line monitoring of machining operation.
Mean tangential cutting force, AE and flank wear values obtained during turning of AISI 1050 steel at 2 mm constant depth of cut with three different cutting speed, feed rate and tool types.
Figs. 2 and 4 shows average values of tangential cutting force and AE signals at different combinations of cutting forces. InFigs 3, 5 and 6, the variation at tangential cutting force, AE signals and flank wear are presented at the end of every three passes respectively.
3.3.1. Effects of cutting parameters on tangential cutting force Main effective factors on tangential cutting force are specific cutting force, feed rate and depth of cut[28]. Tangential cutting force shows decreasing tendency with the increasing hardness because of the difficulty of plastic deformation[29,30]. Depth of cut was kept constant in this study. For all cutting speeds and tool types, increasing feed rate increases tangential cutting force because of the enhancing cutting area (Fig. 2a and b). The com-bined effect occurs from tangential cutting force and feed rate gen-erates these cutting force curves. Maximum tangential cutting force values were obtained because of the combined effect of high feed rate and boundary conditions such as v3T3and v1T1(Fig. 2c).
In other words, low cutting speed and hard-to-deform tool increases tangential cutting force with the additional effect of tool-workpiece contact time occurring from high feed rate. High cutting speed and tough-structured tool affects tool geometry and increases tangential cutting force because of high cutting temperatures.
With the increasing cutting speed, cutting force values show decreasing tendency. High cutting speed causes increasing in tem-perature which rises plastic deformation, increases normal and shear stresses at tool tip and reduces cutting forces [7,31–33]. Therefore, Fig. 3a exhibits higher cutting forces thanFig. 3b. In
Fig. 3c, a similar situation occurs except for tool type of P10. Tool breakage occurred during the tests at cutting conditions V3f1P1.
Maximum cutting speed and minimum feed rate lead to chipping and increasing in tool-workpiece contact time which result in changing of tool geometry and tool-workpiece contact area. Besides, the combined effect of different wear mechanisms (abra-sive, adhesive and diffusive) have devastating effects on tool tip especially because of high cutting speeds and resulting diffusion mechanism[24,34–36]. As a result of this, variation of chip shape and chip formation mechanism increases cutting forces. The addi-tional effect of hard-to-deform structure of P10 tool type also con-tributes to increasing of tangential cutting forces. Analysis of the results according to pass number indicated that increasing number of pass increases tangential cutting force because of increasing tool
Table 3
The experimental design.
Number of Experiment Factors and Levels
v f T 1 1 1 3 2 1 2 2 3 1 3 1 4 2 1 2 5 2 2 1 6 2 3 3 7 3 1 1 8 3 2 3 9 3 3 Table 4
Response table for S/N ratios for VB, AE and FC.
Level Factors
Cutting Speed Feed Rate Tool Tip
Flank Wear 1 4.109 4.882 4.819 2 5.545 4.784 4.593 3 5.902 5.889 6.144 D 1.792 1.105 1.551 Cutting Force 1 55.55 53.84 55.82 2 55.19 55.23 55.19 3 55.15 56.81 54.87 D 0.4 2.97 0.95 Acoustic Emission 1 146.8 151.0 149.2 2 150.9 149.5 151.5 3 152.6 149.9 149.7 D 5.8 1.5 2.3 Table 5
ANOVA results for flank wear, tangential cutting force and acoustic emission.
Cutting Parameters DOF SS MS F P PC
Flank Wear Cutting Speed 2 5.4005 2.7002 24.32 0.039 0.44 Feed Rate 2 2.244 1.122 10.11 0.09 0.18 Tool Type 2 4.2103 2.1051 18.96 0.05 0.34 Error 2 0.222 0.111 Total 8 12.0767
Tangential Cutting Force
Cutting Speed 2 0.2899 0.14495 13.32 0.07 0.02 Feed Rate 2 13.25 6.62598 608.86 0.002 0.88 Tool Type 2 1.4044 0.70218 64.52 0.015 0.09 Error 2 0.0218 0.01088 Total 8 14.9680 Acoustic Emission Cutting Speed 2 53.41 26.7052 42.42 0.023 0.79 Feed Rate 2 3.713 1.8566 2.95 0.253 0.05 Tool Type 2 8.861 4.4307 7.04 0.124 0.13 Error 2 1.259 0.6295 Total 8 67.244 *R2 values for AE = 98%, FC= 99%, VB = 98%.
wear. This implies a changed cutting tool-workpiece contact area and affects rate of material removed.
3.3.2. Effects of cutting parameters on acoustic emission
AE is microstructural energy propagation in internal structure of the material which undergoes an external force. There are sev-eral types of energy appear during machining operations i.e. cut-ting forces, temperature, vibration. When it is considered in micro scale, external loading leads to generation of stress waves called AE. Some of the important AE sources are deformation of tool and workpiece, breakage of tool, friction and collision of chips
[9,10].
It was reported from researches[10,37–39]that two types of AE signal were observed during machining operations. Different events generate different AE signals such as breakage of chip or tool leads to burst type signals while deformation in shear zones or tool wear cause continuous type signals. InFig. 4, AE signal development as a function of cutting conditions is indicated according to different combinations. The graphs are regulated as to show binary combinations of cutting speed, feed rate and tool
type. It can be seen that increasing cutting speed increases AE amplitude. In Fig. 4b, v3T1(207 m/min, P10) combination shows
lower values contrary to the other max cutting speed values. Tool breakage was occurred in the progressive passes of this test. The reason is that the combined effect of hard tool type and high cut-ting speed lead to chipping. Therefore, decreasing tool-workpiece contact area decreases AE signal amplitude.Fig. 4a and c showed that increasing feed rate increases AE signal amplitude for both cutting speed and tool type values.
It was stated that tool wear is one of the most effective factor on increasing rate of AE signal[12]. Besides, AE signal shows increas-ing tendency with the increasincreas-ing of tool wear durincreas-ing depth of cut which is kept constant[9]. Authors pointed out for the reason of that increasing tool wear increases both tool-workpiece contact area and coefficient of friction. However, AE curves demonstrate decreasing tendency in further passes inFig. 5almost for all tool type-cutting speed combinations. Firstly, different wear types observed according to cutting conditions during measurement of wear in the experiments. Notch wear and crater wear in tests occurred of P25 and P35 tool types and chipping in tests of P10 tool
Table 6
Experimental design of Taguchi L9orthogonal array and response parameters.
E. N. Cut. Sp. (m/min) Feed Rate (mm/rev) Tool Tip (mm) VB S/N for VB (dB) FC(N) S/N for FC(dB) AE (mV) S/N for AE (dB)
1 135 0.171 P10 1.45 3.22736 540.5 54.6559 21.6 146,716 2 135 0.214 P25 1.44 3.16725 590 55.4170 25.1 147,992 3 135 0.256 P35 1.98 5.93330 674 56.5732 19.4 145,752 4 194 0.171 P25 1.74 4.81098 476.25 53.5567 43.2 152,720 5 194 0.214 P10 1.97 5.88932 545.50 54.7359 31.1 149,876 6 194 0.256 P35 1.98 5.93330 730 57.2665 32.4 150,215 7 207 0.171 P35 2.14 6.60828 463 53.3116 47.6 153,558 8 207 0.214 P10 1.84 5.29636 599 55.5485 33.6 150,543 9 207 0.256 P25 1.95 5.80069 675.75 56.5957 48.7 153,756 Table 7
Comparison of experimental and predicted results.
Experiment Predicted Experimental Accuracy Error
Tool Wear v1f2T2(optimum) 1.41 1.44 0.98 0.02 v3f1T3(random) 2.11 2.14 0.985 0.015 Cutting Force v3f1T3(optimum) 456 463 0.985 0.015 v1f2T1(random) 626 604 0.965 0.035 Acoustic Emission v1f2T1(optimum) 13.9 24.7 0.57 0.43 v2f3T2(random) 40.8 36.6 0.9 0.1
type in addition to flank wear. The combined effect of several tool wear types have important influence on interactions of cutting parameters and tool-workpiece-chip relations. Contribution of dif-ferent tool wears decrease tool-workpiece contact area distinctly from flank wear regularly developed. In other words, a simple flank wear changes the contact with tool and workpiece from point to area. However, wear types which remove material rapidly change contact area irregularly and lead to reduce friction and amplitude of AE. AE variation for different cutting conditions during 12 pass are shown inFig. 5.
3.3.3. Effects of cutting parameters on flank wear
When the flank wear curves are investigated, it can be seen three wear phase such as accelerated initial wear, constantly pro-gressive wear and rapid wear of cutting tip. In Fig. 6 the wear curves as a function of machining pass number can be seen. Wear
curves are grouped with colors and lines according to cutting speed and feed rate respectively. General tendencies and developments can be seen for 9 combinations in Fig. 6. Minimum flank wear development is obtained in V3T1f1test because of excessive
chip-ping and tool breakage.
3.3.4. Detection of tool breakage
At the cutting conditions such as P10 tool type, 207 m/min and 0.214 mm/rev, tool breakage occurred in the 7. Pass of cutting. It can be seen AE and tangential cutting force signal fluctuations in
Fig. 7. Instant picks and hollows of AE indicates there is chipping during machining and tangential cutting force signals confirms that. At the moment of breakage both signal show excessive increase firstly. After the first picks, signals suddenly drops to zero and this implies that tool-workpiece contact ended.
Fig. 3. Tangential cutting force variation for different cutting conditions during 12 pass a) V = 135 m/min, b) V = 194 m/min, c) V = 207 m/min.
3.3.5. Comparison of experimental and predicted results
Optimized parameters are indicated for all response parameters inTable 7. Besides, the cutting parameters selected randomly and corresponding tool wear, cutting force and AE measurements are
shown inTable 7. Confirmation tests were carried out to check the affinity of experimental and predicted results and errors between them. Taguchi method indicated close relations for opti-mum and random results.
Fig. 5. Acoustic emission variation for different cutting conditions during 12 pass a) f = 0.171 mm/rev, b) f = 0.214 mm/rev, c) f = 0.256 mm/rev.
4. Conclusion
This paper presents an experimental work includes the investi-gation of progressive tool wear, detection of tool breakage and parameter optimization during turning of AISI 1050 steel. Taguchi method was used to design experimental plan and optimization of the parameters. ANOVA provided contribution levels of parameters for each response variables. The conducted implications are pre-sented below:
1. Dynamic interaction between variables make difficult to predict the influence of parameters on outputs. Taguchi method enables robust, reliable and efficient design for optimization and contribution levels.
2. ANOVA results showed that cutting speed is the most dominant factor on flank wear (44%) and AE (79%) however tangential cut-ting force is influenced by feed rate (88%) mostly.
3. Contributions of the cutting speed, feed rate and tool type on flank wear were found as 44%, 18% and 34% respectively and on tangential cutting force were found as 2%, 88% and 9% respec-tively and on AE were found as 79%, 5% and 13% respecrespec-tively. 4. Optimum parameters according to S/N ratios for minimum tool
flank wear (1.44 mm) were found as v1 f2 T2 (135 m/min,
0.214 mm/rev and T2 = P25). For minimum tangential cutting force (456 N) v3 f1 T3 (207 m/min, 0.171 mm/rev and P35)
parameters were found. In order to obtain minimum AE (13.9 mV), such as v1 f2 T1 (135 m/min, 0.214 mm/rev and
P35) parameters should be used.
5. Implemented sensor system was successful for detecting tool breakage and monitoring on-line tool wear development. 6. Confirmation tests were performed to compare Taguchi and
experimental results. Results showed close relationship for flank wear and tangential cutting force.
Acknowledgement
This authors would like to thank to Scientific Research Projects Coordinators (BAP) (Project No: 15401125) of Selcuk University for their support in this experimental study.
Appendix A. Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.measurement.2019.04.022.
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