Burr formation and correlation with cutting force and acoustic emission signals

Abstract

The principle objective of this work is to present a methodology to evaluate the correlation between burr size attributes (thickness and height) and information computed from acoustic emission and cutting forces signals. In the proposed methodology, cutting force and acoustic emission signals were recorded in each cutting test, and each recorded original acoustic emission signal was segmented into two sections that correspond to steady-state cutting process (cutting signal) and cutting tool exit from the work part (exit signal). The dominant acoustic emission signal parameters including AE_max and AE_rms were computed from each segmented acoustic emission signal. The maximum values of directional cutting forces (F_X, F_Y and F_Z) were also measured in each trial. The experimental verification was conducted on slot milling operation which has relatively more complicated burr formation mechanism than that in many other traditional machining operations. Among slot milling burrs, the top-up milling side burrs and exit burrs along up milling side were largest and thickest burrs which were studied in this work. To evaluate the correlation between signal information and burr size, the computed signal information (5 parameters) and their interaction effects (10 parameters) were used to construct the input parameters of the multiple regression fitted models. Statistical methods were then used to assess the adequacy of individual input parameters and signal information. Using the acoustic emission and cutting force signals information in the input layer of multiple regression models, a high correlation was observed between the predicted and observed values of burr size. It was exhibited that due to complex burr formation mechanism in milling operation and strong interaction effects between cutting process parameters, no systematic relationship can be formulated between the milling burrs.

Introduction

One of the main industrial concerns is burr formation on the product edges that may cause many difficulties during inspection, assembly and manufacturing automation of precision components. There are two suggested ways to overcome these difficulties. The first approach is deburring. As pointed out by Gillespie, ¹ deburring and edge finishing operations are hard to automate, and they may represent as much as 30% of the cost of finished parts.^2,3 Thus, it is necessary to limit the burr formation rather than deburring them in subsequent finishing operations. The second approach consists of burr size prevention, minimization and/or estimation. Investigations on burr formation have been reported in various experimental and modeling (numerical and analytical) studies.^4–9 In general, burr size modeling using experimental results is only accurate for a narrow range of input process parameters, and higher-order empirical models require a larger number of experiments. Due to several factors that are very difficult to model explicitly, burr size prediction using analytical models is a complex task. In addition, burr formation models based on finite element method (FEM) are relevant to the accuracy of input boundary conditions, which are not yet advanced and, therefore, are usually simplified. Moreover, the results are strongly influenced by the software applied; time-consuming and generally experimental data are required for model validation. ⁷

One alternative approach is to use signal information for burr size estimation. This method has received less amount of attention ¹⁰ due to certain levels of difficulties, including (1) inadequate understanding of automated burr size measurement approaches, (2) incomplete knowledge about sensitivity of sensors to cutting parameters and (3) lack of knowledge about modeling approaches for burr formation modeling. ¹¹ To monitor machining processes and various machining outputs (e.g. burr size), special attention has been paid to dynamometer, accelerometer, acoustic emission (AE) and current sensors. ¹² Although cutting force is known as a variable that best describes the cutting process, ¹³ but dissimilar to AE signals, it is not independent of the tool path in cutting process. In fact, the ability to detect microscale deformation mechanisms within a relatively noisy cutting background promotes AE as a popular tool for process monitoring applications. ¹⁴ Due to noncontinuous mode of the cutting process and run-out effects in milling operations, vibration signals may not be as reliable as cutting forces and AE signals.^11,15 This exhibits that according to frequency coverage of sensors and complexity of burr formation mechanism, AE and dynamometer sensors seem to perform better when used in milling burr size prediction models. Surprisingly, only few studies^10,16,17 evaluated the correlation between burr size and computed information from AE and/or cutting force signals. This denotes that preliminary studies are still needed to understand better the capability of AE and cutting force signals for burr formation detection and burr size estimation. To that end, this work proposes a methodology to evaluate the correlation between exit and top burrs’ size (thickness and height) and signals information computed from AE and cutting forces signals. Experimental verifications were studied on slot milling top and exit burrs.

Background of burr formation

Burr formation is one of the major issues manufacturing industries are currently facing. ² Among traditional machining operations, milling burr formation has a relatively more complex morphology. Milling burrs are created when the cutting tool enters and exits the machined parts. ¹⁸ The burr formation mechanism in end milling and slot milling operations (Figures 1 and 2) are even more complex than the one in face milling. Unlike in face milling, subsequent tools passing through in end milling and slot milling operations do not usually remove the burrs produced by previous tools. Consequently, side burrs and top burrs remained on the part. ⁸

Figure 1.

Definition of slot milling burrs. ⁷

Figure 2.

Slot milling burrs: (a) exit up milling side (EUMS) burr, (b) exit bottom side (EBS) burr, (c) top-down milling side (TDMS) burr and (d) top-up milling side (TUMS) burr. ²¹

To define better the burr size attributes, a new term called “burr value” (Figure 3) was defined in Schäfer. ¹⁹ It encompasses several burr size parameters such as burr root thickness (b_r), burr height (b_h), burr thickness (b_t) and burr root radius (r_f). However, adequate measurement of all above-mentioned burr size parameters to calculate the burr value is a very difficult and time-consuming approach. Furthermore, it would appear that the burr value cannot be used as an efficient parameter to better select a deburring method. Of all the burr size parameters, the burr height and thickness are used to determine the burr removal difficulties. ²⁰ Generally, the burr thickness is mostly of interest as it defines the time and method necessary for deburring process. ²¹ In this article, both burr height and thickness will be studied.

Figure 3.

Measurement values of a burr.

As presented in Figure 1, the exit burrs along bottom side (EBS) and up milling side (EUMS) are largest burrs, and the top burrs along up milling side (TUMS) and down milling side (TDMS), the entrance and exit burrs along down milling side are on a medium-scale, comparatively ²² (Figure 2). Considering the larger size of exit burrs as compared to top and entrance burrs (Figure 1), extensive studies were conducted to evaluate factors governing exit burrs.^7,23–27

Research methodology

The first considerations of burr formation in metal cutting came along with investigations about chip formation, while burr formation mechanism very much depends on chip formation mechanism. Cutting force is among the governing factors on burr formation mechanism and size, which has a direct relationship with feed rate and depth of cut. ²⁸ According to Niknam ²⁷ and Liang and Dornfeld, ²⁹ directional cutting forces in slot milling of AA 2024-T351 and AA 6061-T6 are mainly controlled by feed per tooth, and it was reported that depth of cut and cutting speed have insignificant effects on directional cutting forces.

As pointed out in Dornfeld, ³⁰ the generation of AE signals in most of the cutting operations is likely dependent on material removal rate (MRR) and consumed energy level in cutting process

$$\overline{{\mathrm{AE}}_{\mathrm{power}}}\approx \mathrm{A}\times \mathrm{MRR}$$

(1)

where A is a constant depending on process parameters such as cutting geometry, thermal properties and so on. The bar above AE_power in equation (1) indicates that the power is averaged over some time interval.

There are number of AE parameters that can be extracted from AE signals through time and frequency analysis. The behavior of AE signals in machining processes depends on variation of cutting parameters, cutting tool and workpiece. According to Niknam et al., ³¹ AE_rms, AE_max and AE_var were found as the most sensitive AE parameters to a broad range of cutting parameters in slot milling of AA 6061-T6 and AA 2024-T351. Other governing factors on burr size variation are material deformation properties as well as cutting parameters and tool geometry.^8,21 Due to strong interaction effects between process parameters and lack of knowledge about dominant process parameters on burr formation in many machining operations, the relationship between the cutting parameters and burr size attributes (e.g. height and thickness) cannot be precisely formulated in mathematical equations. ²⁶ Therefore, by considering the sensitivity of AE and cutting force signals to variation of cutting parameters, MRR and consumed energy level in cutting process, it is expected that there is a correlation between the burr size attributes and signals information.

The proposed method consists of the following steps (Figures 4 and 5):

Figure 4.

Overview of proposed approach: (a) cutting signal (0.2 s) and (b) exit signal (0.1 s).

Figure 5.

Architecture of the proposed methodology.

1. To extract two segmented signals from each original recorded AE signal, x(t), in each cutting trial with respect to cutting tool progress and exit from machined parts;

2. To perform signal processing over segmented AE signals and computing the appropriate signal features (AE_rms and AE_max) using a sampling frequency (f_s) of 100 kHz:

$${\mathrm{AE}}_{\mathrm{rms}}=\sqrt{\frac{1}{\mathrm{n}}\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{x}}_{\mathrm{i}}^2}$$

(2)

where n is the data points in each signal.

• 3. To calculate the maximum values of cutting forces in longitudinal (F_X), vertical (F_Y) and axial (F_Z) directions from each recorded cutting force signal;

• 4. To construct the input layer and then formulating the linear multiple regression models (Appendix 1), followed by feature selection approach;

• 5. To reconstruct the linear multiple regression models (empirical burr size estimation models) using statistical significant variables determined through feature selection approach.

The significant and insignificant variables and models are identified using statistical parameters, including P-value, R² and ${\mathrm{R}}_{\mathrm{adj}}^2$ (Appendix 2). As presented in proposed methodology (Figure 5), to achieve better model’s accuracy, the input variables with highest P-values should be removed from the input layer, unless the model’s P-value is smaller than 0.05 and the corresponding R² is higher than 75%.

Experimental procedure

A multilevel full factorial design (3² × 2²) was used as the experimental plan. The experimental factors and their levels are shown in Table 1. In total, 36 experiments are necessary to complete the study.

Table 1.

Cutting parameters and their levels used.

Cutting parameters	Level
	1	2	3
A: cutting speed (m/min)	300	750	1200
B: feed per tooth (mm/z)	0.01	0.055	0.1
C: depth of cut (mm)	1		2
D: material	AA 2024-T351		AA 6061-T6
E: tool	D = 19.05 mm, Z = 3, Rε = 0.5 mm, coating: TiCN
Cutting fluid	None (dry machining)

The main experimental stages are as follows:

1. Slot milling tests were performed on a three-axis computer numerical control (CNC) machine (power: 50 kW, speed: 28,000 r/min; torque: 50 N m) using the process variables listed in Table 1;

2. Data acquisition using AE sensors (Fs = 100 kHz) placed near the chip formation zone and a three-axis dynamometer (Kistler-9255B; Fs = 48 kHz) for cutting force measurements;

3. AE_rms and AE_max were measured through each segmented AE signal as presented in methodology (Figure 5). The maximum values of directional cutting forces during each cutting trial are computed through cutting force signals (Figures 4 and 5);

4. Burr size attribute (height and thickness) measurement was conducted using a high-precision optical microscope, equipped with high-resolution camera. An average of four burr thickness readings and maximum burr height were considered as the scale of burr size in this study.

To develop the experimental set up, the stability of cutting process, machine and tool vibration and dynamic behavior were carefully controlled in preliminary tests. Using rigid tool and workpiece fixtures, a negligible deflection was observed in the tool and workpiece. To avoid possible deviations in AE and cutting force signals that might occur due to tool wear, a new insert was used after each cutting test. Moreover, to examine background noise, one AE sensor was placed 2 m away from chip formation zone.

Results and discussion

The results in this article are presented in the following categories.

Comparative study between the milling burrs

As noted in Niknam and Songmene, ⁸ the size of burrs that occurs in similar cutting section (e.g. exit, top and entrance) is not statistically correlated to each other. To confirm this statement with recorded experimental results, linear simple regression models (Figures 6–9) were used to study the correlation between recorded values of top and exit milling burrs along up and down milling sides. According to Tables 2 and 3 and Figures 6–9, very weak correlation is observed between exit and top burrs’ size. This exhibits that due to noncontinuous mode of cutting process and run-out effects in milling operations, complex burr formation may occur where, in fact, no systematic relationship can be formulated between exit and top milling burrs. In addition, cutting parameters and their interaction effects are other governing factors on milling burr formation mechanism.^8,26 Consequently, there are severe difficulties for adequate monitoring and burr size estimation or modeling in noncontinuous machining operations such as milling and drilling operations. ²¹ Therefore, as mentioned earlier, to detect burr formation and/or estimate the burr size, the use of alternative approaches such as signal information is evolved.

Figure 6.

Linear simple regression models of EBS and EUMS burrs’ height in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 7.

Linear simple regression models of EBS and EUMS burrs’ thickness in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 8.

Linear simple regression models of TUMS and TDMS burrs’ thickness in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 9.

Linear simple regression models of TUMS and TDMS burrs’ height in (a) AA 2024-T351 and (b) AA 6061-T6.

Table 2.

Statistical summary of regression models of AA 2024-T351.

List	Response characteristics		ANOVA parameters
			Correlation rate	R2 %	P-value	Remark
1	EBS burr height	EUMS height	0.031	0.091	0.91	Insignificant
2	EBS burr thickness	EUMS thickness	0.051	0.281	0.83	Insignificant
3	TDMS burr thickness	TUMS burr thickness	0.021	0.046	0.93	Insignificant
4	TDMS burr height	TUMS burr height	0.031	0.091	0.90	Insignificant

ANOVA: analysis of variance; EBS: exit bottom side; EUMS: exit burrs along up milling side; TUMS: top-up milling side; TDMS: top-down milling side.

Table 3.

Statistical summary of regression models of AA 6061-T6.

List	Response characteristics		ANOVA parameters
			Correlation rate	R2 %	P-value	Remark
1	EBS burr height	EUMS height	0.131	1.86	0.589	Insignificant
2	EBS burr thickness	EUMS thickness	0.582	33.1	0.011	Insignificant
3	TDMS burr thickness	TUMS burr thickness	0.122	1.44	0.631	Insignificant
4	TDMS burr height	TUMS burr height	0.044	0.19	0.862	Insignificant

ANOVA: analysis of variance; EBS: exit bottom side; EUMS: exit burrs along up milling side; TUMS: top-up milling side; TDMS: top-down milling side.

Correlation between burr size and signal information

EUMS burr thickness

According to burr formation mechanism and by considering that there are higher levels of ${\overline{\mathrm{AE}}}_{\mathrm{power}}$ , MRR and cutting forces in up milling side of the machined parts, it is expected that the signal information can be useful for milling burr formation detection and burr size estimation. To that end, the extracted cutting forces, AE data and their interaction effects were combined to initially construct the input layer (Tables 4 and 5) of the multiple regression models by means of evaluating the correlation between response variables (up milling top and exit burrs’ size) and cutting process variables (AE and cutting force parameters).

Table 4.

Statistical summary of EUMS burr thickness models in AA 2024-T351 and AA 6061-T6.

List	Input variables	Index	AA 2024-T351		AA 6061-T6
			P-value	Remark	P-value	Remark
1	Constant	P1	0.1325	Insignificant	0.7291	Insignificant
2	${\mathrm{F}}_{\mathrm{x}}$	P2	0.0798	Mid-significant	0.4550	Insignificant
3	${\mathrm{F}}_{\mathrm{y}}$	P3	0.0618	Mid-significant	0.3901	Insignificant
4	${\mathrm{F}}_{\mathrm{z}}$	P4	0.1416	Insignificant	0.8406	Insignificant
5	${\mathrm{F}}_{\mathrm{x}}\times {\mathrm{F}}_{\mathrm{y}}$	P5	0.2003	Insignificant	0.1936	Insignificant
6	${\mathrm{F}}_{\mathrm{x}}\times {\mathrm{F}}_{\mathrm{z}}$	P6	0.0958	Mid-significant	0.5250	Insignificant
7	${\mathrm{F}}_{\mathrm{z}}\times {\mathrm{F}}_{\mathrm{y}}$	P7	0.1024	Insignificant	0.6321	Insignificant
8	${\mathrm{AE}}_{\mathrm{rms}}\times {\mathrm{F}}_{\mathrm{x}}$	P8	0.1401	Insignificant	0.6161	Insignificant
9	${\mathrm{AE}}_{\mathrm{rms}}$	P9	0.6270	Insignificant	0.4757	Insignificant
10	${\mathrm{AE}}_{\max }\times {\mathrm{F}}_{\mathrm{x}}$	P10	0.0963	Mid-significant	0.4985	Insignificant
11	${\mathrm{AE}}_{\max }$	P11	0.820	Insignificant	0.4697	Insignificant
12	${\mathrm{AE}}_{\max }\times {\mathrm{F}}_{\mathrm{y}}$	P12	0.1061	Insignificant	0.4735	Insignificant
13	${\mathrm{AE}}_{\max }\times {\mathrm{F}}_{\mathrm{z}}$	P13	0.6781	Insignificant	0.4985	Insignificant
14	${\mathrm{AE}}_{\mathrm{rms}}\times {\mathrm{F}}_{\mathrm{y}}$	P14	0.1243	Insignificant	0.5533	Insignificant
15	${\mathrm{AE}}_{\mathrm{rms}}\times {\mathrm{F}}_{\mathrm{z}}$	P15	0.6498	Insignificant	0.4737	Insignificant
16	${\mathrm{AE}}_{\max }\times {\mathrm{AE}}_{\mathrm{rms}}$	P16	0.2235	Insignificant	0.6590	Insignificant
Model			0.1378	Insignificant R2: 98.04%; ${\mathrm{R}}_{\mathrm{adj}}^2$ : 81.41%	0.1618	Insignificant R2: 97.67%; ${\mathrm{R}}_{\mathrm{adj}}^2$ : 80.22%

Table 5.

Statistical summary of EUMS burr thickness models in AA 2024-T351 and AA 6061-T6 after feature selection.

List	AA 2024-T351				AA 6061-T6
	Input variables	Index	P-value	Remark	Input variables	Index	P-value	Remark
1	Constant	P1	0.0364	Significant	Constant	P1	0.0285	Significant
2	${\mathrm{F}}_{\mathrm{x}}$	P2	0.004	Significant	${\mathrm{F}}_{\mathrm{y}}$	P3	0.0001	Significant
3	${\mathrm{F}}_{\mathrm{y}}$	P3	0.0031	Significant	${\mathrm{F}}_{\mathrm{x}}\times {\mathrm{F}}_{\mathrm{y}}$	P5	0.0015	Significant
4	${\mathrm{F}}_{\mathrm{z}}$	P4	0.0187	Significant	${\mathrm{AE}}_{\mathrm{rms}}\times {\mathrm{F}}_{\mathrm{x}}$	P8	0.0004	Significant
5	${\mathrm{F}}_{\mathrm{x}}\times {\mathrm{F}}_{\mathrm{y}}$	P5	0.0403	Significant	${\mathrm{AE}}_{\mathrm{rms}}$	P9	0.0010	Significant
6	${\mathrm{F}}_{\mathrm{x}}\times {\mathrm{F}}_{\mathrm{z}}$	P6	0.0095	Significant	${\mathrm{AE}}_{\max }\times {\mathrm{F}}_{\mathrm{x}}$	P10	0.0006	Significant
7	${\mathrm{F}}_{\mathrm{z}}\times {\mathrm{F}}_{\mathrm{y}}$	P7	0.0109	Significant	${\mathrm{AE}}_{\max }\times {\mathrm{F}}_{\mathrm{z}}$	P13	0.0211	Significant
8	${\mathrm{AE}}_{\mathrm{rms}}\times {\mathrm{F}}_{\mathrm{x}}$	P8	0.0420	Significant	${\mathrm{AE}}_{\mathrm{rms}}\times {\mathrm{F}}_{\mathrm{z}}$	P15	0.0197	Significant
9	${\mathrm{AE}}_{\max }\times {\mathrm{F}}_{\mathrm{x}}$	P10	0.0491	Significant	–	–	–	–
10	${\mathrm{AE}}_{\max }\times {\mathrm{F}}_{\mathrm{y}}$	P12	0.0432	Significant	–	–	–	–
11	${\mathrm{AE}}_{\mathrm{rms}}\times {\mathrm{F}}_{\mathrm{y}}$	P14	0.332	Insignificant	–	–	–	–
Model			0.005	Significant R2: 92.34%; ${\mathrm{R}}_{\mathrm{adj}}^2$ : 83.36%	Model		0.0001	Significant R2: 93.98%; ${\mathrm{R}}_{\mathrm{adj}}^2$ : 86.64%

The input layer of multiple regression fitted models to estimate the thickness of EUMS burr in both tested materials is shown in Table 4. The resulted P-value, R² and ${\mathrm{R}}_{\mathrm{adj}}^2$ of constructed model for AA 2024-T351 are 0.1378, 98.05% and 81.41%, respectively. This states that although a high correlation exist between the predicted and experimental values (R² = 98.05% and ${\mathrm{R}}_{\mathrm{adj}}^2=81.41\mathrm{\%}$ ); however, the models may become more precise (P-value < 0.05) when one or more insignificant process variables which have the highest P-values (Figure 10) are removed from the input layer. Therefore, the feature selection approach was applied (Table 5) by removing five insignificant process variables. Other remained variables in input layer are statistically significant (P-value < 0.05). Consequently, increased ${\mathrm{R}}_{\mathrm{adj}}^2$ and decreased P-values in both materials were resulted (Tables 4 and 5). This indicates that the optimized fitted models are statistically significant, and a precise mathematical relationship can be formulated between EUMS burr thickness and signals information.

Figure 10.

Linear simple regression models of EUMS burr thickness in (a) AA 2024-T351 and (b) AA 6061-T6.

As shown in Table 5, the same procedure was applied on AA 6061-T6 results (see Table 2), and eight insignificant input variables were then removed from the input layer. This led to significant decrease in P-value and an increase in the ${\mathrm{R}}_{\mathrm{adj}}^2$ (Table 5), respectively. Consequently, 86.64% of variability in the fitted model can be resulted if the cutting parameters listed in Table 1 are used. Figure 10 presents the correlation between experimental and predicted values of EUMS burrs thickness in both investigated materials after feature selection. The constructed mathematical models in coded forms are also presented in equations (3) and (4).

Multiple regression fitted equation of EUMS burr thickness for AA 2024-T351

$$\begin{array}{cc}\multicolumn{1}{c}{Y=}& -0.221-(0.013\times ({\mathrm{P}}_2-{\mathrm{P}}_3))\hfill \\ \multicolumn{1}{c}{}& +(0.012\times {\mathrm{P}}_4)+(75.71\times {10}^{-7}\times {\mathrm{P}}_5)\hfill \\ \multicolumn{1}{c}{}& +(3.68\times {10}^{-4}\times {\mathrm{P}}_6))-(0.0004\times {\mathrm{P}}_7)\hfill \\ \multicolumn{1}{c}{}& -(0.001\times ({\mathrm{P}}_{10}-{\mathrm{P}}_{12}))-(0.004\times ({\mathrm{P}}_{14}-{\mathrm{P}}_8))\hfill \end{array}$$

(3)

Multiple regression fitted equation of EUMS burr thickness for AA 6061-T6

$$\begin{array}{cc}\multicolumn{1}{c}{Y=}& -0.065+(0.0028\times {\mathrm{P}}_3)-(88.18\times {10}^{-6}\times {\mathrm{P}}_5)\hfill \\ \multicolumn{1}{c}{}& +(0.001\times {\mathrm{P}}_8)-(0.25\times {\mathrm{P}}_9)+(0.10\times {\mathrm{P}}_{11})\hfill \\ \multicolumn{1}{c}{}& -(0.0006\times {\mathrm{P}}_{12})-(0.0033\times {\mathrm{P}}_{13})+(0.01\times {\mathrm{P}}_{14})\hfill \end{array}$$

(4)

EUMS burr height

The same procedure as presented earlier was applied on EUMS burr height results in both materials, and multiple regression fitted models were formulated after feature selection (Figure 11; equations (5) and (6)). The statistical summary of EUMS burr height results is presented in Appendix 3. According to Figure 6, feature selection in both materials led to increased ${\mathrm{R}}_{\mathrm{adj}}^2$ (70% and 94.57%) in both tested materials.

Figure 11.

Linear simple regression models of EUMS burr height in (a) AA 2024-T351 and (b) AA 6061-T6.

Multiple regression fitted equation of EUMS burr height for AA 2024-T351

$$\begin{array}{cc}\multicolumn{1}{c}{Y=}& -18.86+(0.418\times {\mathrm{P}}_2)+(0.8\times {\mathrm{P}}_4)\hfill \\ \multicolumn{1}{c}{}& -(9\times {10}^{-4}\times {\mathrm{P}}_5)-(0.03\times {\mathrm{P}}_6)+(0.049\times {\mathrm{P}}_7)\hfill \\ \multicolumn{1}{c}{}& +(1.16\times {\mathrm{P}}_8)+(38.70\times {\mathrm{P}}_9)-(0.04\times {\mathrm{P}}_{10})\hfill \\ \multicolumn{1}{c}{}& +(0.18\times {\mathrm{P}}_{13})-(0.8\times {\mathrm{P}}_{14})-(2.35\times {\mathrm{P}}_{15})\hfill & \hfill \end{array}$$

(5)

Multiple regression fitted equation of EUMS burr height for AA 6061-T6

$$\begin{array}{cc}\multicolumn{1}{c}{Y=}& 63.33+(0.795\times {\mathrm{P}}_2)-(1.11\times {\mathrm{P}}_3)-(2.68\times {\mathrm{P}}_4)\hfill \\ \multicolumn{1}{c}{}& -(5\times {10}^{-4}\times {\mathrm{P}}_5)+(0.05\times {\mathrm{P}}_6)-(0.04\times {\mathrm{P}}_7)\hfill \\ \multicolumn{1}{c}{}& +(0.96\times {\mathrm{P}}_8)-(114.953\times {\mathrm{P}}_9)-(45.61\times {\mathrm{P}}_{10})\hfill \\ \multicolumn{1}{c}{}& -(0.32\times {\mathrm{P}}_{10})+(0.64\times {\mathrm{P}}_{12})+(1.66\times {\mathrm{P}}_{13})\hfill \\ \multicolumn{1}{c}{}& -(1.81\times {\mathrm{P}}_{14})-(4.24\times {\mathrm{P}}_{15})\hfill & \hfill \end{array}$$

(6)

TUMS burrs

As presented in Figure 7, except the predicted fitted model of TUMS burr height in AA 6061-T6, other fitted models are statistically significant. This denotes that variation of TUMS and EUMS burrs sizes of AA 6061-T6 and AA 2024-T351 (Tables 4–7; Figures 12 and 13) can be controlled by computed information from AE and cutting forces signals. According to Liang and Dornfeld, ²⁹ the main sources of AE and cutting forces in cutting operations are plastic deformation in the workpiece, chip formation, frictional contact between the tool flank face and workpiece resulting in flank wear, frictional contact between tool face and chip resulting in crater wear, collisions between chip and tool, chip breakage and tool fracture. To investigate better the accuracy of the proposed method, the extracted signals information from cutting and exit stages are then combined to construct the input layer of multiple regression fitted models used to predict the size (thickness and height) of TDMS and EBS burrs (see Figures 14–17). As it is evident from Tables 8 and 9, except few cases, the fused signal parameters are sensitive to variation of TDMS and EBS burrs sizes. This denotes that the above-mentioned sources are relatively similar factors governing the burr formation morphology and size.

Table 6.

Statistical summary of ANOVA parameters of the EUMS burrs size models after feature selection.

Material	Response characteristics	ANOVA parameters
		P-value	R2 %	${\mathrm{R}}_{\mathrm{adj}}^2\%$	Remark
AA 2024-T351	EUMS burr height	0.037	89.37	70	Significant
	EUMS burr thickness	0.005	92.34	83.36	Significant
AA 6061-T6	EUMS burr height	0.013	99.04	94.54	Significant
	EUMS burr thickness	0.0001	93.98	86.64	Significant

ANOVA: analysis of variance; EUMS: exit burrs along up milling side.

Table 7.

Statistical summary of ANOVA parameters of TUMS burrs size models after feature selection.

Material	Response characteristics	ANOVA parameters
		P-value	R2 %	${\mathrm{R}}_{\mathrm{adj}}^2\%$	Remark
AA 2024-T351	TUMS burr height	0.0019	96.35	89.67	Significant
	TUMS burr thickness	0.0002	87.35	80.45	Significant
AA 6061-T6	TUMS burr height	0.23	71.31	30.33	Insignificant
	TUMS burr thickness	0.0029	90.68	80.21	Significant

ANOVA: analysis of variance; TUMS: top-up milling side.

Table 8.

Statistical summary of ANOVA parameters for EBS burrs size models after feature selection.

Material	Response characteristics	ANOVA parameters
		P-value	R2 %	${\mathrm{R}}_{\mathrm{adj}}^2\%$	Remark
AA 2024-T351	EBS burr height	0.003	93.46	84.13	Significant
	EBS burr thickness	0.471	73.18	8.81	Insignificant
AA 6061-T6	EBS burr height	0.045	97.74	87.21	Significant
	EBS burr thickness	0.093	74.29	45.37	Mid-significant

ANOVA: analysis of variance; EBS: exit bottom side.

Table 9.

Statistical summary of ANOVA parameters for TDMS burrs size models after feature selection.

Material	Response characteristics	ANOVA parameters
		P-value	R2 %	${\mathrm{R}}_{\mathrm{adj}}^2\%$	Remark
AA 2024-T351	TDMS burr thickness	0.089	69.68	42.54	Mid-significant
	TDMS burr height	0.012	93.01	80.17	Significant
AA 6061-T6	TDMS burr thickness	0.005	97.03	89.91	Significant
	TDMS burr height	0.019	97.08	87.60	Significant

ANOVA: analysis of variance; TDMS: top-down milling side.

Figure 12.

Linear simple regression models of TUMS burr thickness in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 13.

Linear simple regression models of TUMS burr height in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 14.

Linear simple regression models of TDMS burr thickness in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 15.

Linear simple regression models of TDMS burr height in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 16.

Linear simple regression models of EBS burr thickness in (a) AA 2024-T351 and (b) AA 6061-T6.

Figure 17.

Linear simple regression models of EBS burr height in (a) AA 2024-T351 and (b) AA 6061-T6.

Expect minor cases as shown in Tables 6–9, the results in this article confirm the reliability of the proposed methodology and reveal a strong correlation between the milling burrs sizes, AE and cutting force signal information.

In low-elastic modulus materials, such as AAs, lower magnitude of AE_rms is expected. ³² The low modulus of elasticity is a driver for vibration in machining operations, ³³ which has a negative effect on burr formation and burr size. Moreover, knowledge of material deformation properties (e.g. ductility) is also an important criterion for selecting better cutting parameters and predicting or modeling the burr size. In fact, burr size generally increases if ductile materials such as wrought aluminum alloys are used. This reveals that burr size prediction or estimation using signal information and modeling approaches (FEM, analytical, etc.) for ductile materials with low modulus of elasticity (e.g. AAs) is more complex. This denotes that although experimental verification was conducted on aluminum alloys, however, very good correlations were observed between the predicted (fitted) and experimental values of top and exit burrs size.

The findings in this work are in conflict with the critiques raised against the adequacy of AE signal information in milling operation. This denotes that the proposed method can be used as an alternative approach to detect burr formation and possibly estimate the size of top and exit burrs when adequate data acquisition and signal processing are applied. It is to underline that the signals acquired from machining centers (e.g. CNC) can contain certain levels of mechanical, electrical and acoustic noises. These signals can be highly affected by possible deviations in the system. Generally, more complex signals can be observed in milling than those in many other nontraditional machining operations. One solution is to use advanced filtering and anti-aliasing algorithms to suppress signal deviation. The proposed method could be further evaluated for automatic inspection and online burr size measurement.

Conclusion

The principle objective of this work was to present a methodology to evaluate the correlation between burr size attributes (thickness and height) and information computed from AE and cutting force signals.

Through presented results in this work, the following conclusions are drawn:

1. Due to noncontinuous mode of the cutting process and run-out effects in milling operations, the size of various burrs are not statistically correlated to each other. Moreover, it was observed that the size of milling burrs cannot be precisely formulated as a function of cutting parameters;

2. The proposed methodology is initially used to investigate the correlation between the sizes of TUMS and EUMS burrs (Figures 6 and 7) and computed information from AE and cutting force signals. Very good correlation was found between experimental and fitted values of TUMS and EUMS burrs (Figures 6 and 7) in both tested materials. It was also observed that the signal information is sensitive to the size of TDMS and EBS burrs (Figures 8 and 9), which are relatively small and thin milling burrs;

3. It is concluded that the computed information from multiple sensors, including AE and cutting force signals, contains useful information about machining process, tooling and material interactions to be useful for burr formation detection and burr size estimation.

This work can be later integrated into artificial intelligence (AI) techniques (e.g. neural network) to establish a practical burr size estimation model for real-time burr formation detection and burr size measurement. Furthermore, higher levels of frequency range along with advanced filtering and anti-aliasing algorithms are advised to record less distorted signals. To avoid large number of experimental tests, theoretical prediction of the cutting forces and AE signal parameters with respect to cutting conditions used is a suitable approach.