Analyze the Effect of Crater Cutting Tool Wear Modeling in the Machining of Aluminium Composite

Nagarajan, N.; Kamalakannan, R.

doi:10.1590/1980-5373-MR-2022-0239

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Article • Mat. Res. 25 • 2022 • https://doi.org/10.1590/1980-5373-MR-2022-0239 copy

Analyze the Effect of Crater Cutting Tool Wear Modeling in the Machining of Aluminium Composite

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The present investigation is used to analyze the effect of crater wear modeling in the machining of aluminium composite. Response surface methodology (RSM) is one of the best optimization technique used to bring out the optimal values of speed, feed and depth of cut for attain minimum surface roughness, cutting tool temperature and tool wear. AA7075 alloy with 15wt% of silicon carbide has been investigated in the dry machining condition. The double layered TiCN/Al₂O₃ coated tool was preferred. The worn surface analysis in coated tool was concentrated. The outcomes of the machining conditions were expected to improve surface finish. Novel approach of this investigation is to analyze the surface morphology to visualize the surface peak and valley profile of the coated tool and analyzed with various surface parameters that decide the surface roughness of the profile.

Keywords:
Aluminum; tool wear; cutting tool temperature; coating; surface morphology; surface roughness

Alloy	Zn	Mg	Cu	Cr	Si	Fe	Al
7075	5.62	2.52	1.63	0.22	0.06	0.18	89.77

Input variables	Level 1	Level 2	Level 3
Speed (m/min)	100	150	200
Feed (mm/rev)	0.15	0.20	0.25
Depth of cut (mm)	0.20	0.40	0.60

Corner radius	0.8 mm
Clearance angle in Degree	0⁰
Rake angle in Degree	6⁰
Cutting edge length	13 mm
Position angle (Kr)	45⁰

Experiment runs	Cutting speed (m/min)	Feed(mm/rev)	Depth of cut (mm)	Tool tip temperature (°C)		Tool wear in millimeters (mm)		surface roughness in microns ( μm)
Experiment runs	Cutting speed (m/min)	Feed(mm/rev)	Depth of cut (mm)	Exp	RSM	Exp	RSM	Exp	RSM
1	200	0.15	0.6	84.5	88	0.075	0.079	2.01	2.14
2	100	0.15	0.6	74.9	78	0.048	0.051	2.48	2.64
3	150	0.20	0.4	75.9	79.1	0.053	0.056	2.02	2.15
4	150	0.20	0.4	76.4	79.6	0.054	0.057	2.03	2.16
5	200	0.15	0.2	78.0	81.2	0.063	0.066	1.21	1.29
6	100	0.25	0.2	68.4	71.2	0.036	0.038	2.02	2.15
7	200	0.25	0.2	78.0	81.3	0.057	0.06	1.55	1.65
8	150	0.15	0.4	75.7	78.9	0.055	0.058	1.87	1.99
9	150	0.20	0.4	74.1	77.2	0.046	0.048	1.99	2.12
10	150	0.20	0.4	76.8	80.0	0.053	0.056	2.32	2.17
11	150	0.25	0.4	76.1	79.3	0.050	0.053	2.47	2.31
12	150	0.20	0.2	72.7	75.7	0.047	0.049	1.83	1.71
13	100	0.25	0.6	78.7	82.0	0.044	0.046	3.18	2.97
14	100	0.15	0.2	71.1	70.5	0.039	0.038	1.80	1.78
15	200	0.20	0.4	85.1	84.3	0.070	0.069	2.02	2.00
16	150	0.20	0.6	83.1	82.4	0.063	0.062	2.59	2.56
17	150	0.20	0.4	79.8	79.1	0.058	0.057	2.18	2.16
18	200	0.25	0.6	89.8	89.0	0.082	0.08	2.64	2.61
19	100	0.20	0.4	78.7	78.0	0.046	0.045	2.41	2.39
20	150	0.20	0.4	80.7	80.0	0.057	0.056	2.19	2.17

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	368.75	9	40.97	34.80	< 0.0001	significant
A-Cutting speed	194.48	1	194.48	165.17	< 0.0001	51%
B-Feed	3.84	1	3.84	3.26	0.1009	1%
C-Depth of cut	156.03	1	156.03	132.51	< 0.0001	41%
AB	1.62	1	1.62	1.38	0.2680
AC	1.81	1	1.81	1.53	0.2439
BC	2.21	1	2.21	1.87	0.2011
A²	7.82	1	7.82	6.64	0.0275
B²	0.36	1	0.36	0.30	0.5906
C²	0.47	1	0.47	0.39	0.5415
Residual	11.77	10	1.18
Lack of Fit	6.32	5	1.26	1.16	0.4376	not significant
Pure Error	5.45	5	1.09
Cor Total	380.53	19		R²= 96.9	Adj R² =94.12	Predicted R²=82.68%

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	0.2360	9	0.0262	30.06	< 0.0001	significant
A-Cutting speed	0.1850	1	0.1850	212.00	< 0.0001	75%
B-Feed	0.0023	1	0.0023	2.58	0.1394	1%
C-Depth of cut	0.0449	1	0.0449	51.45	< 0.0001	18%
AB	0.0000	1	0.0000	0.0000	1.0000
AC	0.0018	1	0.0018	2.0600	0.1814
BC	0.0001	1	0.0001	0.0573	0.8156
A²	0.0008	1	0.0008	0.9404	0.3550
B²	0.0000	1	0.0000	0.0163	0.9010
C²	0.0000	1	0.0000	0.0163	0.9010
Residual	0.0087	10	0.0009
Lack of Fit	0.0027	5	0.0005	0.4541	0.7967	not significant
Pure Error	0.0060	5	0.0012
Cor Total	0.2447	19		R²= 96.4	Adj R² =93.3	Predicted R²=76.75%

Source	Sum of Squares	df	Mean Square	F-value	p-value
Model	2.74	9	0.3040	355.80	< 0.0001	significant
A-Cutting speed	0.5018	1	0.5018	587.18	< 0.0001	18.3%
B-Feed	0.3422	1	0.3422	400.52	< 0.0001	12%
C-Depth of cut	1.88	1	1.88	2204.22	< 0.0001	69%
AB	0.0021	1	0.0021	2.47	0.1470
AC	0.0021	1	0.0021	2.47	0.1470
BC	0.0006	1	0.0006	0.71	0.4170
A²	0.0032	1	0.0032	3.74	0.0819
B²	0.0003	1	0.0003	0.38	0.5498
C²	0.0018	1	0.0018	2.16	0.1724
Residual	0.0085	10	0.0009
Lack of Fit	0.0068	5	0.0014	3.88	0.0814	not significant
Pure Error	0.0017	5	0.0003
Cor Total	2.74	19		R²= 98.41	Adj R² =96.41	Predicted R²=96.4%

Response	Constraint	Lower bound	Upper bound	Lower weight	Upper weight	Importance
Cutting speed	In range	100	200	1	1	3
Feed	In range	0.15	0.25	1	1	3
Depth of cut	In range	0.20	0.60	1	1	3
Tool tip temperature	Minimize	70.50	89.0	1	1	3
Tool wear	Minimize	0.03	0.08	1	1	3
Surface roughness	Minimize	1.29	2.97	1	1	3

Number	Cutting speed(m/min)	Feed (mm/rev)	Depth of cut(mm)	Tool tip temperature Deg c	Tool wear (mm)	surface roughness microns	Desirability
1	100.000	0.150	0.200	70.815	0.0402	1.801	0.865	Selected
2	100.399	0.150	0.200	70.830	0.0403	1.798	0.865
3	101.166	0.150	0.200	70.863	0.0404	1.794	0.865
4	101.891	0.150	0.200	70.891	0.0406	1.789	0.865
5	102.589	0.150	0.200	70.921	0.0407	1.784	0.864

S.No	Surface parameters	Cutting speed of 200m/min, feed rate of 0.15 mm/rev,and 0.6mm depth of cut	Cutting speed of 150m/min, feed rate of 0.2 mm/rev, and 0.6mm depth of cut	Cutting speed of 100m/min, feed rate of 0.15 mm/rev, and 0.6mm depth of cut
1	Roughness Average (R_a)	51.32	16.59	30.90
2	Maximum height of the roughness (R_t)	570	206.6	187.6
3	Root mean square	77.96	24.52	39.96
3	Maximum roughness Valley depth (R_v)	227.1	105.8	73.26
4	Maximum roughness Peak height (R_p)	153.2	45.70	78.43
5	Skewness (R_sk)	0.6780	0.3505	0.3924
6	Kurtosis (R_ku)	7.164	7.261	3.072

Response variables	Optimal values			Predicted values by desirability approach	Experimental value	Percentage of deviation
Response variables	Cutting speed (m/min)	Feed (mm/rev)	Depth of cut (mm)	Predicted values by desirability approach	Experimental value	Percentage of deviation
Tool tip temperature (⁰C)	100	0.15	0.20	70.81	72.0	1.65
Surface roughness (μm) in microns				1.80	1.71	5.00
Tool wear (μm) in microns				0.04	0.04	4.70

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