Soft computing approach for optimization of turning characteristics of elastomers under different lubrication conditions

Figures & data

Figure 1. Workpiece materials.

Figure 2. Microstructure of workpiece materials (a) Nitrile Rubber (b) Polyurethane Rubber (c) Neoprene Rubber.

Table 1. Specifications of the cutting tool

Nomenclature	Specification
Tool Material	Solid Carbide
Tool Type	G (Round cutting edge) [ISO]
Insert Clamping Type	M (Pin/Clamp) [ISO]
Insert Shape	T (60°) [ISO]
Cutting Direction	Right
Entry angle	10°
Rake angle	20°
Clearance angle (side)	5°
Clearance angle (end)	10°
Nose Radius	0.4mm

Figure 3. Cryogenic cooling assisted machining setup for turning elastomers.

Figure 4. Architecture of BPANN model.

Figure 5. Python sample code for BPANN implementation.

Table 2. Statistical data for selection of learning algorithm

Learning algorithm	Neuron count	Training data			Test data
		RMSE	R^2	MEP	RMSE	R^2	MEP
LM	9	0.006715	0.999762	1.003417	0.041524	0.996871	3.356467
LM	18	0.004421	0.999921	0.945115	0.021648	0.999214	2.245142
LM	27	0.002122	0.999955	0.716488	0.015466	0.999446	3.451834
GD	9	0.011465	0.999121	1.346469	0.112461	0.992411	2.548926
GD	18	0.009195	0.999546	1.321149	0.012199	0.991205	4.224556
GD	27	0.007516	0.999726	1.347295	0.009954	0.993549	5.112465
SCG	9	0.032566	0.991511	2.201564	0.004651	0.992144	4.216414
SCG	18	0.052522	0.992155	2.106498	0.002246	0.993472	3.958655
SCG	27	0.009144	0.991376	1.998719	0.004694	0.998713	3.567891

Figure 6. Cutting force v/s lubrication conditions (constant cutting speed– 55m/min).

Figure 7. Cutting force v/s lubrication conditions (constant cutting speed– 90m/min).

Figure 8. Cutting force v/s lubrication conditions (constant cutting speed − 125m/min).

Figure 10. Microscopic image of PU elastomer material under (125 m/min) cutting speed, (0.18mm/rev) feed, (0.50 mm) depth of cut and LN₂ conditions.

Figure 11. Cutting force (N) signals for PU elastomer material under (125m/min)cutting speed, (0.18mm/rev)feed, (0.50mm)depth of cut and LN₂ conditions.

Figure 9. Main effects plot for SN ratios of cutting force.

Table 3. ANOVA for cutting force (N)

Source	DF	Seq SS	Adj SS	Adj MS	F	P	P%
A	2	176.15	176.15	138.07	138.151	.262	9.34
B	2	111.81	111.81	110.90	121.256	.115	4.099
C	2	134.61	134.61	112.30	113.51	.106	3.77
D	2	4264.54	4264.54	2132.27	2234.11	2.217	79.03
E	2	138.21	138.21	169.11	158.4	.099	3.53
A*E	4	3.66	3.66	0.92	1.21	.002	.07
B*E	4	3.11	3.11	0.78	1.48	.001	.03
C*E	4	1.70	1.70	0.42	1.85	.001	.03
D*E	4	5.32	5.32	1.33	1.22	.002	.07
Residual Error	0	11.256	11.256	1.215
Total	26	4850.366				2.805	100

where A—Cutting Speed (m/min), B—Feed (mm/rev), C—Depth of Cut (mm), D—Lubrication Conditions, E—Elastomer Material.

Figure 12. Surface roughness v/s lubrication conditions (constant cutting speed– 55m/min).

Figure 13. Surface roughness v/s lubrication conditions (constant cutting speed– 90m/min).

Figure 14. Surface roughness v/s lubrication conditions (constant cutting speed- 125m/min).

Figure 16. Microstructure of turned surfaces of (a) Nitrile Rubber (NBR) (b) Polyurethane Rubber (PU) (c) Neoprene Rubber (CR) under (125m/min)cutting speed, (0.18mm/rev)feed, (0.50mm)depth of cut and LN₂ cooling conditions.

Figure 15. Main effects plot for SN ratios of surface roughness.

Table 4. ANOVA for surface roughness

Source	DF	Seq SS	Adj SS	Adj MS	F	P	P%
A	2	0.2166	0.2166	0.1183	1.126	.215	1.99
B	2	0.459	0.459	0.2295	2.217	.116	5.92
C	2	1.2265	1.2265	0.623	1.958	.336	17.169
D	2	22.56	22.56	11.55	35.521	1.059	54.113
E	2	1.4656	1.4656	0.8982	0.7846	.212	1.83
A*E	4	0.0456	0.0456	0.0023	0.0012	.003	.15
B*E	4	0.0214	0.0214	0.0112	0.0016	.002	.1
C*E	4	0.0369	0.0369	0.0156	0.0045	.002	.1
D*E	4	1.148	1.148	0.075	0.078	.012	.613
Residual Error	0	0.0056	0.0056
Total	26	27.1852					1.00

where A—Cutting Speed (m/min), B—Feed (mm/rev), C—Depth of Cut (mm), D—Lubrication Conditions, E—Elastomer Material.

Table 5. Training set for BPANN

Trial No.	A	B	C	D	E	Cutting force (N) (experimental)	Surface roughness (microns) (experimental)
1	55	0.11	0.25	Dry	NBR	85.1	3.81
2	55	0.11	0.25	Dry	CR	8.3	3.80
3	55	0.18	0.50	TFE	PU	58.1	3.57
4	55	0.18	0.50	TFE	CR	6.3	3.61
5	55	0.25	0.75	LN2	NBR	52.4	3.35
6	55	0.25	0.75	LN2	PU	45.6	3.21
7	90	0.11	0.50	LN2	NBR	5.3	3.24
8	90	0.11	0.50	LN2	PU	42.5	3.18
9	90	0.18	0.75	Dry	NBR	82.6	3.77
10	90	0.18	0.75	Dry	CR	77.5	3.75
11	90	0.25	0.25	TFE	PU	55.8	3.56
12	90	0.25	0.25	TFE	CR	59.1	3.59
13	125	0.11	0.75	TFE	PU	53.6	3.55
14	125	0.11	0.75	TFE	CR	6.2	3.59
15	125	0.18	0.25	LN2	NBR	48.6	3.15
16	125	0.18	0.25	LN2	PU	41.3	3.09
17	125	0.25	0.50	Dry	NBR	79.5	3.71
18	125	0.25	0.50	Dry	CR	74.4	3.67

Table 6. Test set for BPANN prediction

Sl No.	A	B	C	D	E	Cutting force (N) (experimental)	Cutting force (N) (predicted)	% Error	Surface roughness (microns) (experimental)	Surface roughness (microns) (predicted)	% Error
1	55	0.11	0.25	Dry	PU	75.1	74.9	0.26	3.72	3.77	1.34
2	55	0.18	0.50	TFE	NBR	66.5	64.9	2.40	3.65	3.68	0.82
3	55	0.25	0.75	LN2	CR	48.2	47.6	1.24	3.29	3.31	0.60
4	90	0.11	0.50	LN2	CR	46.3	45.2	2.37	3.20	3.18	0.62
5	90	0.18	0.75	Dry	PU	72.7	7.5	3.02	3.71	3.69	0.53
6	90	0.25	0.25	TFE	NBR	65.2	66.4	1.84	3.64	3.69	1.37
7	125	0.11	0.75	TFE	NBR	62.1	63.7	2.57	3.60	3.66	1.66
8	125	0.18	0.25	LN2	CR	45.3	46.3	2.20	3.11	3.15	1.28
9	125	0.25	0.50	Dry	PU	69.6	7.2	0.86	3.60	3.62	0.55

where A—Cutting Speed (m/min), B—Feed (mm/rev), C—Depth of Cut (mm), D—Lubrication Conditions, E—Elastomer Material.

Table 7. Observations of output response

Itinerary	Description
Configuration of the network	5-27-2
Hidden Layer	1
Hidden neurons	27
Applied transfer function	“Logsig (sigmoid)”
Training pattern count	20
Testing pattern count	7
Epoch count	5000
Learning Rate	.7
(α) Factor of momentum	.9

Figure 17. Experimental values compared to BPANN predicted cutting force (N).

Figure 18. Experimental values compared to BPANN predicted surface roughness (microns).

Figure 19. Chips formed during turning of elastomers under different cooling conditions: (a) Dry, (b) TFE, (c) LN₂.