Optimization of joining HDPE rods by continuous drive friction welding

Mohammed A. Tashkandi; Nidhal M. Becheikh

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Optimization of joining HDPE rods by continuous drive friction welding

Mohammed A. Tashkandi

und

Nidhal M. Becheikh

| 13. Okt. 2022

Materials Science-Poland

Band 40 (2022): Heft 2 (August 2022)

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Online veröffentlicht: 13. Okt. 2022

Seitenbereich: 240 - 256

Eingereicht: 13. Jan. 2022

Akzeptiert: 30. Mai 2022

DOI: https://doi.org/10.2478/msp-2022-0017

Schlüsselwörter
continuous drive friction welding, RSM, HDPE, welding temperature, axial shortening, tensile strength

© 2022 Mohammed A. Tashkandi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CDFW machine setup. CDFW, continuous drive friction welding

HDPE rods joined using CDFW according to different welding conditions. CDFW, continuous drive friction welding; HDPE, high-density polyethylene

Data normality check using the normal probability plot, the versus fits, the histogram, and the versus order for Tmax

3D surface and contour plots for the maximum welding temperature. The maximum reported values are actual measured values for each speed level

Data normality check using the normal probability plot, the versus fits, the histogram, and the versus order for the axial shortening

3D surface and contour plots for the axial shortening according to RS's. The maximum reported values are actual measured values for each speed level

Data normality check using the normal probability plot, the versus fits, the histogram, and the versus order for the TS. TS, tensile strength

Pareto chart of the standardized effects for the TS of the joints. TS, tensile strength

Surface and contour plots of the TS as a function of tf and Ff. The maximum reported values are actual measured values for each speed level

Effect of process parameters on the appearance of welded joints arranged according to axial shortening from minimum to maximum

Regression equation in coded parameters for the axial shortening

RS (rpm)	Regression equation
082	$Short = 1.06 - 0.0717 t_{f} + 0.00064 F_{f} + 0.000057 t_{f}^{2} - 0.000001 F_{f}^{2} + 0.000057 t_{f} \times F_{f}$ {\rm{Short}} = 1.06 - 0.0717{t_f} + 0.00064{F_f} + 0.000057t_f^2 - 0.000001F_f^2 + 0.000057{t_f} \times {F_f}
165	$Short = - 0.60 - 0.0400 t_{f} + 0.00147 F_{f} + 0.000057 t_{f}^{2} - 0.000001 F_{f}^{2} + 0.000057 t_{f} \times F_{f}$ {\rm{Short}} = - 0.60 - 0.0400{t_f} + 0.00147{F_f} + 0.000057t_f^2 - 0.000001F_f^2 + 0.000057{t_f} \times {F_f}
300	$Short = - 2.43 + 0.0312 t_{f} + 0.00183 F_{f} + 0.000057 t_{f}^{2} - 0.000001 F_{f}^{2} + 0.000057 t_{f} \times F_{f}$ {\rm{Short}} = - 2.43 + 0.0312{t_f} + 0.00183{F_f} + 0.000057t_f^2 - 0.000001F_f^2 + 0.000057{t_f} \times {F_f}
400	$Short = - 1.75 + 0.0494 t_{f} + 0.00141 F_{f} + 0.000057 t_{f}^{2} - 0.000001 F_{f}^{2} + 0.000057 t_{f} \times F_{f}$ {\rm{Short}} = - 1.75 + 0.0494{t_f} + 0.00141{F_f} + 0.000057t_f^2 - 0.000001F_f^2 + 0.000057{t_f} \times {F_f}
550	$Short = - 4.07 + 0.1086 t_{f} + 0.00290 F_{f} + 0.000057 t_{f}^{2} - 0.000001 F_{f}^{2} + 0.000057 t_{f} \times F_{f}$ {\rm{Short}} = - 4.07 + 0.1086{t_f} + 0.00290{F_f} + 0.000057t_f^2 - 0.000001F_f^2 + 0.000057{t_f} \times {F_f}

Regression equations for predicting the TS

RS (rpm)	Regression equation
082	$TS = - 10.61 + 0.345 t_{f} + 0.00959 F_{f} - 0.00159 t_{f}^{2} - 0.000002 F_{f}^{2} - 0.000051 t_{f} \times F_{f}$ {\rm{TS}} = - 10.61 + 0.345{t_f} + 0.00959{F_f} - 0.00159t_f^2 - 0.000002F_f^2 - 0.000051{t_f} \times {F_f}
165	$TS = 3.10 + 0.256 t_{f} + 0.00532 F_{f} - 0.00159 t_{f}^{2} - 0.000002 F_{f}^{2} - 0.000051 t_{f} \times F_{f}$ {\rm{TS}} = 3.10 + 0.256{t_f} + 0.00532{F_f} - 0.00159t_f^2 - 0.000002F_f^2 - 0.000051{t_f} \times {F_f}
300	$TS = - 2.41 + 0.224 t_{f} + 0.01012 F_{f} - 0.00159 t_{f}^{2} - 0.000002 F_{f}^{2} - 0.000051 t_{f} \times F_{f}$ {\rm{TS}} = - 2.41 + 0.224{t_f} + 0.01012{F_f} - 0.00159t_f^2 - 0.000002F_f^2 - 0.000051{t_f} \times {F_f}
400	$TS = 3.13 + 0.131 t_{f} + 0.00842 F_{f} - 0.00159 t_{f}^{2} - 0.000002 F_{f}^{2} - 0.000051 t_{f} \times F_{f}$ {\rm{TS}} = 3.13 + 0.131{t_f} + 0.00842{F_f} - 0.00159t_f^2 - 0.000002F_f^2 - 0.000051{t_f} \times {F_f}
550	$TS = - 4.04 + 0.242 t_{f} + 0.00859 F_{f} - 0.00159 t_{f}^{2} - 0.000002 F_{f}^{2} - 0.000051 t_{f} \times F_{f}$ {\rm{TS}} = - 4.04 + 0.242{t_f} + 0.00859{F_f} - 0.00159t_f^2 - 0.000002F_f^2 - 0.000051{t_f} \times {F_f}

Analysis of variance for the axial shortening

Source	DF	Adj SS	Adj MS	F-Value	P-Value
Model	17	492.737	28.9845	174.17	0.000
Linear	6	445.319	74.2199	446.00	0.000
t_f	1	97.663	97.6626	586.87	0.000
F_f	1	30.961	30.9606	186.05	0.000
RS	4	316.696	79.1740	475.77	0.000
Square	2	1.404	0.7021	4.22	0.021
$t_{f}^{2}$ t_f^2	1	0.006	0.0058	0.03	0.853
$F_{f}^{2}$ F_f^2	1	1.352	1.3516	8.12	0.006
2-Way Interaction	9	46.014	5.1126	30.72	0.000
t_f*F_f	1	3.486	3.4861	20.95	0.000
t_f*RS	4	37.344	9.3360	56.10	0.000
F_f*RS	4	5.184	1.2959	7.79	0.000
Error	47	7.821	0.1664
Lack-of-Fit	27	5.567	0.2062	1.83	0.084
Pure Error	20	2.254	0.1127
Total	64	500.559

Regression equation in coded parameters

RS	Regression equation
082	$T_{\max} = 9.91 + 1.003 t_{f} + 0.02847 F_{f} - 0.00481 t_{f}^{2} - 0.000006 F_{f}^{2} - 0.000130 t_{f} \times F_{f}$ {T_{max}} = 9.91 + 1.003{t_f} + 0.02847{F_f} - 0.00481t_f^2 - 0.000006F_f^2 - 0.000130{t_f} \times {F_f}
165	$T_{\max} = 16.14 + 0.998 t_{f} + 0.03025 F_{f} - 0.00481 t_{f}^{2} - 0.000006 F_{f}^{2} - 0.000130 t_{f} \times F_{f}$ {T_{max}} = 16.14 + 0.998{t_f} + 0.03025{F_f} - 0.00481t_f^2 - 0.000006F_f^2 - 0.000130{t_f} \times {F_f}
300	$T_{\max} = 21.83 + 1.070 t_{f} + 0.02963 F_{f} - 0.00481 t_{f}^{2} - 0.000006 F_{f}^{2} - 0.000130 t_{f} \times F_{f}$ {T_{max}} = 21.83 + 1.070{t_f} + 0.02963{F_f} - 0.00481t_f^2 - 0.000006F_f^2 - 0.000130{t_f} \times {F_f}
400	$T_{\max} = 22.80 + 1.111 t_{f} + 0.02948 F_{f} - 0.00481 t_{f}^{2} - 0.000006 F_{f}^{2} - 0.000130 t_{f} \times F_{f}$ {T_{max}} = 22.80 + 1.111{t_f} + 0.02948{F_f} - 0.00481t_f^2 - 0.000006F_f^2 - 0.000130{t_f} \times {F_f}
550	$T_{\max} = 35.17 + 0.988 t_{f} + 0.02932 F_{f} - 0.00481 t_{f}^{2} - 0.000006 F_{f}^{2} - 0.000130 t_{f} \times F_{f}$ {T_{max}} = 35.17 + 0.988{t_f} + 0.02932{F_f} - 0.00481t_f^2 - 0.000006F_f^2 - 0.000130{t_f} \times {F_f}

Levels of process parameters for CDFW of HDPE

Parameter	Code	Levels
RS (rpm)	Speed	82 – 169 – 300 – 400 – 550
Friction force – F_f (N)	Force	1,000–2,000
Friction time – t_f (s)	Time	30–60

ANOVA for TS

Source	DF	Adj SS	Adj MS	F-Value	P-Value
Model	17	243.893	14.347	4.34	0.000
Linear	6	163.399	27.233	8.25	0.000
t_f	1	4.031	4.031	1.22	0.275
F_f	1	17.543	17.543	5.31	0.026
RS	4	141.825	35.456	10.74	0.000
Square	2	8.616	4.308	1.30	0.281
$t_{f}^{2}$ t_f^2	1	4.475	4.475	1.36	0.250
$F_{f}^{2}$ F_f^2	1	5.260	5.260	1.59	0.213
2-Way Interaction	9	71.878	7.986	2.42	0.024
t_f *F_f	1	2.833	2.833	0.86	0.359
t_f *RS	4	42.229	10.557	3.20	0.021
F_f *RS	4	26.816	6.704	2.03	0.105
Error	47	155.205	3.302
Lack-of-Fit	27	108.605	4.022	1.73	0.106
Pure Error	20	46.600	2.330
Total	64	399.098

j.msp-2022-0017.apptab.001

Run	Maximum temperature (°C)	Axial shortening (mm)	Tensile strength (MPa)	Run	Maximum temperature (°C)	Axial shortening (mm)	Tensile strength (MPa)
1	56.3	0.15	4.450	34	86.8	4.45	14.910
2	62.4	0.20	7.102	35	81.9	3.55	12.514
3	60.8	0.65	7.800	36	82.0	3.80	12.790
4	72.3	1.65	9.390	37	83.0	4.00	14.370
5	53.3	0.30	5.510	38	84.0	3.70	14.150
6	73.0	1.00	13.260	39	80.5	3.55	11.830
7	58.0	0.15	5.320	40	73.2	2.15	10.870
8	64.5	0.95	8.356	41	95.9	6.20	10.500
9	69.1	0.75	9.320	42	80.6	2.35	9.040
10	64.4	0.55	8.330	43	91.3	8.15	9.090
11	68.6	0.70	9.250	44	76.4	1.95	14.300
12	72.4	2.00	11.110	45	94.1	6.70	7.100
13	68.4	0.90	10.930	46	73.3	2.55	7.390
14	61.0	0.50	13.030	47	90.8	5.35	13.470
15	76.0	0.75	13.550	48	85.2	4.85	10.500
16	68.8	1.35	11.290	49	79.9	4.80	10.160
17	81.8	3.40	11.520	50	85.7	4.85	13.570
18	67.1	0.75	11.910	51	82.7	4.40	12.110
19	79.0	3.25	14.570	52	84.5	4.75	13.060
20	67.9	0.55	13.620	53	81.8	3.20	7.490
21	79.9	2.55	11.450	54	92.6	8.00	8.330
22	74.9	2.05	11.240	55	86.8	5.10	7.900
23	75.4	1.65	13.410	56	95.0	12.80	9.890
24	69.2	1.90	10.820	57	79.6	3.00	8.290
25	77.9	1.90	10.340	58	97.0	10.90	8.270
26	76.5	2.10	7.730	59	83.8	5.10	9.120
27	69.7	1.15	7.540	60	97.6	8.80	11.980
28	87.0	4.05	12.285	61	94.9	6.90	8.980
29	79.0	2.60	13.500	62	92.5	6.60	6.140
30	88.3	6.45	10.500	63	92.2	7.00	9.970
31	72.0	1.55	11.560	64	94.1	7.55	9.930
32	91.0	6.95	10.810	65	91.1	6.90	9.650
33	74.4	1.70	9.360

Physical and mechanical properties of HDPE [23]

Property	Value
Melting temperature (°C)	126–135
Crystallization temperature (°C)	111.9
Density (g/cm³)	0.955
Thermal conductivity (W/mK)	0.35–0.49
Specific heat – solid (kJ/kg°C)	1.9
Tensile strength (MPa) at 23°C	23.0–29.5

Analysis of variance

Source	DF	Adj SS	Adj MS	F-Value	P-Value
Model	17	7297.99	429.29	50.12	0.000
Linear	6	7162.50	1193.75	139.37	0.000
t_f	1	1510.42	1510.42	176.34	0.000
F_f	1	467.61	467.61	54.59	0.000
RS	4	5184.47	1296.12	151.32	0.000
Square	2	92.92	46.46	5.42	0.008
$t_{f}^{2}$ t_f^2	1	40.66	40.66	4.75	0.034
$F_{f}^{2}$ F_f^2	1	63.98	63.98	7.47	0.009
2-Way Interaction	9	42.58	4.73	0.55	0.828
t_f*F_f	1	18.43	18.43	2.15	0.149
t_f*RS	4	20.98	5.24	0.61	0.656
F_f*RS	4	3.17	0.79	0.09	0.984
Error	47	402.56	8.57
Lack-of-Fit	27	287.51	10.65	1.85	0.080
Pure Error	20	115.06	5.75
Total	64	7700.56

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Optimization of joining HDPE rods by continuous drive friction welding

Online veröffentlicht: 13. Okt. 2022

Seitenbereich: 240 - 256

Eingereicht: 13. Jan. 2022

Akzeptiert: 30. Mai 2022

DOI: https://doi.org/10.2478/msp-2022-0017

Schlüsselwörter
continuous drive friction welding, RSM, HDPE, welding temperature, axial shortening, tensile strength

© 2022 Mohammed A. Tashkandi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Regression equation in coded parameters for the axial shortening

Regression equations for predicting the TS

Analysis of variance for the axial shortening

Regression equation in coded parameters

Levels of process parameters for CDFW of HDPE

ANOVA for TS

j.msp-2022-0017.apptab.001

Physical and mechanical properties of HDPE [23]

Analysis of variance

Optimization of joining HDPE rods by continuous drive friction welding

Online veröffentlicht: 13. Okt. 2022

Seitenbereich: 240 - 256

Eingereicht: 13. Jan. 2022

Akzeptiert: 30. Mai 2022

DOI: https://doi.org/10.2478/msp-2022-0017

Schlüsselwörtercontinuous drive friction welding, RSM, HDPE, welding temperature, axial shortening, tensile strength

© 2022 Mohammed A. Tashkandi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Regression equation in coded parameters for the axial shortening

Regression equations for predicting the TS

Analysis of variance for the axial shortening

Regression equation in coded parameters

Levels of process parameters for CDFW of HDPE

ANOVA for TS

j.msp-2022-0017.apptab.001

Physical and mechanical properties of HDPE [23]

Analysis of variance

Schlüsselwörter
continuous drive friction welding, RSM, HDPE, welding temperature, axial shortening, tensile strength