Research Article

Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites

Table 2

Dataset used NN model.

ReferenceUHMWPEZnO
(wt.%)
Zeolite
(wt.%)
CNT
(wt.%)
CF
(wt.%)
GO
(wt.%)
Wollastonite
(wt.%)
Load
(N)
Sliding speed
(m/s)
Volume loss
(mm3)

Chang et al. [44]100100,0335,699
955100,0333,093
9010100,0332,075
8515100,0332,329
8020100,0332,413
955100,0333,781
9010100,0332,218
8515100,0331,962
8020100,0332,091
100200,03313,333
955200,0336,101
9010200,0336,440
8515200,0336,927
8020200,0336,541
955200,0336,358
9010200,0334,150
8515200,0334,965
8020200,0334,611
100300,03315,914
955300,03310,225
9010300,0339,159
8515300,0338,582
8020300,0337,613
955300,0337,905
9010300,0334,722
8515300,0337,172
8020300,0334,986
100100,36822,151
955100,36814,435
9010100,36813,094
8515100,36812,566
8020100,36810,723
955100,36814,092
9010100,3689,874
8515100,36812,382
8020100,3688,096
100200,36827,742
955200,36818,818
9010200,36817,029
8515200,36815,631
8020200,36813,993
955200,36816,326
9010200,36812,665
8515200,36813,669
8020200,36811,312
100300,36829,032
955300,36822,255
9010300,36817,816
8515300,36817,347
8020300,36815,923
955300,36817,271
9010300,36813,309
8515300,36815,324
8020300,36812,867
100101,02223,226
955101,02214,435
9010101,02213,952
8515101,02210,482
8020101,0229,543
955101,02214,350
9010101,02213,666
8515101,02215,018
8020101,0229,704
100201,02227,527
955201,02231,277
9010201,02222,682
8515201,02220,657
8020201,02218,818
955201,02226,895
9010201,02222,825
8515201,02224,458
8020201,02216,942
100301,02253,333
955301,02284,207
9010301,02258,958
8515301,02260,500
8020301,02258,171
955301,02242,361
9010301,02239,067
8515301,02230,465
8020301,02233,348

Chang et al. [45]100100,2092,800
9010100,2092,200
8020100,2092,200
100200,2095,000
9010200,2093,600
8020200,2093,500
100300,2095,900
9010300,2094,700
8020300,2094,700
100100,4194,700
9010100,4193,900
8020100,4193,100
100200,4196,500
9010200,4196,000
8020200,4195,200
100300,4198,100
9010300,4197,900
8020300,4196,200
100100,8388,000
9010100,8385,900
8020100,8385,000
100200,8389,800
9010200,8389,000
8020200,8387,000
100300,83811,000
9010300,83810,500
8020300,8388,900

Zoo et al. [18]10050,3000,376
99,90,150,3000,268
99,80,250,3000,134
99,50,550,3000,021

Dangsheng [14]1001960,4201,820
9551960,4201,400
90101960,4201,350
85151960,4201,110
80201960,4200,730
70301960,4200,590

Tai et al. [46]10050,0900,043
99,90,150,0900,038
99,70,350,0900,033
99,30,750,0900,028
99150,0900,028
98250,0900,026
97350,0900,025

Tong et al. [47]1001200,5301,73
9551200,5301,31
90101200,5301,2
85151200,5301,58
80201200,5301,8
9010400,5300,25
9010800,5300,7
90101600,5302,28