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Estimation of friction and wear properties of additively manufactured recycled-ABS parts using artificial neural network approach: effects of layer thickness, infill rate, and building direction

  • Çağın Bolat , Abdulkadir Çebi EMAIL logo , Sarp Çoban and Berkay Ergene
Published/Copyright: April 24, 2024
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International Polymer Processing
From the journal International Polymer Processing Volume 39 Issue 3

Abstract

This investigation aims to elucidate friction and wear features of additively manufactured recycled-ABS components by utilizing neural network algorithms. In that sense, it is the first initiative in the technical literature and brings fused deposition modeling (FDM) technology, recycled filament-based products, and artificial neural network strategies together to estimate the friction coefficient and volume loss outcomes. In the experimental stage, to provide the required data for five different neural algorithms, dry-sliding wear tests, and hardness measurements were conducted. As FDM printing variables, layer thickness (0.1, 0.2, and 0.3 mm), infill rate (40, 70, and 100 %), and building direction (vertical, and horizontal) were selected. The obtained results pointed out that vertically built samples usually had lower wear resistance than the horizontally built samples. This case can be clarified with the initially measured hardness levels of horizontally built samples and optical microscopic analyses. Besides, the Levenberg Marquard (LM) algorithm was the best option to foresee the wear outputs compared to other approaches. Considering all error levels in this paper, the offered results by neural networks are notably acceptable for the real industrial usage of material, mechanical, and manufacturing engineering areas.

Keywords: recycled filament; fused deposition modeling; friction coefficient; wear resistance; artificial neural network
Corresponding author: Abdulkadir Çebi, Department of Mechanical Engineering, Engineering Faculty, Samsun University, 55420, Samsun, Türkiye, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: CB: Supervisor, Visualization, Writing and Editing; AC: Writing, Editing and Investigation; SC: Neural Network and Writing; BE: Supervisor, Visualization, Writing and Editing. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: The raw data can be obtained on request from the corresponding author.

Appendix

Table A.1: RMSE, MAE, R2 and MEP values for the LM, BR, SCG, BFG and RP algorithms.

Algorithm # of neurons Training Testing
RMSE MAE R 2 MEP (%) RMSE MAE R 2 MEP (%)
LM 5 0.0063 0.0043 0.9996 4.2921 0.0070 0.0045 0.9995 4.7965
LM 6 0.0060 0.0043 0.9996 4.2980 0.0059 0.0044 0.9996 4.3994
LM 7 0.0053 0.0036 0.9997 3.7009 0.0070 0.0045 0.9995 4.2901
LM 8 0.0055 0.0038 0.9997 3.7580 0.0063 0.0044 0.9995 4.4359
LM 9 0.0057 0.0038 0.9996 3.8296 0.0050 0.0034 0.9997 3.3522
LM 10 0.0055 0.0037 0.9997 3.7246 0.0063 0.0044 0.9996 4.2928
BR 5 0.0063 0.0046 0.9996 4.6419 0.0062 0.0046 0.9996 4.9651
BR 6 0.0060 0.0044 0.9996 4.3576 0.0050 0.0037 0.9997 3.7443
BR 7 0.0056 0.0039 0.9996 3.9302 0.0063 0.0039 0.9996 3.9738
BR 8 0.0054 0.0035 0.9997 3.5548 0.0066 0.0048 0.9995 4.5445
BR 9 0.0055 0.0038 0.9997 3.7966 0.0062 0.0042 0.9995 4.1218
BR 10 0.0053 0.0036 0.9997 3.6066 0.0064 0.0046 0.9996 4.5621
SCG 5 0.0093 0.0070 0.9991 7.1770 0.0082 0.0059 0.9993 6.6377
SCG 6 0.0069 0.0047 0.9995 4.6295 0.0086 0.0051 0.9993 5.5648
SCG 7 0.0057 0.0041 0.9996 4.1721 0.0067 0.0047 0.9995 4.7380
SCG 8 0.0073 0.0050 0.9995 5.1616 0.0066 0.0046 0.9995 4.7338
SCG 9 0.0060 0.0043 0.9996 4.3177 0.0045 0.035 0.9997 3.5009
SCG 10 0.0058 0.0040 0.9996 4.0049 0.0055 0.0037 0.9997 3.8441
BFG 5 0.0098 0.0075 0.9990 7.7813 0.0094 0.0063 0.9991 6.6895
BFG 6 0.0085 0.0065 0.9992 6.4473 0.0089 0.0071 0.9991 7.7731
BFG 7 0.0071 0.0052 0.9995 5.3043 0.0080 0.0061 0.9994 6.2189
BFG 8 0.0079 0.0055 0.9993 5.3949 0.0071 0.0057 0.9995 5.4981
BFG 9 0.0073 0.0051 0.9994 5.0717 0.0081 0.0062 0.9993 6.2044
BFG 10 0.0070 0.0051 0.9995 5.0902 0.0069 0.0049 0.9995 4.9913
RP 5 0.0124 0.0096 0.9991 9.5451 0.0095 0.0064 0.9994 7.4851
RP 6 0.0093 0.0073 0.9977 7.7233 0.0079 0.0059 0.9979 5.8844
RP 7 0.0088 0.0067 0.9992 6.8243 0.0096 0.0065 0.9991 6.9147
RP 8 0.0075 0.0055 0.9994 5.4966 0.0069 0.0051 0.9995 5.0257
RP 9 0.0060 0.0044 0.9996 4.4521 0.0076 0.0059 0.9994 5.7031
RP 10 0.0060 0.0042 0.9996 4.2935 0.0065 0.0045 0.9996 4.2303

Table A.2 Normalized dataset.

Experimental number Layer thickness (mm) Infill rate (%) Building direction Friction coefficient Volume loss (mm3)
1 0.33 0.33 0 0.0992 0.0613
2 0.33 0.33 0 0.0995 0.0621
3 0.33 0.33 0 0.1021 0.0620
4 0.33 0.33 0 0.1025 0.0622
5 0.33 0.33 0 0.1071 0.0627
6 0.33 0.66 0 0.0907 0.1300
7 0.33 0.66 0 0.1057 0.1300
8 0.33 0.66 0 0.1052 0.1326
9 0.33 0.66 0 0.1098 0.1301
10 0.33 0.66 0 0.1144 0.1404
11 0.33 1.0 0 0.0907 0.0778
12 0.33 1.0 0 0.1112 0.0779
13 0.33 1.0 0 0.1120 0.0785
14 0.33 1.0 0 0.1223 0.0782
15 0.33 1.0 0 0.1239 0.0801
16 0.33 0.33 1 0.0802 0.1358
17 0.33 0.33 1 0.0931 0.1376
18 0.33 0.33 1 0.0908 0.1376
19 0.33 0.33 1 0.0938 0.1377
20 0.33 0.33 1 0.0960 0.1394
21 0.33 0.66 1 0.1127 0.1514
22 0.33 0.66 1 0.1133 0.1522
23 0.33 0.66 1 0.1174 0.1535
24 0.33 0.66 1 0.1134 0.1535
25 0.33 0.66 1 0.1302 0.1571
26 0.33 1.0 1 0.0935 0.1113
27 0.33 1.0 1 0.0937 0.1176
28 0.33 1.0 1 0.0977 0.1170
29 0.33 1.0 1 0.0940 0.1185
30 0.33 1.0 1 0.1095 0.1203
31 0.66 0.33 0 0.1039 0.0742
32 0.66 0.33 0 0.1183 0.0752
33 0.66 0.33 0 0.1150 0.0796
34 0.66 0.33 0 0.1187 0.0843
35 0.66 0.33 0 0.1190 0.0848
36 0.66 0.66 0 0.0789 0.1000
37 0.66 0.66 0 0.0826 0.1024
38 0.66 0.66 0 0.0907 0.1049
39 0.66 0.66 0 0.0956 0.1085
40 0.66 0.66 0 0.1057 0.1086
41 0.66 1.0 0 0.1038 0.0771
42 0.66 1.0 0 0.1076 0.0772
43 0.66 1.0 0 0.1160 0.0786
44 0.66 1.0 0 0.1254 0.0783
45 0.66 1.0 0 0.1273 0.0819
46 0.66 0.33 1 0.0816 0.1596
47 0.66 0.33 1 0.0852 0.1629
48 0.66 0.33 1 0.0872 0.1628
49 0.66 0.33 1 0.0871 0.1639
50 0.66 0.33 1 0.0949 0.1647
51 0.66 0.66 1 0.1036 0.0917
52 0.66 0.66 1 0.1065 0.0924
53 0.66 0.66 1 0.1109 0.0966
54 0.66 0.66 1 0.1122 0.1002
55 0.66 0.66 1 0.1213 0.1023
56 0.66 1.0 1 0.0822 0.1029
57 0.66 1.0 1 0.0846 0.1027
58 0.66 1.0 1 0.0869 0.0988
59 0.66 1.0 1 0.1004 0.0944
60 0.66 1.0 1 0.0806 0.0953
61 1.0 0.33 0 0.1155 0.1082
62 1.0 0.33 0 0.1279 0.1080
63 1.0 0.33 0 0.1149 0.1072
64 1.0 0.33 0 0.1047 0.1055
65 1.0 0.33 0 0.1112 0.1071
66 1.0 0.66 0 0.0918 0.0642
67 1.0 0.66 0 0.1075 0.0601
68 1.0 0.66 0 0.1029 0.0645
69 1.0 0.66 0 0.1046 0.0658
70 1.0 0.66 0 0.1076 0.0678
71 1.0 1.0 0 0.1218 0.0911
72 1.0 1.0 0 0.1192 0.0915
73 1.0 1.0 0 0.1127 0.0890
74 1.0 1.0 0 0.1150 0.0883
75 1.0 1.0 0 0.0948 0.0850
76 1.0 0.33 1 0.1020 0.0811
77 1.0 0.33 1 0.1013 0.0767
78 1.0 0.33 1 0.1001 0.0779
79 1.0 0.33 1 0.1049 0.0759
80 1.0 0.33 1 0.0922 0.0777
81 1.0 0.66 1 0.1088 0.1064
82 1.0 0.66 1 0.1209 0.1059
83 1.0 0.66 1 0.1091 0.1064
84 1.0 0.66 1 0.1022 0.1073
85 1.0 0.66 1 0.1047 0.1061
86 1.0 1.0 1 0.1011 0.0803
87 1.0 1.0 1 0.1259 0.0811
88 1.0 1.0 1 0.1125 0.0776
89 1.0 1.0 1 0.1021 0.0752
90 1.0 1.0 1 0.1208 0.0737

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Received: 2023-12-19
Accepted: 2024-03-06
Published Online: 2024-04-24
Published in Print: 2024-07-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ipp-2023-4481
Keywords for this article
recycled filament; fused deposition modeling; friction coefficient; wear resistance; artificial neural network

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
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  4. Investigation of the mechanical, thermal and wear properties of eggshell/PLA composites
  5. Impact of fiber diameter on mechanical and water absorption properties of short bamboo fiber-reinforced polyester composites
  6. Polyurethane foam reinforced with Ag nanoparticle decorated ZnO nanorods: a dual-functional approach for improved antibacterial and mechanical properties
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  8. Study on flame retardant properties and thermal stability of synergistically modified polyurethane foam with ammonium polyphosphate and barium phytate
  9. Investigation on the mechanical and moisture uptake properties of epoxy-Terminalia arjuna fiber natural composites containing nano-silica
  10. Tribo-mechanical and structural characterizations of LLDPE matrix bio-composite reinforced with almond shell micro-particles: effects of the processing methodology
  11. Influence of the injection velocity profile on the properties of injection moulded parts
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