Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks
Source abstract
Abstract
The present paper discussed the development of a reliable and robust artificial neural network (ANN) capable of predicting the tribological performance of three highly alloyed tool steel grades. Experimental results were obtained by performing plane-contact sliding tests under non-lubrication conditions on a pin-on-disk tribometer. The specimens were tested both in untreated state with different hardening levels, and after surface treatment of nitrocarburizing. We concluded that wear maps via ANN modeling were a user-friendly approach for the presentation of wear-related information, since they easily permitted the determination of areas under steady-state wear that were appropriate for use. Furthermore, the achieved optimum ANN model seemed to be a simple and helpful design/educational tool, which could assist both in educational seminars, as well as in the interpretation of the surface treatment effects on the tribological performance of tool steels.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lubricants and Their Additives · Metal and Thin Film Mechanics · Metal Alloys Wear and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, GR 14121 Athens, Greece
Department of Mechanical Engineering, University of West Attica, 12244 Egaleo, Greece
Laboratory of Manufacturing Processes and Machine Tools (LMProMaT), Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), ASPETE Campus, N. Heraklion, GR 14121 Athens, Greece
References (87)
Show source-provided references
1. Wear and degradation modes in selected vehicle tribosystems
2. Failure mechanisms of an automobile clutch assembly cast iron pressure plate10.1007/s11668-011-9523-3 ↗
3. Williams, J.A. (1996). Engineering Tribology, Oxford University Press.
4. Wear behavior of anti-galling surface textured gray cast iron using pulsed-CO2 laser treatment10.1016/S0043-1648(96)07339-5 ↗
5. Microstructure and tribological behaviour of liquid nitrocarburised tool steels10.1016/S0257-8972(02)00566-2 ↗
6. Influence of microstructure on the sliding wear behavior of nitrocarburized tool steels10.1016/j.wear.2013.04.002 ↗
7. Surface hardening of steel in a solar furnace10.1016/S0257-8972(97)00073-X ↗
8. Improvement of resistance to oxidation by laser alloying on a tool steel10.1016/S1359-6462(98)00329-7 ↗
9. Surface hardening of low-alloy 15CrNi6 steel by CO2 laser beam10.1016/S0257-8972(99)00246-7 ↗
10. ASM Handbook (1996). Surface Engineering, ASM International.
11. Tool steel ion beam assisted nitrocarburization10.1016/j.msea.2007.02.048 ↗
12. Friction and rolling-sliding wear of DC-pulsed plasma nitrided AISI 410 martensitic stainless steel10.1016/j.wear.2005.03.008 ↗
13. Plasma and gaseous nitrocarburizing of C60W steel for tribological applications10.1016/j.vacuum.2008.12.004 ↗
14. Investigation of the effects of time and temperature of oxidation on corrosion behavior of plasma nitrided AISI 4140 steel10.1016/j.surfcoat.2012.04.064 ↗
15. Pye, D. (2003). Practical Nitriding and Ferritic Nitrocarburizing, ASM International.10.31399/asm.tb.pnfn.9781627083508 ↗
16. Wang, Q.J., and Chung, Y.W. (2013). Tufftriding and tennifer surface treatment. Encyclopedia of Tribology, Springer.
17. Yu, L. (1977). Engineering Physical Metallurgy and Heat Treatment, Mir Publishers.
18. Control of iron nitride layers growth kinetics in the binary Fe-N system10.1007/BF02651932 ↗
19. Sliding wear behaviour of a liquid nitrocarburised precipitation-hardening (PH) stainless steel10.1016/j.surfcoat.2004.01.015 ↗
20. Tribological properties of a liquid nitrocarburised special purpose cold work tool steel10.1016/j.surfcoat.2005.09.001 ↗
21. Erosion and wear behavior of nitrocarburized DC53 tool steel10.1016/j.wear.2009.10.012 ↗
22. Improvement of wear and corrosion resistances of 17-4PH stainless steel by plasma nitrocarburizing10.1016/j.matdes.2009.11.069 ↗
23. Evolving RBF neural networks for adaptive soft-sensor design10.1142/S0129065713500299 ↗
24. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization10.1007/s12517-013-1174-0 ↗
25. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition10.1016/j.tust.2016.12.009 ↗
26. Landslide hazard assessment using random subspace fuzzy rules based classifier ensemble and probability analysis of rainfall data: A case study at mu cang chai district, yen bai province (Vietnam)10.1007/s12524-016-0620-3 ↗
27. Evaluation and comparison of logitboost ensemble, fisher’s linear discriminant analysis, logistic regression and support vector machines methods for landslide susceptibility mapping10.1080/10106049.2017.1404141 ↗
28. A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: A case study in the himalayan area, india10.1016/j.ijsrc.2017.09.008 ↗
29. Machine learning methods of kernel logistic regression and classification and regression trees for landslide susceptibility assessment at part of himalayan area, India10.17485/ijst/2018/v11i12/99745 ↗
30. Ripley, B.D. (1996). Pattern Recognition and Neural Networks, Cambridge University Press.10.1017/CBO9780511812651 ↗
31. Neural networks in civil engineering: 1989–200010.1111/0885-9507.00219 ↗
32. Abrasive wear resistance of TiN/NbN multi-layers: Measurement and neural network modelling10.1016/S0257-8972(96)02956-8 ↗
33. Preliminary investigation of neural network techniques to predict tribological properties10.1080/10402009708983660 ↗
34. Artificial neural network prediction of fretting wear behavior of structural steel, En 24 against bearing steel, En 3110.1007/s11665-007-9100-9 ↗
35. Application of probablistic neural network for the development of wear mechanism map for glass fiber reinforced plastics10.1177/0731684407082632 ↗
36. Prediction of tribological behavior of aluminum–copper based composite using artificial neural network10.1016/j.jallcom.2008.03.035 ↗
37. Prediction of wear behaviour of A356/SiCp MMCs using neural networks10.1016/j.triboint.2008.08.010 ↗
38. Modeling of the wear behavior in A356–B4C composites10.1007/s10853-011-5623-4 ↗
39. Modeling the wear of Polyamide 66 using artificial neural network10.1016/j.matdes.2012.05.013 ↗
40. Optimization of hybrid aluminum composites wear using Taguchi method and artificial neural network10.1108/ILT-02-2017-0043 ↗
41. Heat treatment effects on tribological characteristics for AISI A8 tool steel and development of wear mechanism maps using K means clustering and neural networks10.1080/17515831.2018.1437335 ↗
42. Prediction of surface roughness in the end milling machining using artificial neural network10.1016/j.eswa.2009.07.033 ↗
43. Application of artificial neural networks for modelling correlations in titanium alloys10.1016/j.msea.2003.09.029 ↗
44. Performance of neural networks in materials science10.1179/174328408X311053 ↗
45. Incorporation of material behavior in modeling of metal forming and machining processes: A review10.1016/j.matdes.2011.03.049 ↗
46. Experimental optimisation of the tribological behaviour of Al/SiC/Gr hybrid composites based on Taguchi’s method and artificial neural network10.1007/s40430-018-1237-y ↗
47. Parametric optimization of the aluminium nanocomposites wear rate10.1007/s40430-018-1531-8 ↗
48. Wear detection of rolling element bearings using multiple-sensing technologies and mixture-model-based clustering method
49. Multilayer feedforward networks are universal approximators10.1016/0893-6080(89)90020-8 ↗
50. Papadrakakis, M., Kojic, I., and Tuncer, M. (2013, January 12–14). Neural network approximation of the masonry failure under biaxial compressive stress. Proceedings of the 3rd South-East European Conference on Computational Mechanics—An ECCOMAS and IACM Special Interest Conference, Kos Island, Greece.
51. Prediction of self-compacting concrete strength using artificial neural networks10.1080/19648189.2016.1246693 ↗
52. Prediction of the fundamental period of infilled rc frame structures using artificial neural networks10.1155/2016/5104907 ↗
53. Asteris, P.G., Roussis, P.C., and Douvika, M.G. (2017). Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors, 17.10.3390/s17061344 ↗
54. Anisotropic masonry failure criterion using artificial neural networks10.1007/s00521-016-2181-3 ↗
55. Self-compacting concrete strength prediction using surrogate models10.1007/s00521-017-3007-7 ↗
56. Cavaleri, L., Chatzarakis, G.E., Di Trapani, F., Douvika, M.G., Foskolos, F.M., Fotos, A., Giovanis, D.G., Karypidis, D.F., Livieratos, S., and Roinos, K. (2016, January 24–28). Surface roughness prediction of electro-discharge machined components using artificial neural networks. Proceedings of the 5th International Conference on Integrity, Reliability and Failure, Faculty of Engineering/U, Porto, Portugal.
57. Modeling of surface roughness in electro-discharge machining using artificial neural networks
58. The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network10.1109/18.661502 ↗
59. Performance analysis of various activation functions in generalized MLP architectures of neural networks
60. Surface treatment of tool steels against galling failure10.1051/matecconf/201818804024 ↗
61. Lourakis, M.I.A. (2005). A Brief Description of the Levenberg-Marquardt Algorithm Implemened by Levmar, Hellas (FORTH) Institute of Computer Science Foundation for Research and Technology. Available online: http://www.ics.forth.gr/~lourakis/levmar/levmar.
62. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks10.1016/j.aap.2005.06.024 ↗
63. Iruansi, O., Guadagnini, M., Pilakoutas, K., and Neocleous, K. (2010, January 3–5). Predicting the shear strength of rc beams without stirrups using bayesian neural network. Proceedings of the 4th International Workshop on Reliable Engineering Computing, Robust Design—Coping with Hazards, Risk and Uncertainty, Singapore.
64. Asteris, P.G., Moropoulou, A., Skentou, A.D., Apostolopoulou, M., Mohebkhah, A., Cavaleri, L., Rodrigues, H., and Varum, H. (2019). Stochastic vulnerability assessment of masonry structures: Concepts, modeling and restoration aspects. Appl. Sci., 9.10.3390/app9020243 ↗
65. Krill herd algorithm-based neural network in structural seismic reliability evaluation10.1080/15376494.2018.1430874 ↗
66. Chen, H., Asteris, P.G., Armaghani, D.J., Gordan, B., and Pham, B.T. (2019). Assessing dynamic conditions of the retaining wall using two hybrid intelligent models. Appl. Sci., 9.10.3390/app9061042 ↗
67. Asteris, P.G., Argyropoulos, I., Cavaleri, L., Rodrigues, H., Varum, H., Thomas, J., and Lourenço, P.B. (2018, January 10–13). Masonry compressive strength prediction using artificial neural networks. Proceedings of the 1st International Conference TMM_CH, Transdisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage, Athens, Greece.10.1007/978-3-030-12960-6_14 ↗
68. An optimized ANN model based on genetic algorithm for predicting ripping production10.1007/s00521-016-2359-8 ↗
69. Asteris, P.G., and Nikoo, M. (2019). Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput. Appl.10.1007/s00521-018-03965-1 ↗
70. Apostolopoulou, M., Douvika, M.G., Kanellopoulos, I.N., Moropoulou, A., and Asteris, P.G. (2018, January 10–13). Prediction of compressive strength of mortars using artificial neural networks. Proceedings of the 1st International Conference TMM_CH, Transdisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage, Athens, Greece.
71. Flood-routing modeling with neural network optimized by social-based algorithm10.1007/s11069-016-2176-5 ↗
72. Determination of damage in reinforced concrete frames with shear walls using self-organizing feature map
73. Determining the natural frequency of cantilever beams using ann and heuristic search10.1080/08839514.2018.1448003 ↗
74. Prediction of the durability of limestone aggregates using computational techniques10.1007/s00521-016-2456-8 ↗
75. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions10.1007/s00500-018-3253-3 ↗
76. Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal area, Uttarakhand, India10.1007/s40710-017-0248-5 ↗
77. A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using gis10.1007/s12665-017-6689-3 ↗
78. Ly, H.-B., Le, L.M., Duong, H.T., Nguyen, T.C., Pham, T.A., Le, T.-T., Le, V.M., Nguyen-Ngoc, L., and Pham, B.T. (2019). Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections. Appl. Sci., 9.10.3390/app9112258 ↗
79. Koopialipoor, M., Fahimifar, A., Ghaleini, E.N., Momenzadeh, M., and Armaghani, D.J. (2019). Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance. Eng. Comput.10.1007/s00366-019-00701-8 ↗
80. Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN10.1007/s12665-019-8163-x ↗
81. Prediction of seismic slope stability through combination of particle swarm optimization and neural network10.1007/s00366-015-0400-7 ↗
82. A risk-based technique to analyzeflyrock results through rock engineering system10.1007/s10706-018-0459-1 ↗
83. Predicting tunnel boring machine performance through a new model based on the group method of data handling10.1007/s10064-018-1349-8 ↗
84. Liao, X., Khandelwal, M., Yang, H., Koopialipoor, M., and Murlidhar, B.R. (2019). Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques. Eng. Comput.10.1007/s00366-019-00711-6 ↗
85. Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques10.1007/s00366-018-0642-2 ↗
86. Zhao, Y., Noorbakhsh, A., Koopialipoor, M., Azizi, A., and Tahir, M.M. (2019). A new methodology for optimization and prediction of rate of penetration during drilling operations. Eng. Comput.10.1007/s00366-019-00715-2 ↗
87. Sarir, P., Chen, J., Asteris, P.G., Armaghani, D.J., and Tahir, M.M. (2019). Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng. Comput.10.1007/s00366-019-00808-y ↗