Surface roughness evaluation in hardened materials by pattern recognition using network theory

Springer Science and Business Media LLC - Tập 13 Số 1 - Trang 211-219 - 2019
Matej Babič1, Michele Calı̀2, Іван Назаренко3, Cristiano Fragassa4, Sabahudin Ekinović5, Mária Mihaliková6, Mileta Janjić7, I. Belič8
1Jožef Štefan Institute, Ljubljana, Slovenia
2Department of Electrical Electronic and Computer Engineering, University of Catania, Catania, Italy
3Kyiv National University of Construction and Architecture, Kiev, Ukraine
4Alma Mater Studiorum, University of Bologna, Bologna, Italy
5University of Zenica , Zenica, Bosnia and Herzegovina.
6Faculty of Materials, Metallurgy and Recycling, Kosice, Slovakia
7Faculty of Mechanical Engineering, University of Montenegro, Podgorica, Montenegro
8Institute of Metals and Technology, Ljubljana, Slovenia

Tóm tắt

Từ khóa


Tài liệu tham khảo

Grum, J., Žerovnik, P., Šturm, R.: Measurement and analysis of residual stresses after laser hardening and laser surface melt hardening on flat specimens. In: Proceedings of the conference “Quenching’96”, Ohio, Cleveland (1996)

Zhai, C., Gan, Y., Hanaor, D., Proust, G., Retraint, D.: The role of surface structure in normal contact stiffness. Exp. Mech. 56(3), 359–368 (2016). https://doi.org/10.1007/s11340-015-0107-0

Calì, M., Oliveri, S.M., Sequenzia, G., Fatuzzo, G.: An effective model for the sliding contact forces in a multibody environment. Lecture Notes in Mechanical Engineering. Springer, Cham, pp. 675–685 (2017). www.springer.com/series/11236 https://doi.org/10.1007/978-3-319-45781-9_121

Krulikowski, A.: Fundamentals of Geometric Dimensioning and Tolerancing. Cengage Learning, Boston (2012)

Calì, M., Oliveri S. M., Ambu, R., Fichera, G.: An integrated approach to characterize the dynamic behaviour of a mechanical chain tensioner by functional tolerancing. Strojniški vestnik—J. Mech. Eng. 64(4), 245–257 (2018)

Abouelatta, O.B., Madl, J.: Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J. Mater. Process. Technol. 118(1–3), 269–277 (2001)

Lai, J., Huang, H., Buising, W.: Effects of microstructure and surface roughness on the fatigue strength of high-strength steels. Procedia Struct. Integr. 2, 1213–1220 (2016)

Wakuda, M., Yamauchi, Y., Kanzaki, S., Yasuda, Y.: Effect of surface texturing on friction reduction between ceramic and steel materials under lubricated sliding contact. Wear 254(3–4), 356–363 (2003)

Ambu, R., Bertetto, A.M., Mazza, L.: Re-design of a guide bearing for pneumatic actuators and life tests comparison. Tribol. Int. 96, 317–325 (2016)

Petare, A.C., Jain, N.K.: On simultaneous improvement of wear characteristics, surface finish and microgeometry of straight bevel gears by abrasive flow finishing process. Wear 404–405, 38–49 (2018)

Boeing, G.: Visual analysis of nonlinear dynamical systems: chaos, fractals, self-similarity and the limits of prediction. Systems 4(4), 37 (2016). https://doi.org/10.3390/systems4040037

Tai, Y., Yang, J., Luo, L., Zhang, F., Qian, J.: Learning discriminative singular value decomposition representation for face recognition. Pattern Recognit. 50, 1–16 (2016)

Bridge, J.P., Holden, S.B., Paulson, L.C.: Machine learning for first-order theorem proving. J. Autom. Reason. 53(2), 141–172 (2014)

Grandjean, Martin: A social network analysis of twitter: mapping the digital humanities community. Cogent Arts Human. 3(1), 1171458 (2016). https://doi.org/10.1080/23311983.2016.1171458

Yazdi, M., Hamzeh S.: Knowledge acquisition development in failure diagnosis analysis as an interactive approach. Int. J. Interact. Des. Manuf. (IJIDeM) 1–18 (2018). https://doi.org/10.1007/s12008-018-0504-6

Vergnano, A., Berselli, G., Pellicciari, M.: Interactive simulation-based-training tools for manufacturing systems operators: an industrial case study. Int. J. Interact. Des. Manuf. (IJIDeM) 11(4), 785–797 (2017)

Mukattash, A.M., Tahboub, K.K., Adil, M.B.: Interactive design of cellular manufacturing systems, optimality and flexibility. Int. J. Interact. Des. Manuf. (IJIDeM) 12, 1–8 (2017)

Li, J., Du, Q., Sun, C.: An improved box-counting method for image fractal dimension estimation. Pattern Recogn. 42(11), 2460–2469 (2009)

Armstrong, J.: Scott: illusions in regression analysis. Int. J. Forecast. 28(3), 689 (2012)

Hansen, J.V., Lowry, P.B., Meservy, R.D., McDonald, D.M.: Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection. Decis. Support Syst. 43(4), 1362–1374 (2007). https://doi.org/10.1016/j.dss.2006.04.004.SSRN877981

Ojha, V.K., Abraham, A., Snášel, V.: Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng. Appl. Artif. Intell. 60, 97–116 (2017). https://doi.org/10.1016/j.engappai.2017.01.013

Wenzel, F., Galy-Fajou, T., Deutsch, M., Kloft, M.: Bayesian nonlinear support vector machines for big data (PDF). Machine learning and knowledge discovery in databases (ECML PKDD). Archived (PDF) from the original on 2017-08-30

Lewoniewski, W., Węcel, K., Abramowicz, W.: Quality and Importance of wikipedia articles in different languages. Information and software technologies. ICIST 2016. Commun. Comput. Inf. Sci. 639, 613–624 (2016). https://doi.org/10.1007/978-3-319-46254-7_50

Malkov, Y., Ponomarenko, A., Krylov, V., Logvinov, A.: Approximate nearest neighbor algorithm based on navigable small world graphs. Inf. Syst. 45, 61–68 (2014)