Karafan Journal

Karafan Journal

Experimental analysis of the influence of input parameters on surface roughness and roughing time in the the honing plateau

Document Type : Original Article

Authors
Naser Shokrollahi 1
Mohammad Reza Shabgard 2
Kamal Jahani 2
1 Mechanical engineering faculty , tabriz technical and vocational university
2 Department of Mechanical Engineering, University of Tabriz, Tabriz, 5166616471, Iran
10.48301/kssa.2024.456406.2902
Abstract
In the present study, the impact of key honing process parameters, including tool contact pressure, abrasive grain size, reciprocating speed, and tangential speed of the tool, on surface roughness parameters according to the Abbott-Firestone curve and honing time in the rough honing of gray cast iron cylinders has been evaluated. A linear regression model was developed for analyzing these parameters, and the most significant factors influencing surface roughness and material removal time were identified using analysis of variance. The direct and interaction effects of these factors were also examined. The results showed that the shortest honing time is achieved when all process input parameters are set to their highest levels. Additionally, tool pressure, grain size, linear speed, and tangential speed were found to have the greatest influence on honing time, in that order. Grain size was identified as the most influential parameter on surface roughness, with an increase in grain size leading to a decrease in honing surface quality. Furthermore, increasing linear speed improved surface roughness when tool pressure was low, whereas at higher pressures, increasing linear speed resulted in a decline in surface quality.
Keywords
Honing time
surface roughness of honing
honing plateau
regression
analysis of variance
Subjects
[1] Ren, F., & Bao, Y. (2020). A Review on Human-Computer Interaction and Intelligent Robots. International Journal of Information Technology & Decision Making, 19(01), 5-47. https://doi.org/10.1142/s0219622019300052
[2] Rai, A., Constantinides, P., & Sarker, S. (2019). Next generation digital platforms: toward human-AI hybrids. Management Information Systems quarterly, 43(1), iii-ix. https:/ /wrap.warwick.ac.uk/id/eprint/113653/
[3] Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534. https://doi.org/10.1126/science.aap8062
[4] Li, N. L., & Zhang, P. (2005). The intellectual development of human-computer interaction research: A critical assessment of the MIS literature (1990-2002). Journal of the Association for information Systems, 6(11), 227-292. https://doi.org/10.17705/1jais.00070
[5] Rzepka, C., & Berger, B. (2018, December 13-16). User interaction with AI-enabled systems: A systematic review of IS research [Conference session]. Thirty Ninth International Conference on Information Systems, San Francisco, California. https://www.researc hgate.net/publication/329269262_User_Interaction_with_AI-enabled_Systems_A_ Systematic_Review_of_IS_Research
[6] Rapp, A. (2023). Human–Computer Interaction. In Oxford Research Encyclopedia of Psychology. Oxford University Press. https://doi.org/10.1093/acrefore/9780190236557.013.47
[7] Sivakumar, N., K. K, C., Easwaran, B., & Tabassum, H. (2023, May 11-12). Design And Analysis of Human Computer Interaction Using AI Intelligence [Conference session]. 2023 International Conference on Disruptive Technologies, Greater Noida, India. ht tps://doi.org/10.1109/ICDT57929.2023.10150705
[8] Sreedharan, S. (2023). Human-aware AI —A foundational framework for human–AI interaction. Artificial Intelligence Magazine, 44(4), 460-466. https://doi.org/10.1002/aaai.12142
[9] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
[10] Horvitz, E. (1999, May 15-20). Principles of mixed-initiative user interfaces [Conference session]. Proceedings of the Special Interest Group on Computer-Human Interaction conference on Human Factors in Computing Systems, Pittsburgh, Pennsylvania, USA. https://doi.org/10.1145/302979.303030
[11] Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2022). Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research, 33(2), 678-696. https://doi.org/10.1287/isre.2021.1079
[12] Kim, J., Merrill Jr, K., & Collins, C. (2021). AI as a friend or assistant: The mediating role of perceived usefulness in social AI vs. functional AI. Telematics and Informatics, 64(1), 101694. https://doi.org/10.1016/j.tele.2021.101694
[13] Jin, S. V., & Youn, S. (2023). Social Presence and Imagery Processing as Predictors of Chatbot Continuance Intention in Human-AI-Interaction. International Journal of Human–Computer Interaction, 39(9), 1874-1886. https://doi.org/10.1080/1044731 8.2022.2129277
[14] Samanta, D. P. S., Patnaik, B., Satpathy, I., & Panda, L. (2024). A Bibliometric Analysis on Application of Artificial Intelligence (AI) in Workforce Management. In A. Khang, S. Rani, R. Gujrati, H. Uygun, & S. Gupta (Eds.), Designing Workforce Management Systems for Industry 4.0 (pp. 217-234). CRC Press. https://doi.org/10.1201/9781003 357070-14
[15] Jiang, J., Karran, A. J., Coursaris, C. K., Léger, P-M., & Beringer, J. (2023). A Situation Awareness Perspective on Human-AI Interaction: Tensions and Opportunities. International Journal of Human–Computer Interaction, 39(9), 1789-1806. https://doi.org/10.108 0/10447318.2022.2093863
[16] Buzydlowski, J. W. (2015). Co-occurrence analysis as a framework for data mining. Journal of Technology Research, 6, 1-19. https://www.semanticscholar.org/paper/Co-occurr ence-analysis-as-a-framework-for-data-Buzydlowski/f17632a4388a924ed406462c 7f1e374ab236df4a
[17] Hendijani Fard, M., Arasti, Z., Imanipour, N., & Chitsaz, E. (2024). Business Failure: A Bibliometric Co-occurrence and Content Analysis. Quarterly Scientific Journal of National University of Skills, 20(Special Issue), 35-62. https://doi.org/10.48301/kss a.2023.353332.2214
[18] Chandran, R. (2022). Human-Computer Interaction in Robotics: A bibliometric evaluation using Web of Science. Metaverse Basic and Applied Research, 1, 22. https://doi.org/ 10.56294/mr202222
[19] Liu, Y-X., Zhu, C., Wu, Z-X., Lu, L-J., & Yu, Y-T. (2022). A bibliometric analysis of the application of artificial intelligence to advance individualized diagnosis and treatment of critical illness. Annals of Translational Medicine, 10(16), 854. https://doi.org/10. 21037/atm-22-913
[20] Gao, F., Jia, X., Zhao, Z., Chen, C-C., Xu, F., Geng, Z., & Song, X. (2021). Bibliometric analysis on tendency and topics of artificial intelligence over last decade. Microsystem Technologies, 27(4), 1545-1557. https://doi.org/10.1007/s00542-019-04426-y
[21] Huang, P., Feng, Z., Shu, X., Wu, A., Wang, Z., Hu, T., Cao, Y., Tu, Y., & Li, Z. (2023). A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022). Frontiers in Oncology, 13, 1-15. https://doi.org/10.3389/fonc.2 023.1077539
[22] Hajizadeh, M., Alaeddini, M., & Reaidy, P. (2023). Bibliometric Analysis on the Convergence of Artificial Intelligence and Blockchain. In J. Prieto, F. L. Benítez Martínez, S. Ferretti, D. Arroyo Guardeño, & P. Tomás Nevado-Batalla (Eds.), Blockchain and Applications, 4th International Congress (pp. 334-344). Springer International Publishing. https:// doi.org/10.1007/978-3-031-21229-1_31
[23] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
[24] Hosseinpour, M., Fathi Zolmabadi, B., Heshmati, H., & Khodaei, A. (2023). Factors Accepting the Electronic Procurement System and User Satisfaction in Small and Medium Rural Businesses in Kermanshah Province. Quarterly Scientific Journal of National University of Skills, 20(Special Issue), 499-520. https://doi.org/10.48301/kssa.2021.276821.1424
[25] Emerson, R. M. (1976). Social Exchange Theory. Annual Review of Sociology, 2(1), 335-362. https://doi.org/10.1146/annurev.so.02.080176.002003
[26] Foa, E. B., & Foa, U. G. (1980). Resource Theory. In K. J. Gergen, M. S. Greenberg, & R. H. Willis (Eds.), Social Exchange: Advances in Theory and Research (pp. 77-94). Springer https://doi.org/10.1007/978-1-4613-3087-5_4
[27] Blau, P. M. (2017). Exchange and Power in Social Life (2 ed.). Transaction Publishers. https://doi.org/10.4324/9780203792643
[28] Cropanzano, R., & Mitchell, M. S. (2005). Social Exchange Theory: An Interdisciplinary Review. Journal of Management, 31(6), 874-900. https://doi.org/10.1177/01492063 05279602
[29] Etemadi, M., Chitsaz, E., & Abolghasemi Dehaghani, M. (2023, May 6). The Myth of Rewards and Creative Performance: Should Companies Use Incentives to Boost Creativity in Personnel Performance? [Conference session]. 3rd Iran Business Watch Conference, 2023, Tehran, Iran. https://civilica.com/doc/1720016/
[30] Coleman, J. S. (1990). Foundations of Social Theory. Belknap Press of Harvard University Press. https://books.google.com/books?id=XgC2AAAAIAAJ
[31] Skvoretz, J. (1998). Coercive Power in Social Exchange. Social Forces, 76(3), 1135-1137. https://doi.org/10.2307/3005707
[32] Etemadi, M., Chitsaz, E., & Abolghasemi Dehaghani, M. (2023, May 6). Unveiling the Complexity of the Reward, Creativity, and Performance Relationship: When Does Behavioral Theories Reward Backfire? [Conference session]. 3rd Iran Business Watch Conference, 2023, Tehran, Iran. https://civilica.com/doc/1720035/
 
Karafan Journal
Volume 22, Issue 3
Technical and Engineering
Autumn 2025
Pages 173-192

  • Receive Date 08 May 2024
  • Revise Date 10 September 2024
  • Accept Date 19 November 2024