{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T04:14:46Z","timestamp":1781324086267,"version":"3.54.1"},"reference-count":61,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":345,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009392","name":"Prince Sattam bin Abdulaziz University","doi-asserted-by":"publisher","award":["PSAU\/2023\/R\/1445"],"award-info":[{"award-number":["PSAU\/2023\/R\/1445"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>The thermal issues generated from friction are the key obstacle in the high\u2010performance machining of titanium alloys. The friction between the workpiece being cut and the cutting tool is the dominant parameter that affects the heat generation during the machining processes, i.e., the temperature inside the cutting zone and the consumed cutting energy. Besides, the complexity is associated with the nature of the friction phenomenon. However, there are limited efforts to forecast the friction coefficient during the machining operations. In this work, the friction coefficients between the titanium alloy against zirconia ceramics lubricated by minimum quantity lubrication were recorded and measured using a universal mechanical tester pin\u2010on\u2010disc tribometer. Then, we proposed two models for forecasting the friction coefficient which are trained and tested on the recorded data. The two predictive models are based on autoregressive integrated moving average and gated recurrent unit deep neural network methods. The proposed models are evaluated through a set of exhaustive experiments. These experiments demonstrated that the proposed models can efficiently be used to reduce power consumption dedicated to monitoring the friction coefficients. Besides, they can reduce or avoid surface thermal damage by predicting the high level of friction coefficients in advance, which can be used as an alert to enable or readjust the lubrication parameters (fluid pressure, fluid flow rate, etc.) to maintain lower ranges of friction coefficients and power consumption.<\/jats:p>","DOI":"10.1155\/2023\/6681886","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T20:50:16Z","timestamp":1702414216000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Forecasting the Friction Coefficient of Rubbing Zirconia Ceramics by Titanium Alloy"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3433-7640","authenticated-orcid":false,"given":"Ahmad","family":"Salah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5432-5407","authenticated-orcid":false,"given":"Ahmed","family":"Fathalla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6519-0963","authenticated-orcid":false,"given":"Esraa","family":"Eldesouky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5711-8104","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-0853","authenticated-orcid":false,"given":"Ahmed Mohamed","family":"Mahmoud Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.triboint.2020.106387"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0890-6955(02)00039-1"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7091-2678-3_2"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2015.03.039"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1039\/c5ra11862j"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-022-10788-x"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmapro.2022.11.047"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-016-9719-5"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2013.12.236"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00603-022-03095-0"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2016910497"},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2020.101588"},{"key":"e_1_2_12_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-020-01495-8"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.035254"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.082"},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8551167"},{"key":"e_1_2_12_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijimpeng.2023.104659"},{"key":"e_1_2_12_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmapro.2023.03.034"},{"key":"e_1_2_12_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2022.107797"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-023-11452-8"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-022-09841-5"},{"key":"e_1_2_12_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tsep.2022.101630"},{"key":"e_1_2_12_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.triboint.2022.108166"},{"key":"e_1_2_12_24_2","doi-asserted-by":"publisher","DOI":"10.1177\/13506501221089519"},{"key":"e_1_2_12_25_2","first-page":"1","article-title":"Artificial intelligence algorithms for prediction of the ultimate tensile strength of the friction stir welded magnesium alloys","author":"Mishra A.","year":"2023","journal-title":"International Journal on Interactive Design and Manufacturing"},{"key":"e_1_2_12_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105961"},{"key":"e_1_2_12_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12541-020-00388-8"},{"key":"e_1_2_12_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2017.07.091"},{"key":"e_1_2_12_29_2","doi-asserted-by":"publisher","DOI":"10.1142\/s1469026818500177"},{"key":"e_1_2_12_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41939-023-00178-5"},{"key":"e_1_2_12_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2020.04.056"},{"key":"e_1_2_12_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2018.08.253"},{"key":"e_1_2_12_33_2","doi-asserted-by":"publisher","DOI":"10.1186\/1556-276x-7-226"},{"key":"e_1_2_12_34_2","doi-asserted-by":"publisher","DOI":"10.1088\/0957-4484\/20\/18\/185702"},{"key":"e_1_2_12_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.icheatmasstransfer.2015.01.002"},{"key":"e_1_2_12_36_2","volume-title":"133 Standard Test Method for Linearly Reciprocating ball-on-flat Sliding Wear","author":"Astm G.","year":"2016"},{"key":"e_1_2_12_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2014.07.071"},{"key":"e_1_2_12_38_2","doi-asserted-by":"publisher","DOI":"10.2307\/2985674"},{"key":"e_1_2_12_39_2","volume-title":"Gm Time Series Analysis","author":"Box G. J.","year":"1970"},{"key":"e_1_2_12_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.11.071"},{"key":"e_1_2_12_41_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301485"},{"key":"e_1_2_12_42_2","doi-asserted-by":"crossref","unstructured":"QuanZ. LinX. WangZ.-J. LiuY. WangF. andLiK. A system for learning atoms based on long short-term memory recurrent neural networks Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) December 2018 Madrid Spain IEEE 728\u2013733.","DOI":"10.1109\/BIBM.2018.8621313"},{"key":"e_1_2_12_43_2","doi-asserted-by":"crossref","unstructured":"ChoK. Van Merri\u00ebnboerB. GulcehreC. BahdanauD. BougaresF. SchwenkH. andBengioY. Learning phrase representations using rnn encoder-decoder for statistical machine translation 2014 https:\/\/arxiv.org\/abs\/1406.1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_2_12_44_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_12_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/tetci.2017.2762739"},{"key":"e_1_2_12_46_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.1.1"},{"key":"e_1_2_12_47_2","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N.","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_12_48_2","unstructured":"KingmaD. P.andBaJ. Adam: a method for stochastic optimization 2014 https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"e_1_2_12_49_2","doi-asserted-by":"publisher","DOI":"10.1088\/1749-4699\/8\/1\/014008"},{"key":"e_1_2_12_50_2","doi-asserted-by":"crossref","unstructured":"BergstraJ. YaminsD. andCoxD. D. Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms Proceedings of the 12th Python in Science Conference July 2013 Austin TX USA Citeseer 13\u201320.","DOI":"10.25080\/Majora-8b375195-003"},{"key":"e_1_2_12_51_2","doi-asserted-by":"crossref","unstructured":"McKinneyW. Data structures for statistical computing in python 445 Proceedings of the 9th Python in Science Conference June 2010 Austin TX USA 51\u201356.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"e_1_2_12_52_2","doi-asserted-by":"crossref","unstructured":"SeaboldS.andPerktoldJ. Statsmodels: econometric and statistical modeling with python Proceedings of the 9th Python in Science Conference June 2010 Austin TX USA.","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"e_1_2_12_53_2","unstructured":"AbadiM. BarhamP. ChenJ. ChenZ. DavisA. DeanJ. DevinM. GhemawatS. IrvingG. andIsardM. Tensorflow: a system for large-scale machine learning Proceedings of the 12th USENIX Symposium on Operating Systems Design And Implementation (OSDI 16) November 2016 Savannah GA USA 265\u2013283."},{"key":"e_1_2_12_54_2","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_12_55_2","volume-title":"A Guide to NumPy","author":"Oliphant T. E.","year":"2006"},{"key":"e_1_2_12_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcse.2007.55"},{"key":"e_1_2_12_57_2","doi-asserted-by":"publisher","DOI":"10.1080\/10402004.2015.1044149"},{"key":"e_1_2_12_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"e_1_2_12_59_2","doi-asserted-by":"crossref","unstructured":"NetoM. C. A. TavaresG. AlvesV. M. CavalcantiG. D. andRenT. I. Improving financial time series prediction using exogenous series and neural networks committees Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN) July 2010 Barcelona Spain IEEE 1\u20138.","DOI":"10.1109\/IJCNN.2010.5596911"},{"key":"e_1_2_12_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09754-z"},{"key":"e_1_2_12_61_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6393805"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/6681886.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/6681886.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/6681886","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:12:03Z","timestamp":1735621923000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/6681886"}},"subtitle":[],"editor":[{"given":"Alexander","family":"Ho\u0161ovsk\u00fd","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/6681886"],"URL":"https:\/\/doi.org\/10.1155\/2023\/6681886","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2023-06-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6681886"}}