Pub Date : 2022-09-15DOI: 10.24874/ti.1313.05.22.09
M. Chernets, A. Kornienko, Yu. M. Chernets’
{"title":"Investigations on Contact Pressures and Durability of Metal-Polymer Dry Sliding Bearings with Miniature, Small and Large Diameters","authors":"M. Chernets, A. Kornienko, Yu. M. Chernets’","doi":"10.24874/ti.1313.05.22.09","DOIUrl":"https://doi.org/10.24874/ti.1313.05.22.09","url":null,"abstract":"","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49405746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1177.08.21.04
M. N. Muigai, F. Mwema, E. Akinlabi, T. Jen, O. Fatoba, T. P. Leso, J. Obiko, Percy Mphasha
{"title":"TIG Welding Methods of Repairing Steel Components with Stainless Steel Coatings","authors":"M. N. Muigai, F. Mwema, E. Akinlabi, T. Jen, O. Fatoba, T. P. Leso, J. Obiko, Percy Mphasha","doi":"10.24874/ti.1177.08.21.04","DOIUrl":"https://doi.org/10.24874/ti.1177.08.21.04","url":null,"abstract":"","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48639521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1256.02.22.04
T. Schneider, A. B. Bedrikow, K. Völkel, H. Pflaum, K. Stahl
Wet multi-plate clutches are safety-critical components of drive trains. Failure and damage must therefore be strongly avoided. In contrast to long-term damage, spontaneous damage and failures are difficult to predict. Different authors have already theorized the influence of different parameters on the carrying capacity. This paper extends the analysis of these effects by conducting experimental investigations. Furthermore, an evaluation method that allows to compare different clutch systems regarding their load carrying capacity is presented. Clutch systems with variation of oil flow, plate thickness, coating and groove pattern were tested, and their results analyzed. Statistical methods were used to consider the variance of the measurements. Lastly, recommendations are formulated for the development of clutch systems concerning the load carrying capacity. Strong trends can be seen for the influences of the thickness of the steel plate and oil flow rate, but these can only be statistically substantiated for the variation of the oil flow rate. For the variation of the coating and the grooving, only slight to no differences are discernible.
{"title":"Load Capacity Comparison of Different Wet Multi-Plate Clutches with Sinter Friction Lining with Regard to Spontaneous Damage Behavior","authors":"T. Schneider, A. B. Bedrikow, K. Völkel, H. Pflaum, K. Stahl","doi":"10.24874/ti.1256.02.22.04","DOIUrl":"https://doi.org/10.24874/ti.1256.02.22.04","url":null,"abstract":"Wet multi-plate clutches are safety-critical components of drive trains. Failure and damage must therefore be strongly avoided. In contrast to long-term damage, spontaneous damage and failures are difficult to predict. Different authors have already theorized the influence of different parameters on the carrying capacity. This paper extends the analysis of these effects by conducting experimental investigations. Furthermore, an evaluation method that allows to compare different clutch systems regarding their load carrying capacity is presented. Clutch systems with variation of oil flow, plate thickness, coating and groove pattern were tested, and their results analyzed. Statistical methods were used to consider the variance of the measurements. Lastly, recommendations are formulated for the development of clutch systems concerning the load carrying capacity. Strong trends can be seen for the influences of the thickness of the steel plate and oil flow rate, but these can only be statistically substantiated for the variation of the oil flow rate. For the variation of the coating and the grooving, only slight to no differences are discernible.","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43730441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1368.07.22.09
Strahinja Ðurovic, Jelena Stanojković, D. Lazarević, Bogdan Ćirković, Aleksa Lazarvic, D. Džunić, Ž. Šarkočević
In recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high reliability in the prediction and optimization of machining processes. In this paper, machining parameters such as spindle speed, feed rate and depth of cut were used in end milling process to minimize surface roughness. The influence of the parameters on the surface roughness was investigated using an artificial neural network and multiple regression analysis, and results are compared with the measured results
{"title":"Modeling and Prediction of Surface Roughness in the End Milling Process using Multiple Regression Analysis and Artificial Neural Network","authors":"Strahinja Ðurovic, Jelena Stanojković, D. Lazarević, Bogdan Ćirković, Aleksa Lazarvic, D. Džunić, Ž. Šarkočević","doi":"10.24874/ti.1368.07.22.09","DOIUrl":"https://doi.org/10.24874/ti.1368.07.22.09","url":null,"abstract":"In recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high reliability in the prediction and optimization of machining processes. In this paper, machining parameters such as spindle speed, feed rate and depth of cut were used in end milling process to minimize surface roughness. The influence of the parameters on the surface roughness was investigated using an artificial neural network and multiple regression analysis, and results are compared with the measured results","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44134969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1278.03.22.07
S. Kango, N. Sharma, Rajesh Sharma, R. Pandey
{"title":"Experimental Investigations on Temperature and Frictional Behaviors of Textured Journal Bearings","authors":"S. Kango, N. Sharma, Rajesh Sharma, R. Pandey","doi":"10.24874/ti.1278.03.22.07","DOIUrl":"https://doi.org/10.24874/ti.1278.03.22.07","url":null,"abstract":"","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45255839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1211.11.21.02
V. Navrátil, R. Galas, M. Klapka, Daniel Kvarda, M. Omasta, Lu-bing Shi, H. Ding, Wen-jian Wang, I. Křupka, M. Hartl
{"title":"Wheel Squeal Noise in Rail Transport: The Effect of Friction Modifier Composition","authors":"V. Navrátil, R. Galas, M. Klapka, Daniel Kvarda, M. Omasta, Lu-bing Shi, H. Ding, Wen-jian Wang, I. Křupka, M. Hartl","doi":"10.24874/ti.1211.11.21.02","DOIUrl":"https://doi.org/10.24874/ti.1211.11.21.02","url":null,"abstract":"","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42742041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1223.11.21.03
Y. P. Ravitej, C. B. Mohan, M. G. Ananthaprasad
done using Minitab software where SN ratio, probability, ANOVA, and a regression model are analyzed. Obtained experimental wear properties are validated using artificial neural networks (ANN) by training the neurons where good agreement is obtained (R 2 = 0.98). Present research encapsulates the effect of different wear parameters like applied load, sliding speed, and sliding distance on the wear rate of LM13/zircon/C (hybrid metal matrix composites), and experimental wear results are correlated with artificial neural network (ANN) by training the algorithm.
{"title":"Dry Sliding Friction and Wear Behavior of LM13/Zircon/Carbon (HMMC’s): An Experimental, Statistical and Artificial Neural Network Approach","authors":"Y. P. Ravitej, C. B. Mohan, M. G. Ananthaprasad","doi":"10.24874/ti.1223.11.21.03","DOIUrl":"https://doi.org/10.24874/ti.1223.11.21.03","url":null,"abstract":"done using Minitab software where SN ratio, probability, ANOVA, and a regression model are analyzed. Obtained experimental wear properties are validated using artificial neural networks (ANN) by training the neurons where good agreement is obtained (R 2 = 0.98). Present research encapsulates the effect of different wear parameters like applied load, sliding speed, and sliding distance on the wear rate of LM13/zircon/C (hybrid metal matrix composites), and experimental wear results are correlated with artificial neural network (ANN) by training the algorithm.","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47146739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1244.01.22.05
B. Suresha, A. Padmanabha, A. Ramesh, Ishwara Prasanna Shirankallu, Shiva Charan Mysore Ramalingappa
{"title":"A Fundamental Investigation of the Tribological Performance of Mahua-Castor Blend in a Four-Ball Tester","authors":"B. Suresha, A. Padmanabha, A. Ramesh, Ishwara Prasanna Shirankallu, Shiva Charan Mysore Ramalingappa","doi":"10.24874/ti.1244.01.22.05","DOIUrl":"https://doi.org/10.24874/ti.1244.01.22.05","url":null,"abstract":"","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42025354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-15DOI: 10.24874/ti.1298.05.22.08
Victor Vojtov, Katherine Fenenko, A. Voitov, A. Hrynkiv, O. Lyashuk, Y. Vovk
and identify surface processes during wear
并识别磨损过程中的表面过程
{"title":"Methodical Approach to Using Acoustic Emission Method for Tribosystem Monitoring","authors":"Victor Vojtov, Katherine Fenenko, A. Voitov, A. Hrynkiv, O. Lyashuk, Y. Vovk","doi":"10.24874/ti.1298.05.22.08","DOIUrl":"https://doi.org/10.24874/ti.1298.05.22.08","url":null,"abstract":"and identify surface processes during wear","PeriodicalId":23320,"journal":{"name":"Tribology in Industry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47808206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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