2篇
您的检索式：作者名="Cihan OZEL"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms显示文摘In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding were determined.The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory.The linear regression(LR),support vector machine(SVM),and Gaussian process regression(GPR)algorithms are used for predicting wear quantities.A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms. | Osman ALTAY Turan GURGENC Mustafa ULAS Cihan OZEL | 2020 | Friction2020，8，1： | 2 |
| 2 | A new approach for prediction of the wear loss of PTA surface coatings using artificial neural network and basic,kernel-based,and weighted extreme learning machine显示文摘Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost,labor,and time.In this study,wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding(PTAW)method with FeCrC,FeW,and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group.The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests.The wear tests were performed at three different loads(19.62,39.24,and 58.86 N)over a sliding distance of 900 m.In this study,models have been developed by using four different machine learning algorithms(an artificial neural network(ANN),extreme learning machine(ELM),kernel-based extreme learning machine(KELM),and weighted extreme learning machine(WELM))on the data set obtained from the wear test experiments.The R2 value was calculated as 0.9729 in the model designed with WELM,which obtained the best performance among the models evaluated. | Mustafa ULAS Osman ALTAY Turan GURGENC Cihan OZEL | 2020 | Friction2020，8，6： | 2 |
您所在的机构暂未开通此部分的功能，请联系管理员