Four cushion adhesives (Fittydent I; Fittydent II, introduced as an advanced formula; Protefix; and Seabond) were compared clinically through patients subjective evaluations. Maxillary dentures of 32 patients were relined and the mandibular dentures were scored by Kapur index before the application of different treatments. The cushion adhesives were used only with the existing mandibular prosthesis. The four treatments were applied to the patients in groups of 8 by allocating each group randomly to one of the four sequences of treatments determined by latin square design. Each patient took each treatment only once. The adhesives were applied by the authors as recommended by the manufacturers. Patients used each material for 24 h and, through a questionnaire, they evaluated the seven following aspects of each different cushion material: retention, duration of retention, effects on ability to chew, effects on other oral functions, cleansing of dentures, cleansing of gums, and an overall evaluation of materials. Both Fittydent products significantly improved denture retention and the ability to chew. The patients who expressed 'much better' chewing with Fittydent products, had mandibular dentures rated poor or fair.

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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.

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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 [with 11among the models evaluated.