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Slider-crank mechanisms have a wide range of applications in machine design, primarily for converting rotary motion into reciprocating motion, as seen in internal combustion engines, pumps, compressors, and human-powered vehicles. Due to their widespread usage, analytical and graphical solutions for the position analysis of slider-crank mechanisms are introduced in various textbooks and lecture notes. Recently, artificial neural network algorithms have been applied to various design applications across different research fields, and mechanism design is no exception. However, investigations into the application of neural network algorithms specifically to slider-crank mechanisms are quite limited. In this study, the analytical position analysis of the slider-crank mechanism is presented, and datasets are obtained first. Subsequently, the application of following three different supervised learning algorithms to the position analysis of the slider-crank (R-RRT) mechanism is investigated using analytical solution datasets: the Levenberg-Marquardt Backpropagation (LM) algorithm, Bayesian Regularization (BR) algorithm, and Scaled Conjugate Gradient Descent (SCG) algorithm. These three algorithms were chosen due to their distinct characteristics. The main objectives of this study are to highlight the differences among various neural network algorithms' applications and to understand the applicability and suitability of different neural network algorithms for kinematic position analysis in mechanisms. The results indicate that the Bayesian regularization algorithm yields the best results, while the Levenberg-Marquardt Backpropagation algorithm exhibits the best performance.

The conventional approach of the mechanism design process, generally, has a two-step procedure: Kinematic synthesis/analysis of the mechanism in the first step and optimization of the synthesized/analyzed mechanism based on optimization criteria in the second step. This study presents an approach that combines kinematic synthesis with the static balancing of the same, and optimization, into a one-step procedure. As an example of this one-step design process, a tension-spring assisted four-bar hood linkage optimal synthesis and design is performed in one-step. This one-step solution includes kinematic synthesis and analysis of the hood linkage, virtual work, static balancing with tension spring, and optimization in the presence of joint friction. The resulting design requires a minimum force to raise and lower the hood in the presence of unknown optimum levels of joint friction while the hood is statically balanced for its entire range of motion. A total of twelve different scenarios are investigated and the results are discussed.

Objective: Hip fractures due to domestic low-energy traumas are common problems that can cause significant morbidity and mortality in the elderly population. Since the Covid-19 outbreak, although the lock-down of older people could decrease contagion,the incidence of domestic traumas did not decrease, and led to development of various comorbidities. In this study, we aimed to compare the epidemiology of hip fractures in 6 months of the pandemic in 2020, with the equivalent 6-month period in the previous year to determine the relationship between lock-down and hip fractures due to domestic falls. Method: In this retrospective study, patients over 65 years old who were hospitalized due to hip fractures between May 1st, 2020 - November 30th, 2020 were compared with the similar cohort hospitalized between May 1st, 2019 - November 30th, 2019. The patients were compared in terms of demographic characteristics, American Society of Anesthesiologists (ASA) scores, type of anesthesia, time until surgery, comorbidities, surgical treatment modality, need for postoperative primary care unit, postoperative complications, and mortality. Results: Overall, 210 patients were included in our study. There was no significant difference between the groups regarding demographic characteristics and ASA scores. In the 2020 group, there were eight patients (8.6%) who were Covid-19 PCR- positive. There was no death proven by PCR that was related to COVID-19 disease. Time to surgery, fracture type, anesthesia type did not differ between the two groups. The need for a postoperative primary care unit was higher in the 2020 group. Ninety-day mortality rates were not significantly different between the two groups. Conclusions: In conclusion, despite higher mortality rates described in the literature, there was no Covid -19 associated mortality in our study. Hip fractures in the elderly did not alter with social mobilization; hence they usually occur due to domestic low- energy traumas. Reporting of the information in this patient group by all centers will provide important data in the management of hip fractures in this special process.

This study investigates the effects of filler content and type on the mechanical (tensile strength and impact resistance) and tribological properties of woven glass fiber‐reinforced composites (WGFC) produced using three different nanofillers (MgO, CuO, and Al2O3) at various loading levels (0.7, 1.4, and 2.8 wt%). Additionally, analyses of the artificial neural network algorithm were reported for the predictions of wear rate, mass loss, and coefficient of friction (COF) by given applied load, sliding distance, filler ratio, and filler type. The composites were fabricated using the hand lay‐up method. The filled composites were characterized using Fourier Transform Infrared (FTIR) spectroscopy, and the worn surfaces of the samples after the wear test were analyzed using Scanning Electron Microscopy (SEM). The wear resistance of the composite samples improved at 0.7 wt% CuO and 1.4 wt% MgO filler contents. At 200 m sliding distance and 1.4 wt% filler ratio, the highest wear ratio was determined as 55 mm3/m × 10−3 in the Al2O3‐filled composite, and the lowest was determined as 31 mm3/m × 10−3 in the MgO‐filled composite. At 1.4% filler ratio and 15 N load, the lowest friction coefficient was determined as 0.45 μ in MgO‐filled composite, and the highest was 0.56 μ in Al2O3‐filled composite. Al2O3 filler increased the mass loss and wear rate of the composite material. In Charpy impact tests, the maximum impact energy of 13.9 J/m2 was obtained with a 2.8 wt% Al2O3 filler content. Tensile tests showed an increase in tensile strength for all filler contents compared to unfilled composites. The highest tensile strength of 242.1 MPa was achieved with 0.7 wt% CuO reinforcement. Highlights: In all applied wear parameters and filler ratios, the Al2O3 filler adversely affected the wear resistance of the composite, while the MgO filler showed the best performance in terms of wear resistance.All types and ratios of fillers increased the tensile strength compared to the unfilled composite.Contrary to its negative effect on wear, the Al2O3‐filled composites exhibited the highest impact resistance.The prediction analysis of the tribological behavior of composite material using ANN reveals that the Levenberg–Marquardt algorithm fits very well. [ABSTRACT FROM AUTHOR]

Slider-crank mechanisms have a wide range of applications in machine design, primarily for converting rotary motion into reciprocating motion, as seen in internal combustion engines, pumps, compressors, and human-powered vehicles. Due to their widespread usage, analytical and graphical solutions for the position analysis of slider-crank mechanisms are introduced in various textbooks and lecture notes. Recently, artificial neural network algorithms have been applied to various design applications across different research fields, and mechanism design is no exception. However, investigations into the application of neural network algorithms specifically to slider-crank mechanisms are quite limited. In this study, the analytical position analysis of the slider-crank mechanism is presented, and datasets are obtained first. Subsequently, the application of following three different supervised learning algorithms to the position analysis of the slider-crank (R-RRT) mechanism is investigated using analytical solution datasets: the Levenberg-Marquardt Backpropagation (LM) algorithm, Bayesian Regularization (BR) algorithm, and Scaled Conjugate Gradient Descent (SCG) algorithm. These three algorithms were chosen due to their distinct characteristics. The main objectives of this study are to highlight the differences among various neural network algorithms' applications and to understand the applicability and suitability of different neural network algorithms for kinematic position analysis in mechanisms. The results indicate that the Bayesian regularization algorithm yields the best results, while the Levenberg-Marquardt Backpropagation algorithm exhibits the best performance. [ABSTRACT FROM AUTHOR]

Equipment such as a rod between vehicle body and hood or a lift strut are used to keep automobile hood in open positions during the regular maintenances or repairs. Moreover, some effort is required to open or close automobile hood because of its weight. To keep it in various opened positions and to exert the least force to open or close it, a mechanism is used in conjunction with extension, compression or torsion springs in two different configurations. Each optimization has been carried out for all these different springs, each in two configurations and results are compared. Sensitivity analysis for the design variables are examined to determine how these variables are influential on the designed mechanism. Optimization results of extension and compression springs are observed to be similar in both configurations of the designed mechanism. On the other hand, results for the torsion spring in two configurations are exactly the same for the mechanism. Sensitivity analysis shows that the designed mechanism is not sensitive on the design variables that are on the bound in both of these two configurations.