Abrasive wear behaviour of 27MnB5 steel used in agricultural tines

Understanding the wear mechanisms in wear parts is a crucial element of tribological investigation, particularly in agricultural applications where the knowledge about abrasive micro-mechanisms of soil engaging tools are limited. In the current research, symmetrical skew wedge cultivator tines of 27MnB5 steel were wear tested to investigate the change in mass, linear dimensions, hardness and microstructure, aiming at prolonging the lifetime of these parts through design and material. The wear mechanisms were identified and characterized by non-contact 3D optical profilometry. Test results clearly shows a zone specific wear micro-mechanism based on the tine geometry. The cutting edge of the tine can be segmented into micro-cutting and micro-ploughing zone. Vickers hardness and microstructural analysis were performed on the cross-section of the sliding interface. Tribolayer was observed on the worn surface. Degree of penetration from the wear scratches was calculated to justify the wear micro-mechanisms. A Discrete Element Method (DEM) model was developed to investigate the soil flow during the tillage process. The model results and field test wear scars are in good agreement with each other with respect to the wear patterns

Analytical Comparison of Two Different Redundancy Concepts for Switched Reluctance Machines

Abstract In this paper two different, fully redundant SRM (switched reluctance machine) topologies are compared: Firstly a 6/4-topology with two identical motors on one axis, and secondly a 12/8-topology, where one 3-phase-system uses every other stator tooth (and the second, redundant, 3-phase-system uses the rest of the stator teeth). The following calculation will be performed using analytical formulae to get a fast and clear comparison. The nonlinearity caused by the usual saturation of SRM is covered by a simple correction factor: As the relative comparison of the two redundancy concepts is of interest, this method leads to qualitative and quantitative good results. In addition, a very good starting point for the detailed FEM-refinement of the most promising alternative is generated

Generation of artificial road profile for automobile spring durability analysis

This paper presents the use of a generated artificial road profile in the simulation of a quarter car model for spring durability based-force extraction. In situ measurement of the road loading profile for automotive spring durability analysis, requires considerable cost and effort due to the complex experimental setup. Hence, an artificial road profile was generated for the quarter car model simulation to obtain the spring force signals. Initially, according to the ISO 8608 standard, a class “A” artificial road profile was generated using a designated waviness value, unevenness index and random phase angle. The generated road profile was used as the input to a constructed quarter car model to obtain the spring force signals. Subsequently, the generated nominal force signal was used to predict the fatigue life of the spring. Moreover, to obtain the localise fatigue behaviour of the spring, a finite element spring model together with the force signal was used for fatigue prediction. Under this class “A” road excitation, the spring possessed very high fatigue life of 1.87 × 106 blocks to failure. Further, a series of spring variant was analysed for fatigue life through this road class excitation. The relationship between spring stiffness and fatigue lives established using power regression and the coefficient of determination (R2) as high as 0.9815 was obtained. Therefore, this analysis will assist in automobile spring design regarding fatigue when road load data is not available

Bump Energy for Durability Prediction of Coil Spring Based on Local Regularity Analysis

This paper aims to study the identification of bumps in vibrational signals and develop bump-energy-based durability predictive models for a suspension coil spring. The bump energy of the loading signal is affected by high frequency noises and can lead to inaccurate results. Therefore, it is necessary to eliminate high frequency noise during bump identification. Local regularity analysis was employed to determine the singular points in road signals. Bump signals were then reconstructed from these singular points. Subsequently, bump-energy-based models were developed by correlating with the fatigue lives estimated using the Coffin–Manson, Morrow and Smith–Watson–Topper strain-life models. The results show that the bump signals extracted from the road excitations had a frequency band within 0–50 Hz, indicating that the high frequency noises had been successfully removed during extraction of the bumps. The bump-energy-based models predicted a fatigue life ranging from 3.98x104 to 4x109 cycles within a 95% confidence interval, where the Coffin–Manson-based model showed the highest fatigue life. This is because the Coffin–Manson model did not consider the mean stress effects. When compared with the experimental results, the Coffin–Manson-based model indicates the highest accuracy, given its highest R2 of 0.948. The bump-energy-based models developed in this study contributed an accurate durability prediction of coil springs

Practice and Effectiveness of Outpatient Psycho-Oncological Counseling for Cancer Patients

Objective: Because of various types of psychological distress, cancer patients are encouraged to attend outpatient psycho-oncological and psychosocial counseling. The aim of this prospective study was an analysis of the impact and success of existing counseling resources

Customer active power consumption prediction for the next day based on historical profile

Energy consumption prediction application is one of the most important fieldsthat is artificially controlled with Artificial Intelligence technologies to maintainaccuracy for electricity market costs reduction. This work presents a way to buildand apply a model to each costumer in residential buildings. This model is built by using Long Short Term Memory (LSTM) networks to address a demonstration of time-series prediction problem and Deep Learning to take into consideration the historical consumption of customers and hourly load profiles in order to predict future consumption. Using this model, the most probable sequence of a certain industrial customer’s consumption levels for a coming day is predicted. In the case of residential customers, determining the particular period of the prediction in terms of either a year or a month would be helpful and more accurate due to changes in consumption according to the changes in temperature and weather conditions in general. Both of them are used together in this research work to make a wide or narrow prediction window.A test data set for a set of customers is used. Consumption readings for anycustomer in the test data set applying LSTM model are varying between minimum and maximum values of active power consumption. These values are always alternating during the day according to customer consumption behavior. This consumption variation leads to leveling all readings to be determined in a finite set and deterministic values. These levels could be then used in building the prediction model. Levels of consumption’s are modeling states in the transition matrix. Twenty five readings are recorded per day on each hour and cover leap years extra ones. Emission matrix is built using twenty five values numbered from one to twenty five and represent the observations. Calculating probabilities of being in each level (node) is also covered. Logistic Regression Algorithm is used to determine the most probable nodes for the next 25 hours in case of residential or industrial customers.Index Terms—Smart Grids, Load Forecasting, Consumption Prediction, Long Short Term Memory (LSTM), Logistic Regression Algorithm, Load Profile, Electrical Consumption.</p