A Critical Review on Improving the Fatigue Life and Corrosion Properties of Magnesium Alloys via the Technique of Adding Different Elements

Magnesium is the eighth-most abundant element in the world and its alloys have a widespread application in various industries such as electronic and transport (i.e., air, land, and sea) engineering, due to their significant mechanical properties, excellent machinability, high strength to weight ratios, and low cost. Although monolithic Mg metal is known as the lightest industrial metal (magnesium density is 30% less than the density of the aluminum, and this unique property increases the attractiveness of its usage in the transportation industry), one of the significant limitations of magnesium, which affects on its applications in various industries, is very high reactivity of this metal (magnesium with an electronegativity of 31.1 can give electrons to almost all metals and corrodes quickly). To overcome this problem, scholars are trying to produce magnesium (Mg) alloys that are more resistant to a variety of loads and environmental conditions. In this regard, Mg alloys include well-known materials such as aluminum (Al), Zinc (Zn), Manganese (Mn), Silicon (Si), and Copper (Cu), etc., and their amount directly affects the properties of final products. In the present review paper, the authors attempted to present the latest achievements, methods, and influential factors (finish-rolling, pore defects, pH value, microstructure, and manufacturing processes, etc.) on the fatigue life and corrosion resistance of most significant Mg alloys, including AM50, AM60, AZ31, AZ61, AZ80, AZ91, ZK60, and WE43, under various conditions. The summarized results and practical hints presented in this paper can be very useful to enhance the reliability and quality of Mg-made structures

Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints

© 2020 The Society of Manufacturing Engineers. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Ultrasonic Testing (UT) is one of the well-known Non-Destructive Techniques (NDT) of spot-weld inspection in the advanced industries, especially in automotive industry. However, the relationship between the UT results and strength of the spot-welded joints subjected to various loading conditions isunknown. The main purpose of this research is to present an integrated search system as a new approach for assessment of tensile strength and fatigue behavior of the spot-welded joints. To this end, Resistance Spot Weld (RSW) specimens of three-sheets were made of different types of low carbon steel. Afterward, the ultrasonic tests were carried out and the pulse-echo data of each sample were extracted utilizing Image Processing Technique (IPT). Several experiments (tensile and axial fatigue tests) were performed to study the mechanical properties of RSW joints of multiple sheets. The novel approach of the present research is to provide a new methodology for static strength and fatigue life assessment of three-sheets RSW joints based on the UT results by utilizing Artificial Neural Network (ANN) simulation. Next, Genetic Algorithm (GA) was used to optimize the structure of ANN. This approach helps to decrease the number of tests and the cost of performing destructive tests with appropriate reliability.Peer reviewe

A Novel Approach for Analyzing the Effects of Almen Intensity on the Residual Stress and Hardness of Shot-Peened (TiB + TiC)/Ti–6Al–4V Composite: Deep Learning

In the present study, the experimental data of a shot-peened (TiB + TiC)/Ti–6Al–4V composite with two volume fractions of 5 and 8% for TiB + TiC reinforcements were used to develop a neural network based on the deep learning technique. In this regard, the distributions of hardness and residual stresses through the depth of the materials as the properties affected by shot peening (SP) treatment were modeled via the deep neural network. The values of the TiB + TiC content, Almen intensity, and depth from the surface were considered as the inputs, and the corresponding measured values of the residual stresses and hardness were regarded as the outputs. In addition, the surface coverage parameter was assumed to be constant in all samples, and only changes in the Almen intensity were considered as the SP process parameter. Using the presented deep neural network (DNN) model, the distributions of hardness and residual stress from the top surface to the core material were continuously evaluated for different combinations of input parameters, including the Almen intensity of the SP process and the volume fractions of the composite reinforcements

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

In this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 mu m were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10-15 mu m had the highest effects on fatigue life improvement

Effects of Axial and Multiaxial Variable Amplitude Loading Conditions on the Fatigue Life Assessment of Automotive Steering Knuckle

In this paper, the author has attempted to investigate the effects of different loading conditions including axial and multiaxial variable amplitude loading (VAL) on the fatigue life assessment of automotive components under various maneuvers. To this end, a case study was conducted on the cast iron steering knuckle of a passenger car. In fact, the various VAL histories are entered on the three joints of knuckle, namely steering linkage, lower control arm, and MacPherson strut. However, previous studies have shown that this high super-critical component fails through the steering linkage. Moreover, the rotation of the steering linkage is the most destructive load. Hence, in this research, different loading cases such as axial (destructive load as means 1 channel), multiaxial (only relates to loading on the joint of knuckle and steering linkage means 3 channels), and full multiaxial (including all loading time histories means 9 channels) were considered. Afterward, finite element analysis was performed for each case, and fatigue life of the component was predicted under different conditions. Next, fatigue life of the component was evaluated using the time histories of stress tensor in the root of steering linkage which is extracted by transient dynamic analysis and applying probabilistic approach based on the Liu–Zenner equivalent stress criterion. Eventually, the responses from both techniques were compared in different cases. The results reveal that life predicted using two methods are slightly different. But, the results of probabilistic approach are more accurate than the results of FEM in comparison with experimental data for the axial state. Also, one of the major achievements of this study is that for the components with complex geometry and under multi-input loading like the steering knuckle, it is essential to perform fatigue analysis by considering all real conditions and cannot be only focused to the destructive loading. © 2020, ASM International

EFFECTS OF THE HARDENED NICKEL COATING ON THE FATIGUE BEHAVIOR OF CK45 STEEL: EXPERIMENTAL, FINITE ELEMENT METHOD, AND ARTIFICIAL NEURAL NETWORK MODELING

Hardened nickel coating is widely used in many industrial applications and manufacturing processes because of its benefits in improving the corrosion fatigue life. It is clear that increasing the coating thickness provides good protection against corrosion. However, it reduces the fatigue life. Thus, applying a thin layer of coated nickel might give an acceptable corrosion protection with minimum loss of the fatigue life. In the present study, the effects of hardened nickel coating with different thicknesses on the fatigue behavior of CK45 mild steel were experimentally investigated. After conducting the experimental tests, we carried out two different modeling approaches of finite element method (FEM) and artificial neural network (ANN). In the FEM modeling, an attempt was made to analyze the fatigue of the components by modeling the interface phase between the base metal and coating more accurately and using the spring elements; ANNs were developed based on the back propagation (BP) error algorithm. The comparison of the obtained results from FEM and ANN modeling with the experimental values indicates that both of the modeling approaches were tuned finely

New neural network-based algorithm for predicting fatigue life of aluminum alloys in terms of machining parameters

Various classification of aluminum alloys is one of the most common applied materials in transportation industries due to the specific mechanical and material properties. Body of vehicles, including cars, ships, and airplanes, are constantly exposed to different cyclic loads that lead to surface damage and finally, fatigue failure occurs by crack growth. Therefore, the quality of the machined surface has a direct effect on the fatigue life of the products. In this regard, the most important source of surface roughness is the choice of machining parameters. To understand it, turning operations were performed on 2xxx and 7xxx series aluminum alloys considering different values of process parameters. For any series of aluminums, 189 high-cycle fatigue testing specimens were prepared, and experiments were performed by axial tension–compression fatigue test machine in 7 levels of stress (all tests were repeated three times and the mean failure cycles were reported as the fatigue life). Next, an Artificial Neural Network (ANN) based on the Back Propagation (BP) error algorithm was developed to predict fatigue life of Al alloys which are machined with different conditions. To this end, the parameters considered as input variables to the neural network structure include the Yield Strength (YS) and Ultimate Tensile Strength (UTS) to identify the aluminum series, as well as the process parameters such as cutting depth (d), rotational speed (R), and feed speed (V). In addition, the applied cyclic stress (S) to specimen was considered as input. Furthermore, surface roughness (Z) and number of cycles to failure (N) were considered as ANN output. The comparison of the obtained results from ANN predicting with the experimental values indicates that this approach was tuned finely. Eventually, the presented model can be a suitable alternative to perform fatigue tests with high costs and time-consuming.‌‌ Also, the most effective machining parameter on the surface roughness and fatigue limit was reported

Experimental Study of the Effect of Vehicle Velocity on the Ride Comfort of a Car on a Road with Different Types of Roughness

Vehicle speed control is one of the most important factors at increasing safety, reducing the number and severity of road accidents. Also, other factors such as inappropriate designing, the overuse of speed bumps, nonconformity to the prevailing standards of the world in construction, maintenance, and repair of roads can cause damage to the car suspension and consequently brings the discomfort of car occupants when crossing the actual road under different driving conditions (various speeds and maneuvers). In the present paper, the effect of different vehicle velocities on the comfort of occupants was investigated empirically. To this end, a passenger vehicle was driven at different constant speeds of 30, 40, and 50 km/h on the actual road which has various types of speed bumps and uneven such as sewer door. Also, the accelerometer sensor of mobile was used to extract the acceleration time histories entered on occupants in three different directions X, Y, and Z. Finally, the BS 6841 and ISO 2631-1 standards were used to compare the laboratory-measured vertical acceleration at different speeds with the vertical acceleration that occupants feel comfortable. The results indicated that the maximum speed for not feeling the discomfort of occupants is 67 km/h. Moreover, the maximum human tolerance was obtained approximately 178 min of continuous travel. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd

Comparison of some selected time-domain fatigue failure criteria dedicated for multi input random non-proportional loading conditions in industrial components

This paper presents time-domain procedure to evaluate High-Cycle Fatigue (HCF) life of industrial components with complicated geometries and subjected to multi-input random loading conditions. To this end, the authors discuss some well-known equivalent stress criteria which can consider the orientation of critical plane changes with the time in non-proportional loading conditions to estimate the time history of equivalent stress. This task is successfully accomplished by knowing time histories of local stress tensor components in a critical area of the part that is prone to failure. Next, two different categories were used to predict fatigue life of component: 1- Linear Damage Accumulation (LDA) rule by employing Rain-flow cycle counting and 2- probabilistic approach by utilizing level crossing counting method. To apply these methodologies and awareness of their challenges, a case study was performed on the automotive steering knuckle. Finally, the predicted lives were compared to experimental results, and the accuracies of different fatigue life assessment techniques were reported. The obtained results show that combination of Rain-flow cycle counting with the Liu-Zenner criterion and especially, with the Energy-based instant fatigue damage tracing proposed by Shariyat lead to the highest accuracies. However, using the probabilistic approach, we will get the response in a much shorter time compared to another category. Therefore, the choice which method to use also depends on the parameters of the research situation, and the interaction of the two factors of prediction accuracy and prediction time in the industry