A theoretical and experimental investigation into the development of coverage in shot peening

Shot peening is a mechanical surface treatment process used mainly for the improvement of the fatigue structural integrity of metallic components. In this process, the surface of a part is bombarded with small spherical media called shot, at high velocity, to induce desirable residual compressive stresses and strains within the surface layers of the component The effectiveness of the shot peemng process 1s dependent upon the uniformity of the induced compressive residual stresses and the energy transfer that occurs during the impact of the shots with the target surface. In practice, the process efficiency is established by means of coverage, intensity and saturation. Therefore, the scope of this study is to investigate the development of coverage and its relationship to intensity and saturation of peening. Within the scope, the objectives of the study are to compare and contrast the coverage results obtained experimentally with theoretical models, to establish a relationship between coverage and intensity and to obtain an empirical relationship to predict coverage. Theoretical models used to predict coverage give mixed results compared to experimental results. The Holdgate model gives a very good coverage prediction whereas the A vrami equation does not Coverage development is found to be a function of shot size, impingement angle and target material properties. Intensity and saturation time is found to be dependent upon shot size and impingement angle. Complete coverage is achieved earlier than the saturation point which 1s a contrast to the usual assumption that coverage and saturation occurs at the same time. However, a clear relationship could not be established. An empirical relationship can be used to predict coverage. This relationship, which is a function of the process parameters such as shot size and impingement angle, is established by using multiple regression analysis

Numerical analysis of low-velocity impact of carbon-basalt/epoxy hybrid laminates

349-355In this paper, an attempt has been made to numerically investigate the transient dynamic response of carbon-basalt/epoxy laminated composites subjected to low velocity impact. Carbon laminates are expensive hence, inclusion of cheaper basalt to obtain an improved, yet economical laminate is necessary. Finite element analysis (FEA) technique has been employed to simulate the laminated models. Loading profiles and test conditions from drop-weight tests have been obtained from literatures and necessary validation of FEA has also been performed. A correlation between impactor mass and velocity on the maximum laminate centre deflection has been established. In addition, the influence of hybrid stacking sequence and carbon position in the hybrid on laminate damage response has been studied. It has been observed from the study that hybrid 3 (H3) with stacking sequence CCBC-2 (best combination) showed the least deflection of all the stacking sequence sets and has the lowest deflection in all the low velocity impact testing conditions

Failure pressure prediction of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loadings using FEM and ANN

Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R2 of 0.9921, with the percentage error ranging fro

Modeling and Simulation of PMSG Wind Energy Conversion System using Active Disturbance Rejection Control

Electrical power generated from wind turbines inherently fluctuates due to changing wind speeds. Without proper control, disturbances such as changing wind speeds can degrade the power quality factor and robustness of the electrical grid. To ensure good power quality factor, high performance and robustness of the grid against internal and external disturbances, the use of Active Disturbance Rejection Control with an extended state observer ESO for a PMSG Wind Energy Conversion System is investigated. The system has been simulated in MATLAB/Simulink at various wind speeds. The obtained simulation results indicate that the controller maintains constant DC voltage at the interface of the generator-side converter and grid-side converters and achieves maximum power. The results also show that the system performance has good stability, precision and rejection of internal disturbances, with an overall system efficiency of 98.65%

A New Roll and Pitch Control Mechanism for an Underwater Glider

In this paper, a new roll and pitch control mechanism for an underwater glider is described. The mechanism controls the glider’s pitch and roll without the use of a conventional buoyancy engine or movable mass. It uses water as trim mass, with a high flow rate water pump to shift water from water bladders located at the front, rear, left, and right of the glider. By shifting water between the left and right water bladder, a roll moment is induced. Similarly, pitch is achieved by shifting water between the front and rear water bladders. The water bladders act not only as a means for roll and pitch control but as a buoyancy engine as well. This eliminates the use of a dedicated mechanism for pitch and roll, thereby improving gliding efficiency and energy consumption, as the glider's overall size is decreased since the hardware required is reduced. The dynamics of the system were derived and simulated, as well as validated experimentally. The glider is able to move in a sawtooth pattern with a maximum pitch angle of 43.5˚, as well as a maximum roll angle of 43.6˚ with pitch and roll rates increase with increasing pump rate

ANN- and FEA-Based Assessment Equation for a Corroded Pipeline with a Single Corrosion Defect

Most of the standards available for the assessment of the failure pressure of corroded pipelines are limited in their ability to assess complex loadings, and their estimations are conservative. To overcome this research gap, this study employed an artificial neural network (ANN) model trained with data obtained using the finite element method (FEM) to develop an assessment equation to predict the failure pressure of a corroded pipeline with a single corrosion defect. A finite element analysis (FEA) of medium-toughness pipelines (API 5L X65) subjected to combined loads of internal pressure and longitudinal compressive stress was carried out. The results from the FEA with various corrosion geometric parameters and loads were used as the training dataset for the ANN. After the ANN was trained, its performance was evaluated, and its weights and biases were obtained for the development of a corrosion assessment equation. The prediction from the newly developed equation has a good correlation value, R2 of 0.9998, with percentage errors ranging from −1.16% to 1.78%, when compared with the FEA results. When compared with the failure pressure estimates based on the Det Norske Veritas (DNV-RP-F101) guidelines, the standard was more conservative in its prediction than the assessment equation developed in this study

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings

Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis

An Artificial Neural Network-Based Equation for Predicting the Remaining Strength of Mid-to-High Strength Pipelines with a Single Corrosion Defect

Numerical methods such as finite element analysis (FEA) can accurately predict remaining strength, with strong correlation with actual burst tests. However, parametric studies with FEA are time and computationally intensive. Alternatively, an artificial neural network-based equation can be used. In this work, an equation for predicting the remaining strength of mid-to-high strength pipelines (API 5L X52, X65, and X80) with a single corrosion defect subjected to combined loadings of internal pressure and longitudinal compressive stress was derived from an ANN model trained based on FEA results. For FEA, the pipe was assumed to be isotropic and homogenous, and the effects of temperature on the pipe failure pressure were not considered. The error of remaining strength predictions, based on the equation, ranged fro

Failure pressure prediction of high toughness pipeline with a single corrosion defect subjected to combined loadings using artificial neural network (ANN)

Conventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. This paper proposes an analytical equation based on finite element analysis (FEA) for the failure pressure prediction of a high toughness corroded pipeline with a single corrosion defect subjected to internal pressure and axial compressive stress. The equation was developed based on the weights and biases of an Artificial Neural Network (ANN) model trained with failure pressure from finite element analysis (FEA) of a high toughness pipeline for various defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials and was found to be capable of making accurate predictions with a coefficient of determination (R2) of 0.99. An extensive parametric study using the proposed model was subsequently conducted to determine the effects of defect length, defect depth, and axial compressive stress on the failure pressure of a corroded pipe with a single defect. The application of ANN together with FEA has shown promising results in the development of an empirical solution for the failure pressure prediction of pipes with a single corrosion defect subjected to internal pressure and axial compressive stress

Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network

Conventional pipeline failure pressure assessment codes do not allow for failure pressure prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with a longitudinal interacting corrosion defect subjected to combined loadings is proposed. An artificial neural network (ANN) trained with failure pressure obtained from finite element analysis (FEA) of API 5L X52, X65, and X80 pipes for varied defect spacings, depths and lengths, and axial compressive stress were used to develop the equation. Subsequently, a parametric study on the effects of the defect spacing, length, and depth, and axial compressive stress on the failure pressure of a corroded pipe with longitudinal interacting defects was performed to demonstrate a correlation between defect geometries and failure pressure of API 5L X52, X65, and X80 pipes, using the equation. The new equation predicted failure pressures for these pipe grades with a coefficient of determination (R2) value of 0.9930 and an error range of −10.00% to 1.22% for normalized defect spacings of 0.00 to 3.00, normalized effective defect lengths of 0.00 to 2.95, normalized effective defect depths of 0.00 to 0.80, and normalized axial compressive stress of 0.00 to 0.80