This study contributes to examine the impact of design variations on self-piercing riveting (SPR) in dissimilar metal joints, focusing on DP780, DP980, 1180MS steel, and Al6061 aluminum sheets with total thicknesses r...
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This study contributes to examine the impact of design variations on self-piercing riveting (SPR) in dissimilar metal joints, focusing on DP780, DP980, 1180MS steel, and Al6061 aluminum sheets with total thicknesses ranging from 2 to 4 mm. The study aligns with the target for aluminum content in light vehicles, which is set at 570 net pounds per vehicle by 2030. On the other hand, dual-phase steel combinations are crucial in the automotive industry due to their superior strength-to-weight ratio and excellent formability, which enhance vehicle safety and fuel efficiency. To address these factors, the study presents several key innovations: (1) the development of a novel artificial neural network (ann) model for predicting riveting quality and mechanical properties, capturing parameters not covered by simulations alone;(2) the use of a contour graph method to optimize sheet thickness and die depth, introducing a new approach for achieving optimal SPR results;and (3) the establishment of a new correlation between process chain quality and shear test evaluations for self-piercing rivets in dissimilar metals. Results show that a die depth of 2.25 mm is most effective for joining 1-mm (1180MS) and 2-mm (Al6061) materials, achieving a maximum tensile force of 9.26 kN and absorbing up to 36.02 J of energy. The ann model demonstrated high prediction accuracy with MAPEs ranging from 7.56 to 15.8%, highlighting its potential for integration into industrial applications. By allowing precise prediction of joint performance, the ann model and the contour graph offer a transformative tool to optimize the SPR process, minimize development costs, and improve production efficiency in automotive manufacturing.
Voltage unbalance is an important power quality issue, which occurs in electrical power systems (EPSs) and causes severe problems for them. In this work, a general mathematical model for EPS including its long transmi...
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Voltage unbalance is an important power quality issue, which occurs in electrical power systems (EPSs) and causes severe problems for them. In this work, a general mathematical model for EPS including its long transmission line is developed using the generalised circuit parameters method. Then, a hybrid Particle Swarm Optimisation-Artificial Neural Network (PSO-ann) algorithm is proposed to overcome the voltage unbalance problem by controlling the firing angles of thyristor-controlled reactor (TCR) which varies the amount of reactive power at the load side. PSO algorithm is responsible for determining the optimal set of TCR firing angles required to retrieve the balance conditions in offline mode for different load changes, employing the developed mathematical model of the EPS. Then, a dataset is taken as training samples for the ann to be used in online mode. Aqaba Qatranah South-Amman (AQSA) EPS is considered as a real case study and simulated in MATLAB environment to validate the proposed algorithm. The simulation results are compared with other ann algorithms available in the literature. Finally, a laboratory prototype is built for AQSA EPS including its long transmission line to test the proposed hybrid PSO-ann algorithm for real unbalance conditions acquired from the laboratory prototype.
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