Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Analysis of defects in clean fabrication process of friction stir welding

Striving for cleaner production is a sought-after manufacturing philosophy.Friction stir welding(FSW)is a joiningtechnique with par excellence and far less invasive to the environment than even best conventional welding processes.It is energyefficient and free from consumables,affluent and radiations.It is,thus,accepted as a clean welding process that can produceacceptable quality joints.It suffers from some major challenges of defects of its own kind that subject the process open toimprovements so as to prove itself a reliable production process.This study presents a holistic characterization of defects commonlyfound in FSW joints.The finding of the present study reveals that most defects are caused by inadequate heat generation,impropermaterial movement around the pin and inadequate material consolidation behind the pin.The amount of heat generation andmaterial stirring depends on several FSW parameters which may lead to the defect formation,if not selected properly.The resultsreported in this work are derived from sound literature support and experimentation.Prescriptions are made in the form ofcharacteristics of defects such as likelihood of their location,main responsible parameters along with the recommendations forminimizing them.