Metallurgical behavior and variation of vibro-acoustic signal during preheating assisted friction stir welding between AA6061-T6 and AA7075-T651 alloys

The present work investigated the effects of pin profiles(cylindrical and square),pin eccentricity(0.5 mm and 1 mm)in cylindrical tool and preheating(secondary heating)on metallurgical behavior,variation of vibro-acoustic signal pattern and joint strength during friction stir welding(FSW)between AA6061-T6 and AA7075-T651 alloys.The eccentric tool pins were observed to provide good flowability and intermixing between dissimilar metals,increased the size of stir zone,and the grains in stir zone were sufficiently finer with eccentric tool pin than concentric pin.The magnitude of vibro-acoustic signal increased when shoulder plunging started and drop in signal was noted when the tool shoulder reached its desired depth.The signal magnitude was noted to be higher in welding stage compared to tool plunging stage as the tool took in fresh material during tool movement along the weld path.Preheating the workpiece prior to pin plunging and during welding notably influenced the flow behavior and mixing pattern,and the grains in stir zone were slightly coarser than those in specimen without preheating.Significant reduction in the magnitude of the signal was also observed after preheating.Tensile and flexural strength of joints were also improved slightly when additional heating was employed.

FE SIMULATION AND EXPERIMENTAL VALIDATION OF POWDER MIXED EDM PROCESS FOR ESTIMATING THE TEMPERATURE DISTRIBUTION AND VOLUME REMOVED IN SINGLE CRATER

This study reports the results of a finite element simulation of powder mixed electric discharge machining process for H11 Hot Die steel material using relevant boundary conditions and reasonable assumptions.The crater shape was developed using simulated temperature profiles to estimate the volume removed in a single crater.The temperature distribution on the workpiece was used to predict the cooling rate and calculate the stresses generated due to thermal loading.Subsequently,the simulation results were experimentally validated by physically measuring the crater shape and volume.From the results it was concluded that about 25%of heat is transmitted to the workpiece during machining at the process conditions used in the experiment.The microscopic pictures showed bigger craters with increase in current.The machined surface showed overlapping craters with surface cracks suggesting a high cooling rate.

Finite element modeling and analysis of powder mixed electric discharge machining process for temperature distribution and volume removal considering multiple craters

Powder mixed electric discharge machining(PMEDM)is one of the modern developments in electric discharge machining(EDM)process.In the present work,finite element modeling has been carried out considering randomly oriented multiple sparks during PMEDM.Transient thermal analysis is done to obtain temperature distribution,volume removal,and proportion of volume removed by melting and evaporation at different current,pulse on time and fraction of heat that enters to work piece.Gradually growing spark behavior and Gaussian distribution of heat source is used to simulate multiple craters.Temperature distribution along radial direction shows peak temperature at center of spark and thereafter a gradual decrease with increase in radial distance.Along depth direction temperature sharply decreases that forms wider craters with shallow depth in PMEDM.Peak temperature and volume removal increases with current more rapidly.Volume removal by melting is much higher than evaporation at lower current settings and with higher current almost equal amount of material is removed by melting and evaporation thus reducing the re-solidification of melted material.Current plays a significant role behind the contribution of material removal by evaporation followed by fraction of heat.Increase in pulse on duration increases the total volume of material removal however does not significantly increase the proportion of volume removal by vaporization.

Bayesian optimization with adaptive surrogate models for automated experimental design

Bayesian optimization(BO)is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate.Currently,optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional strategies.This can have a significant impact on modern scientific discovery,in particular autonomous materials discovery,which can be viewed as an optimization problem aimed at looking for the maximum(or minimum)point for the desired materials properties.The performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective functions.In this paper,we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure,namely Bayesian multivariate adaptive regression splines and Bayesian additive regression trees.They can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective functions.Both simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.