Investigation of the Wear Behavior of Surface Welding AZ91 and AZ91+Gd Alloys under Variable Loading Conditions

Abstract

Adding rare earth elements to magnesium alloys is an effective way to improve their wear resistance. However, the effect achieved is closely related to the friction condition. In this paper, two different types of welding wires, AZ91 magnesium alloy and AZ91 + gadolinium (Gd), were used for surface welding. Dry sliding friction and wear experiments were performed on the surfacing alloys using the pin-on-disc test. The effects of Gd addition on the wear resistance and wear mechanism of the alloy were systematically studied under low to high loads. The results show that as the load increases, the friction coefficient of the surfacing AZ91 alloy gradually decreases as the wear rate increases. A mild–severe wear transition occurred at 100 N. The addition of Gd only slightly increased the wear rate under a load of 15 N. The wear rate was significantly decreased with loads in the range of 30 to 100 N and mild–severe wear transition was avoided. The influence of both Gd addition and load on the wear mechanism were considered. The overall wear resistance of the surfacing magnesium alloy was determined.

An Insight of AZ91D/WS2 Surface Composite developed by FSP

“…The change from moderate to severe wear occurs when the contact surface temperature is over a critical temperature, which is roughly equal to around 0.4 of the melting point of the aluminium alloy [23] .The effects of Gd addition on the alloy's wear resistance and wear mechanism were systematically investigated under low to high loads. Using the data, the friction coe cient of the surface AZ91 alloy decreases as the load increases, while the wear rate increases, according to the results [24]. From the literature survey the ceramic particles are added in the matrix material hardness increased and wear rate was decreased due to the impact of particle reinforcement.…”

High-temperature tribological performance of stir-cast and heat-treated EV31A magnesium alloy: Experiments and predictions

“…The machine learning (ML) methodology was adapted to predict the coefficient of friction (CoF) and wear rate. The dataset for the study was collected from various literature by considering operating temperature (⁰C), hardness of the workpiece and counterpart (HV), normal load (N), the weight percentage of Mg and REE (%) as input parameters and CoF and wear (mm 3 /Nm) as response variables [ [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] ]. For the purpose of comparison, the experimental wear rate in mg is converted into mm 3 /Nm.…”