Abstract:Friction stir welding is a solid-state environment-friendly joining process with many advantages over all fusion welding processes. Many variants in friction stir welding are used for uplifting the process to its maximum possible extent. One such variant is particle reinforcement in the weldment, which is used for improving the mechanical properties by enhancing the metallurgical aspects in the weld region. Generally, micro- and nano-sized ceramic particles are used for reinforcing, which have a much higher me… Show more
“…The contact area between the tool and workpiece governs the heat input, which is affected by tilt angle and plunger depth (Asadi et al , 2010). Some recent studies show that multiple passes led to homogeneous dispersion and grain refinement (Shivakumar and Rajamurugan, 2022; Gangil et al , 2018). Mazaheri et al revealed that the mean grain size was reduced from 10.2 to 2 µm of AZ31/ZrO 2 after four FSP passes (Mazaheri et al , 2020).…”
Purpose
This paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.
Design/methodology/approach
An empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.
Findings
The relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.
Practical implications
AZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.
Originality/value
In terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.
“…The contact area between the tool and workpiece governs the heat input, which is affected by tilt angle and plunger depth (Asadi et al , 2010). Some recent studies show that multiple passes led to homogeneous dispersion and grain refinement (Shivakumar and Rajamurugan, 2022; Gangil et al , 2018). Mazaheri et al revealed that the mean grain size was reduced from 10.2 to 2 µm of AZ31/ZrO 2 after four FSP passes (Mazaheri et al , 2020).…”
Purpose
This paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.
Design/methodology/approach
An empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.
Findings
The relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.
Practical implications
AZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.
Originality/value
In terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.
Aluminum (Al) alloys are reinforced with carbides and oxides to enhance their properties. Al composites are developed to meet current automotive, shipbuilding, and aviation requirements. In the current study, aluminum 6061 is reinforced with B4C and Cr2O3 separately to fabricate Al6061 + B4C and Al 6061+Cr2O3 aluminum metal matrix composites (Al MMC). The Al composites were fabricated by stir casting with a wt % in steps of 2%, 4%, and 6%. Joining of Al MMC is essential to develop valuable components. The developed composites were welded using friction stir welding (FSW). FSW is recognized and widely used for joining Al MMC due to premium weld quality with minimum defects. The present study aims to analyze the effect of process parameters and predictive accuracy of the artificial neural network (ANN) and response surface methodology (RSM). The parameters selected for the study are tool rotational speed, tool travel speed, and reinforcement wt %. The FSW was performed based on the experimental design developed by the design expert software. Through RSM analysis, it was found that both the independent factors (tool rotational and tool transverse speed) and the interaction of factors jointly contribute to the FSW joint properties. The higher ultimate strength of 139 MPa and lower tensile strength of 48 MPa are found. As the tool travel speed increase from 20 to 25 mm\min, ultimate tensile strength increase about 59%. The average accuracy of RSM was 98.26 and of ANN was 94.86.
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