Effect of wear parameter on Al-Zn alloy on wear rate by using Taguchi’s technique

Pruthvi, H. M.; Shenoy, H. Ghanashyam

doi:10.1016/j.matpr.2021.10.204

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…In the scope of the 7xxx group of Al alloys, Reis et al [16] investigated abrasive wear of two different Al-Zn-Mg alloys and proposed a correlation between wear, hardness, and UTS, where wear decreases with the increase in hardness and UTS. Focusing on different Zn and Mg content in Al-Zn-Mg alloys, Pruthvi and Shenoy [17] investigated wear properties after solution and retrogression re-aging heat treatment. Their study concludes that the wear rate decreases, i.e., the material is more wear-resistant with the increase in Zn content and re-aging duration.…”

Section: Introductionmentioning

confidence: 99%

Abrasion Wear Resistance of Precipitation-Hardened Al-Zn-Mg Alloy

Rodinger,

Lukšić,

Ćorić

et al. 2024

Materials

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The heat treatment of aluminum alloys is very important in industries where low weight in combination with high wear resistance, good strength, and hardness are important. However, depending on their chemical composition, aluminum alloys are subjected to different mechanical and thermal treatments to achieve the most favorable properties. In this study, an Al-Zn-Mg alloy was heat-treated including solution annealing at 490 °C for 1 h with subsequent artificial aging at 130, 160, and 190 °C for 1, 5, and 9 h. The hardness (HV1) and abrasive wear resistance with three different abrasive grain sizes were measured for all samples. The highest hardness was measured for the samples artificially aged at 130 °C/5 h, 227 HV1, while the lowest hardness was measured for the samples aged at 190 °C/9 h. The highest and the lowest wear resistance was also observed for the same state, i.e., artificially aged at 130 °C/5 h and 190 °C/9 h, respectively. The critical abrasive grain size was detected for some samples, where a decrease in wear rate was observed with an increase in the abrasive grain size from the medium value to the largest. The Response Surface Methodology (RSM) was applied to demonstrate the influence of the input parameters on the material wear rate.

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Section: Introductionmentioning

confidence: 99%

Abrasion Wear Resistance of Precipitation-Hardened Al-Zn-Mg Alloy

Rodinger,

Lukšić,

Ćorić

et al. 2024

Materials

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“…The results showed that corrosion resistance has been elevated, making them suitable for use in corrosive environments [10]. Among the different types of AMCs, ACZ MMCs reinforced with AOX ceramic particles have attracted significant interest for high-wear applications, such as in the aerospace and automotive industries [11]. A study investigated the wear behavior of ACZ MMCs reinforced with different weight percentages of AOX particles.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approach on Influence of Ceramics on Dry Sliding Wear in Al-Cu-Zr Metal Matrix Composite

Pandiaraj,

Vivek,

Thiyagarajan

et al. 2023

MSF

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This research aims to investigate the dry sliding wear behavior of Al-Cu-Zr (ACZ) metal matrix composite (MMC) at various Aluminium oxide (AOX) Nano particles compostion. ACZ alloy is widely used in on road and space mobility applications where the focus is on wear resistance. To enhance the resistance towards wear, Al alloy is reinforced with AOX nanoparticles at 3%, 6%, and 9% addition using stir casting process. The wear assessment is conducted at varying Composition (COMP), load (LD), speed (Ns), and sliding distance (SLDN). The wear rate (WRT) and Frictional force (FRFC) are analysed for different process parameters. To optimize the experiments, Taguchi signal-to-noise ratio (STNR) is used. Taguchi analysis show that the optimal conditions for minimum WRT and FRFC are at 6% AOX addition, 12.5 N load, 500 rpm speed, and 35 mm SLDN. Furthermore, an artificial neural network model (ANNM) is developed to forecast the WRT and FRFC. The neural network model is trained using the experimental data and the optimized process parameters. The neural network is a powerful tool that can learn the complex relationship between input and output variables. The model is validated using the experimental data, and the results show that the neural network model can predict the WRT and coefficient of friction with high accuracy. The Taguchi optimization and neural network model can provide a systematic approach to optimizing the process parameters and predicting the WRT and coefficient of friction. This approach can be applied to other materials and processes to improve their performance and reduce costs.

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