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Evaluation of Cu-Al-Be SMA Wear Behaviour using Taguchi Approach

EasyChair Preprint no. 5175

16 pagesDate: March 18, 2021


The preparation of Cu-Al-Be SMAs is the focus of this experimental work. The goal was to economically prepare them using Gravity die casting and an induction furnace. Martensitic phase was obtained by subjecting the casts to suitable thermal procedure. Shape memory effect and Hardness were evaluated for verification using Bend test and Vickers hardness equipment respectively. It was observed that with an increase in small addition of Be increases the hardness of alloy. After confirmation of SME, the wear test of Cu-Al-Be SMA’s was performed using Pin on disc equipment. The three discrete parameters namely “sliding speed”, “applied load” and “sliding distance” were analyzed using Taguchi technique. The experiment plan is generated by Taguchi’s technique and based on “L27 orthogonal array” the experiments were conducted. The optimal wear properties under the impact of three discrete parameters were found using ANOVA and regression equations developed. This showed that with rising sliding distance and load, wear loss rises, while with rising sliding speed, it reduces. Based on the “smaller the best” principle, dry sliding wear resistance was evaluated and validation was performed to validate the experimental findings. SEM is used to study the morphology of worn surface and its wear mechanisms. Microstructural studies showed that adhesive, abrasive, brinelling and surface fatigue wear mechanisms are major contributors to this SMA alloy wear.

Keyphrases: brinelling wear, martensitic transformation, shape memory alloys, Signal to Noise Ratio, Surface Fatigue, Taguchi approach, wear mechanisms

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {P Rajendra and Pradeep K V Kumar and K R Phaneesh and Madeva Nagaral},
  title = {Evaluation of Cu-Al-Be SMA Wear Behaviour using Taguchi Approach},
  howpublished = {EasyChair Preprint no. 5175},

  year = {EasyChair, 2021}}
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