Parametric study on the dry sliding wear behaviour of AA6082–T6/TiB2 in situ composites using response surface methodology
Present work deals with the parametric study of dry sliding wear behaviour of TiB 2 -reinforced aluminium matrix composites (AMCs). Aluminium 6082-T6 alloy specimens reinforced with 0, 3, 6, 9 and 12 wt% of TiB 2 particles were fabricated by the in situ reaction of K 2 TiF 6 and KBF 4 in heated liqu...
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Veröffentlicht in: | Journal of the Brazilian Society of Mechanical Sciences and Engineering 2018-06, Vol.40 (6), p.1-12, Article 310 |
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Sprache: | eng |
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Zusammenfassung: | Present work deals with the parametric study of dry sliding wear behaviour of TiB
2
-reinforced aluminium matrix composites (AMCs). Aluminium 6082-T6 alloy specimens reinforced with 0, 3, 6, 9 and 12 wt% of TiB
2
particles were fabricated by the in situ reaction of K
2
TiF
6
and KBF
4
in heated liquid aluminium. Experiments were conducted to study the wear behaviour of AA6082–T6/TiB
2
composites using pin-on-disc apparatus at room temperature. Weight percentage of reinforcement, sliding speed, load and sliding distance were the process parameters studied in the present investigation, with five different levels of each parameter. The parametric optimization was done employing response surface methodology. The results confirmed that an increase in the amount of reinforcement and sliding speed decreased the wear loss, and an increase in load and sliding distance increased the wear in TiB
2
-reinforced AMCs. However, the relative significance of these parameters on the sliding wear resistance of the AMCs was very much different. Analysis of variance showed that the sliding distance was the most dominating factor with 65.28% to influence the wear loss in the fabricated composites; it was preceded by the sliding speed with 14.78%, load (9.39%) and reinforcement percentage (3.86%), respectively. The present model was validated by conducting confirmation tests. Thus in this work an accurate wear model has been developed, and it can be used as a predictive tool for wear applications. |
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ISSN: | 1678-5878 1806-3691 |
DOI: | 10.1007/s40430-018-1235-0 |