Santanu Sardar (Awarded on July 19, 2019)


Designation: Research Scholar
Degree: Ph. D.
Supervisor: Debdulal Das
Co-Supervisor: Santanu Kumar Karmakar

Research Area

Topic: Tribological Characterization of Stir-Cast AA7075/Al2O3 Composites under Two-Body Abrasion: Experiment and Modelling

Tribological Characterization of Stir-Cast AA7075/Al2O3 Composites under Two-Body Abrasion: Experiment and Modelling

Aluminum alloys find wide applications mainly in aerospace and automotive industries owing to their low densities, good mechanical properties and excellent corrosion resistance. Use of Al-alloys in tribological fields is, however, limited due to their relatively poor wear property. Therefore, numerous attempts have been made over the years to improve tribological performance of Al-alloys by reinforcing them with hard ceramics like Al2O3, SiC, TiC, B4C, TiN and TiB2. This study primarily focuses on the understanding of tribological characteristics of ceramic particles reinforced Al-matrix composites (AMCs) under high-stress abrasion which is one of the most frequently encountered wear processes in practice.  

Al-Zn-Mg-Cu (AA 7075) matrix composites reinforced with (0-20 wt.%) 45 micron sized Al2O3 particles have been manufactured by enhanced stir casting route with bottom pouring facility employing appropriately designed three-stage graphite stirrer and considering well identified processing parameters like bath temperature, time and speed of stirring, pouring temperature, preheating temperature of reinforcement and mould on the basis of trial experiments. Microstructural characterizations of cast composites by optical, FE SEM, EDS and XRD confirm homogeneous distribution of Al2O3 particles in Al?alloy matrix with MgZn2 plus Al2CuMg intermetallics along the interdendritic regions. With increasing particle content, hardness of composite enhances significantly in spite of slight rise in porosity level.

 Using cylindrical pin specimens against SiC emery papers with the help of an advanced pin-on-disc tribometer and optical profilometer, two-body abrasive wear characteristics, namely: wear rate, coefficient of friction (COF) and roughness (Ra) of abraded surfaces, of the developed materials have been studied via an elaborate experimental plan involving input variables of normal load, abrasive size in the counter body, sliding velocity distance and sliding distance. The influence of an individual variable over a wide range has been experimentally established keeping the other parameters unchanged. Operative wear mechanisms have been identified through detailed examinations of generated wear debris and abraded surfaces using FE SEM and EDS. It has been established that irrespective of abrasion conditions, composites exhibit significantly lower wear rate and reduced COF with reference to base alloy owing to the load bearing ability and better wear resistance capability of Al2O3 reinforcements.

Roughness of worn surfaces of composites is, however, found to be higher over base alloy due to the development of protruded Al2O3 particles on contact surfaces since the surrounding softer matrix is removed with progressive loss of material. Mechanisms of abrasion are identified to change from microcutting and microploughing in unreinforced base alloy to mainly delamination with limited extents of microploughing and microcutting in composites. In general, the degrees of these abrasion mechanisms are found to enhance considerably with increasing both abrasive size and load, and marginally with sliding velocity, but remain almost independent on sliding distance. With increasing reinforcement content, wear rates of composites diminish due to the considerable reduction in the extents of microcutting and microploughing as well as slight lowering of delamination.

In order to identify as well as to quantify the influencing control factors and their interactions on two-body abrasion of the developed materials, Design of Experiments (DOE) methodologies such as Taguchi and response surface methods as well as soft computing techniques like artificial neural networks and genetic algorithm are employed in this investigation; since, these tools provide structured approaches for statistical analyses and modelling of complex engineering system. Tribological characteristics of base alloy and 20 wt.% Al2O3 composite have been investigated separately employing standard Taguchi L27 orthogonal array, signal-to-noise ratio, analysis of variance technique (ANOVA) and regression methods considering four independent control factors each at three different levels. For all three tribo-responses of both materials, the most influential factor is identified SiC abrasive size followed by load and then, abrasive size-load interaction. Linear regression models with excellent predictability have been developed for all tribo-characteristics separately for base alloy and composite.

Subsequently, abrasive wear characteristics of composites and base alloy have further been studied employing central composite design technique based on full quadratic response surface methodology (RSM). The effects of four independent factors (reinforcement content, load, abrasive size and sliding distance) on wear rate, COF and Ra have been evaluated using RSM based ANOVA. The most significant factors are identified as reinforcement content followed by abrasive  size, load and their interactions on wear rate, COF and Ra. Combined optimization of wear rate, COF and Ra adapting multi-response optimization technique with desirability approach as well as regression models for individual responses have been developed, and their adequacies are validated by several confirmatory tests. The developed models provide further insight on abrasive wear performances of the selected materials. For the lowest magnitudes of both wear rate and COF, the optimum amount of reinforcement has been identified at around 15 wt.% Al2O3.

Keeping in mind about the non-linear relationships of different variables associated with material and abrasion condition on tribo-responses, artificial neural networks (ANNs) and genetic algorithm (GA) have been employed utilizing all experimentally generated results in order to realize the wear response as a whole. It has been shown that ANN is an effective tool to model and effectively predict the two-body abrasion of AMCs. The developed ANN models have been successfully used as objective functions for multi-objective optimization of conflicting properties using GA. Output variables of wear rate, COF and Ra are optimized towards achieving their lowest magnitudes against different input variables. The generated Pareto solutions provide further clues for designing better AMCs as well as indicate applicable tribo-conditions where the performance of the investigated AMCs will be far superior to unreinforced base alloy. Application of ANN and GA in tandem can successfully be used for the speedy optimization of complex process like tribology of AMCs.