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Influence of mechanical alloying process on structural, mechanical and tribological behaviours of CNT reinforced aluminium composites – a statistical analysis

Manikandan P., Elayaperumal A., Franklin Issac R.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2021

Vol. 69, nr 2

art. no. e136745

The mechanical and tribological properties of the Al/CNT composites could be controlled and improved by the method of its fabrication process. This research article deals with the optimization of mechanical and tribological properties of Al/CNT composites, which are fabricated using the mechanical alloying process with the different weight percentage of multi-walled CNT reinforcement. The phase change and the presence of CNT are identified using the X-Ray Diffraction (XRD) analysis. The influence of mechanical alloying process and the multi-walled CNT reinforcement on the mechanical, and tribological behaviours of the Al/CNT composites are studied. The optimal mechanical alloying process parameters and the weight percentage of multi-walled CNT reinforcement for the Al/CNT composite are identified using the Response Surface Methodology (RSM), which exhibits the better hardness, compressive strength, wear rate and Coefficient of Friction (CoF). The Al/CNT composite with 1.1 wt.% of CNT has achieved the optimal responses at the milling speed 301 rpm and milling time 492 minutes with the ball to powder weight ratio 9.7:1, which is 98% equal to the experimental result. This research also reveals that the adhesive wear is the dominant wear mechanism for the Al/CNT composite against EN31 stainless steel but the optimal Al/CNT composite with 1.1 wt.% of multi-walled CNT has experienced a mild abrasive wear.

Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

Manikandan P., Geetha M.

Archives of Control Sciences

2014

Vol. 24, no. 3

271--287

The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.