R. Viswanathan

Bio: R. Viswanathan is an academic researcher from Techno India. The author has contributed to research in topics: Machining & Taguchi methods. The author has an hindex of 5, co-authored 10 publications receiving 169 citations.

Experimental investigation on surface roughness, tool wear and cutting force in turning of hybrid (Al7075 + SiC + Gr) metal matrix composites

Abstract: This paper presents a detailed study on optimization of turning parameters of Al7075/SiC/Gr metal matrix composites that are used for structural applications widely. The composite specimen containing a mixture of 90 wt.% of Al7075 and 10wt% of SiC and Gr is fabricated by stir casting process. The focus of the work is to identify the optimum weight fraction of reinforcement as well as optimizing the machining parameters. TOPSIS method of optimization is used to predict the optimum composition of reinforcement and the turning parameters like feed, speed and depth of cut. L27 orthogonal array (OA) design is used to carry out the turning experiments. As per TOPSIS analysis, minimum speed of 40 m/min, feed rate of 0.100 mm/rev, high depth of cut of 0.3 mm and material composition of 3%SiC + 7%Gr were found as optimal levels. SEM analysis is also carried out to study microstructural variations after machining. The confirmation test revealed the improvement in surface finish as 16.02% while the tool wear and cutting force are reduced by 22% and 32.30% respectively.

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.

Abstract: The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.