Browsing by Author "Umashankar, K.S."

Now showing 1 - 6 of 6

Machining characteristics of multiwall-CNT reinforced Al/Al-Si composites using recurrence quantification analysis
(2011) Gangadharan, K.V.; Umashankar, K.S.; Ravish; Desai, V.
Aluminium (Al)/Aluminium alloy composites are emerging as very promising materials, especially in the fields of aerospace and automotive for their various attractive and technically demanding properties. Discontinuously reinforced aluminium metal matrix composites with reinforcements as nanoparticles of ceramics in general and carbon nanotubes in particular have emerged as the forerunner for a variety of general and special engineering and structural applications. In many of the fields where these materials find applications, machining is invariably required for getting correct geometries, dimensions and surface finish of the components. Hence, establishing the machining characteristics of these materials in terms of the deterministic nature of dynamic signals such as cutting force signals and vibration signals is very important and sought after. Machining process has been understood to be nonlinear and chaotic in nature. In this paper a relatively new technique called Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA), a tool to analyse nonlinear and chaotic systems, is used to study the machining characteristics of cast and powder metallurgy Al and Al-Si alloys (LM6 and LM25), CNT reinforced Al/Al-Si composites produced by powder metallurgy route. Cutting force signals were sensed, acquired and analysed using RQA technique. Determinism (DET), which is one of the variables of RQA, indicates the determinism present in a signal. The values of DET were used to compare the machining characteristics. For all the three materials the deterministic nature of the cutting force signal was highest when reinforced with 0.5 weight percentage CNT, followed by respective base alloys produced by powder metallurgy method and casting route. 2011 Jordan Journal of Mechanical and Industrial Engineering.
Machining characteristics of multiwall-CNT reinforced Al/Al-Si composites using recurrence quantification analysis
(2011) Gangadharan, K.V.; Umashankar, K.S.; Ravish; Desai, V.
Aluminium (Al)/Aluminium alloy composites are emerging as very promising materials, especially in the fields of aerospace and automotive for their various attractive and technically demanding properties. Discontinuously reinforced aluminium metal matrix composites with reinforcements as nanoparticles of ceramics in general and carbon nanotubes in particular have emerged as the forerunner for a variety of general and special engineering and structural applications. In many of the fields where these materials find applications, machining is invariably required for getting correct geometries, dimensions and surface finish of the components. Hence, establishing the machining characteristics of these materials in terms of the deterministic nature of dynamic signals such as cutting force signals and vibration signals is very important and sought after. Machining process has been understood to be nonlinear and chaotic in nature. In this paper a relatively new technique called Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA), a tool to analyse nonlinear and chaotic systems, is used to study the machining characteristics of cast and powder metallurgy Al and Al-Si alloys (LM6 and LM25), CNT reinforced Al/Al-Si composites produced by powder metallurgy route. Cutting force signals were sensed, acquired and analysed using RQA technique. Determinism (DET), which is one of the variables of RQA, indicates the determinism present in a signal. The values of DET were used to compare the machining characteristics. For all the three materials the deterministic nature of the cutting force signal was highest when reinforced with 0.5 weight percentage CNT, followed by respective base alloys produced by powder metallurgy method and casting route. © 2011 Jordan Journal of Mechanical and Industrial Engineering.
Machining characteristics of nanocomposites
(2011) Umashankar, K.S.; Gangadharan, K.V.; Desai, V.; Shivamurthy, B.
Nanocomposites were prepared with Al/Al-Si alloys (LM 6 and LM 25) as matrix and multi wall carbon nanotube (MWNT) of 0.25, 0.5, 0.75, 1.0 and 1.5 wt. % as reinforcement through powder metallurgy (PM) followed by sintering and hot extrusion techniques. Fabricated nanocomposites were machined on a Panther 1530/1650 lathe by using tungsten carbide tool. Recurrence quantification analysis (RQA) was used to study the machining characteristics by using cutting force signal stability. Highest value ofdeterminism (DET-one of the RQA parameter) was observed for 0.5 wt.% MWNT reinforced Al and Al-Si nanocomposites. This attributes better machining characteristic due uniformity of the signals. Also it is observed better mechanical properties at 0.5 wt.% reinforced nanocomposite and further reinforcement deteriorate the machinability and mechanical properties. 2011 VBRI press.
Machining characteristics of nanocomposites
(2011) Umashankar, K.S.; Gangadharan, K.V.; Desai, V.; Shivamurty, B.
Nanocomposites were prepared with Al/Al-Si alloys (LM 6 and LM 25) as matrix and multi wall carbon nanotube (MWNT) of 0.25, 0.5, 0.75, 1.0 and 1.5 wt. % as reinforcement through powder metallurgy (PM) followed by sintering and hot extrusion techniques. Fabricated nanocomposites were machined on a Panther 1530/1650 lathe by using tungsten carbide tool. Recurrence quantification analysis (RQA) was used to study the machining characteristics by using cutting force signal stability. Highest value ofdeterminism (DET-one of the RQA parameter) was observed for 0.5 wt.% MWNT reinforced Al and Al-Si nanocomposites. This attributes better machining characteristic due uniformity of the signals. Also it is observed better mechanical properties at 0.5 wt.% reinforced nanocomposite and further reinforcement deteriorate the machinability and mechanical properties. © 2011 VBRI press.
Recurrence quantification analysis to compare the machinability of steels
(2011) Ravish; Umashankar, K.S.; Alva, A.; Gangadharan, K.V.; Desai, V.
Machinability, though is a simple term, is difficult to generalize. But nevertheless, it can be realized as the ease or difficulty with which a material can be machined. Assessing the machinability of various materials before they are used in commercial manufacturing is very demanding, as the machinability affects the material removal rate, surface finish of the workpiece, cutting power consumption and tool wear rate. The present work aims at establishing Recurrence Quantification Analysis, a relatively new technique in the study of chaotic systems, as a potential tool to establish and compare the machinability of steels. The technique has its roots in quantifying the Recurrence Plots obtained by the phase space reconstruction of time domain signals. Variation in Determinism, one of the variables of the technique, is used as a mean to establish the comparison of machinability. 2006-2011 Asian Research Publishing Network (ARPN).
Recurrence quantification analysis to compare the machinability of steels
(2011) Ravish; Umashankar, K.S.; Alva, A.; Gangadharan, K.V.; Desai, V.
Machinability, though is a simple term, is difficult to generalize. But nevertheless, it can be realized as the ease or difficulty with which a material can be machined. Assessing the machinability of various materials before they are used in commercial manufacturing is very demanding, as the machinability affects the material removal rate, surface finish of the workpiece, cutting power consumption and tool wear rate. The present work aims at establishing Recurrence Quantification Analysis, a relatively new technique in the study of chaotic systems, as a potential tool to establish and compare the machinability of steels. The technique has its roots in quantifying the Recurrence Plots obtained by the phase space reconstruction of time domain signals. Variation in Determinism, one of the variables of the technique, is used as a mean to establish the comparison of machinability. © 2006-2011 Asian Research Publishing Network (ARPN).