Kirthivasan Aruljothi

Thema: Modellierung und simulative Untersuchung des Einflusses von Druckverlusten in Kältemittelkreisläufen - Modellierung eines 1D stationären kompressiblen Strömungsmodells (CFD) mit vollständiger Wärmeintegration zur Bestimmung des Druckverlustes (Simcenter FloMaster) - Integration des Druckverlustes in den Kältemittelkreislauf, um dessen Einfluss auf die Systemeffizienz zu bestimmen, d.h. Verdichterleistung, Kälteleistung (Dymola)

Thema: Simulation von Schweißverzug im Rahmen von Prozesskettensimulationen (FEM) - Simulation der Positionierung von Karosseriebauteilen (Frontklappe, B-Säule, Heckklappe) in einer Fügevorrichtung mit komplexer Kinematik (ESI-SYSWELD) - Ermittlung der Auswirkung des thermischen Fügens (Punktschweißen) auf die eingespannten Bauteile (ESI-SYSWELD) - Entwicklung eines MATLAB-Skripts zur Automatisierung der Extraktion von Schweißpunkten aus CAD0 (.iges) und Schreiben in ein ESI-kompatibles Format (.mwf)

H-Series and Neptune Engine Assembly & Testing - supervised the hot testing of compression ignition IC engines, ranging from rotary, inline, common rail system on basis of diesel injection pump, marine, defence, genset & commercial vehicle based on utility - diagnosed and distinguished engine noises due to valve hitting, valve timing wrong, tappet noise, gear rubbing noise, misfiring and valve leak.

Lean Six Sigma Advanced Problem Solving Project, Hosur Unit 2 (3 months) - improved the Straight Pass of 1616 IL/3C model in Line 2 MDV (Medium Duty Vehicle), Chassis Assembly deploying 7 step QC story. H-Series and Neptune Series Engine Cylinder Block Machining (On Job Training - 6 months) - reduced the setup change time of NAGAL cylinder bore honing machine (bottle-neck) from 60 mins to 47 mins for H-Series Engine Finish Machining.

CGPA: 1,7 out of 5 Multibody Dynamics Advanced Finite Element Methods Intelligent Monitoring of Engineering Systems -raw signals (accelerometer & gyroscopic) from materials processed during milling were transformed through offset, filters, FFT - useful features (90% size reduction) were extracted for machine learning (MATLAB), - various classification algorithms such as KNN, LSTMs, Dense NN were used to train the network to optimise the prediction accuracy, computational memory and tim

CGPA: 1,8 out of 5 Advanced Internal Combustion Engines Thermodynamics