ABSTRAK Muhammad Nurrafi Ihsan
PUBLIC Irwan Sofiyan COVER - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan BAB I - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan BAB II - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan BAB III - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan BAB IV - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan BAB V - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan BAB VI - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan Bibliography - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan LAMPIRAN - Muhammad Nurrafi Ihsan.pdf
PUBLIC Irwan Sofiyan
This researchaimstodesignandoptimiseanautonomoustram(AT)ve-
hicle's crashenergymanagement(CEM)throughnumericalsimulationsand
machinelearningprocessestoimproveitscrashworthiness.Inthiscase,the
structure's performanceisevaluatedthroughspecicenergyabsorption(SEA)
and eectivedeceleration.TheCEMsystemisequippedwithcrashboxesand
a deformableupperbumper,whicharetheprimarystructuralloadpathsin
absorbing energyduringacollision.
The ATchassismodelisbasedona12-meterlow-
oorplatformequipped
with anadditionalCEMsystem.Thestructureismodelledintovariouscong-
urations accordingtothedata-samplingprocess.Then,numericalsimulations
with ascenariobasedonexistingcrashworthinessregulationsareperformedon
all modelsusingFEM-basedsoftware.Thesimulationresultsarecollectedand
used fortheneuralnetworktraining.Subsequently,theselectedneuralnet-
workmodelsareemployedfortheNSGA-IIalgorithm,whichproducesaPareto
frontcontainingpossibleoptimumsolutions.Lastly,theTOPSISalgorithm
providesthedecision-makingprocesstoestimatetheoptimumconguration.
The resultsshowthattheestimatedoptimumATstructureismadeof
AA6061-T6 alloy,equippedwithanupperbumperwithathickness(tub) of1.1
mm andcircularcrashboxeswithseveralparameters:cross-sectionsize(C) of
100 mm,thickness(tc) of3.3mm,anddouble
angecutat3H. Furthermore,
this congurationimprovesupto193.109%fortheSEAand23.618%forthe
eectivedecelerationofthebaselinemodel.