ANSYS BASICS TRAINING

ANSYS BASICS TRAINING
Jakarta | 26 – 28 November 2012 | Rp, 4.500.0000,-
Jakarta | 17  – 19 Desember 2012 | Rp, 4.500.0000,-

 

Day-1

• Introduction to FEM and FEA procedures
• Starting ANSYS

1.  Opening, assigning analysis, saving, renaming, resuming and allocating memory for ANSYS session.
2. Import and Export Operations.

• Geometrical Modeling in ANSYS

1. Creating solid models in ANSYS using geometrical or hierarchical entities, namely keypoint, line,       area and volume.

• Introduction to Element Types in ANSYS

1. Assigning 1D model (truss, beam, pipe, etc), 2D model (plate, shell, etc), 3D model (2D solid, 3D solid, axis symmetric, etc).
2.  Assigning thermal element types (as the counterparts of the structural element types).

• Introduction to Material

Day-2

• Transformation Operators for Solid Modeling

1. copying, moving, reflecting, scaling

• Boolean or Logical Operators

1.  add, subtract, glue, overlap, intersection, partition, divide

• Other Operators

1. Checking and calculating geometry: keypoint or node distance, center of line, area, volume, etc

• Meshing

1. What is meshing ?
2. Meshing attributes
3. Meshing guidelines
4. Art of meshing – I

 

Day-3

• Art of meshing – II
• Solution steps and options
• General post processing
• Case(s)
  • Building model
    • Meshing
    • Solving or running solution

• Post processing
• Interpreting results
• Documenting results
  • Review and QA

 

WORKSHOP (3 Days)

ANSYS BASIC TRAINING

• Jakarta, 26 – 28 November 2012/ 17  – 19  Desember 2012
• ADHI GRAHA Lt.12 (Ged. Surveyor Indonesia),

Jln. Gatot Subroto, Kav-56, Jakarta

• Durasi Training 7 jam  x  3 hari
• Biaya Training Rp 4,500,000,-

(Sertificate, Install, Module, Konsultasi, Souvenir, Lunch & Coffe Break)

 

Instruktur :

Mas Irfan Purbawanto Hidayat, S.T., M.Sc.

Bachelor and Master Theses :

• J-Integral Analysis for Compact Tension Specimen (CTS) of Ti-6Al-4V using Finite Element Method.
• Fatigue Life Assessment of Polymer-Matrix Composites under Variable Amplitude Loading using Neural Networks.

 

 

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