Suprayitno, Ph.D.
Biography
Suprayitno, Ph.D graduated from Mechanical Engineering Universitas Brawijaya, Indonesia in 1996 for his bachelor degree and in 2005 for his master degree. He pursued his PhD degree from National Kaohsiung University of Science and Technology, Taiwan in 2018. He now serves `as senior lecture in the Industrial and Mechanical Engineering Department, Universitas Negeri Malang (UM), Indonesia.
Before his career in higher education, he worked as design engineer at PT IPTN (an Indonesia aircraft industry), Bandung Indonesia and also worked as material engineer at Maleo Project (an Indonesia national car project) from 1996 to 1999. He then joined the Mechanical Engineering Department, Universitas Negeri Malang, Indonesia in 1999 as a lecture. His research interests include computational intelligence, evolutionary algorithm, design optimization for expensive problems, robust and reliability-based design optimization, and also multiobjective design exploration.
Research History
-
- Evolutionary algorithm using progressive Kriging model and dynamic reliable region for expensive optimization problems
- Evolutionary Reliable Regional Kriging Surrogate and Soft Outer Array for Robust Engineering Optimization
- Robust design optimisation via surrogate network model and soft outer array design
- Optimum design of microridge deep drawing punch using regional kriging assisted fuzzy multiobjective evolutionary algorithm
- Evolutionary reliable regional Kriging surrogate for expensive optimization
- Optical design optimization of high contrast light guide plate for front light unit
- Reliability-based Robust Design Optimization for Correlated Variables Using Sequential Surrogate assisted Evolutionary Algorithm
- Airfoil aerodynamics optimization under uncertain operating conditions
- Design of Metallic Catalytic Converter using Pareto Optimization to Improve Engine Performance and Exhaust Emissions
- Multiobjective Optimization of Three-Pass Perforated Muffler Design for Improved Acoustic Performance and Reduced Fluid Pressure Drop Using Genetic Algorithms
