About Event

ABOUT THE FDP
The statistical principles underlying design of experiments were largely developed by R. A. Fisher during the 1920s and 1930s. Over the past 15 years, there has been a tremendous increase in the application of experimental design techniques in almost all types of industries. The work of G.Taguchi on robust design for variation reduction had revolutionary impact on Japanese industry. Today industries all over the world use design of experiments for problem solving and robust product development. The academic community use design of experiments extensively to prove or validate their research findings. Design of Experiments is the most powerful statistical tool that will provide the most significant information possible with the least amount of work. DOE has gained common acceptance among the researchers of various disciplines. Its cost effectiveness, greater speed and its ability to reveal design limitations not apparent with the traditional experimental methods make DOE approach more vital in research field. The objective of this course is to provide faculty members and students a theoretical and practical knowledge on the Design of Experiments and the skill required to analyze engineering problems with a commercially available software package. This course is designed in such a way that, in addition to the fundamental topics, advance topics have been included in line with the current research scenario which will be covered through expert lectures and hands on training. Also this FDP is devoted to addressing the need to enhance the knowledge about the Optimization Techniques in Engineering Applications is being conducted at SRM Institute of Science and Technology, Chennai.
Focus Area (Key Take Away) of the FDP
• Basics of Design of Experiments (DOE’s)
• TAGUCHI Analysis- Theoretical calculation and software interpretation using Mechanical Case Studies
• ANOVA and Regression Analysis using Machine Learning Techniques
• Response Surface Methodology (RSM), 2k Factorial Design
• Multi-Response Optimization- Water Cycle Algorithm, TOPSIS, Grey Relational Analysis (GRA)
• Fuzzy Logic Analysis- Expert system for Optimization, Fuzzy-Grey analysis
• Neural Network analysis and its application-Machine learning
• Genetic Algorithm, Particle Swarm and Simulated Annealing Optimization
• Teaching Learning Based Optimization Algorithm
• Hands-on Training using MINITAB and MATLAB Software