Foundations of Design of Experiments (DOE)
Systematic methodology to plan, conduct, analyze, and interpret controlled tests, isolating factor effects, error variance, and causal interactions.
Standard Experimental Designs & ANOVA
Comprehensive mathematical formulations, ANOVA partitioning, and step-by-step solved problems for CRD, RBD, LSD, and 2^k Full Factorial designs.
Fractional Factorials & Taguchi Robust Design
Principles of fractional factorial screening, Taguchi Quality Loss Function, Orthogonal Arrays, and Signal-to-Noise (S/N) ratio optimization.
RSM Fundamentals & First-Order Modeling
The canonical 10-step RSM methodology, step-by-step first-order regression calculations, model verification, and the Method of Steepest Ascent.
RSM Designs: Central Composite & Box-Behnken
In-depth mathematical structure, point derivations, rotatability alpha, run count formulas, and comparison between Central Composite Design (CCD) and Box-Behnken Design (BBD).
RSM: Multi-Response Optimization (MRO)
Principles of simultaneous multi-response optimization, Derringer-Suich desirability functions, linear and geometric formulations, and solved industrial trade-offs.
Global Optimization & Metaheuristics Taxonomy
The 5 formal components of optimization, local vs. global optima, heuristic vs. metaheuristic foundations, exploration-exploitation mechanics, and the master 5-branch taxonomy.
Genetic Algorithms (GA)
Principles of Darwinian natural selection, chromosome encoding, genetic operators (selection, crossover, mutation), elitism, and step-by-step solved binary optimization.
Physics & Swarm Algorithms: SA & PSO
Physical metallurgy principles of Simulated Annealing (SA), Metropolis acceptance probability, flocking dynamics of Particle Swarm Optimization (PSO), and velocity/position updates.