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Experimental Design and Statistical Process Optimization

30 Chapters
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01
Foundations of experimental design
randomization, replication, blocking, and the role of statistical thinking in process optimization.
Foundationsexperimental
02
Defining the problem and response variables
translating engineering objectives into measurable outcomes, factors, and levels.
Definingresponse
03
Completely randomized designs
analysis of variance, model assumptions, and practical interpretation for single-factor experiments.
Completelyrandomized
04
Randomized complete block designs
blocking strategies, efficiency comparison, and handling nuisance variables in industrial settings.
Randomizedcomplete
05
Factorial designs at two levels
main effects, interactions, and the geometry of 2^k experiments.
Factorialinteractions
06
Fractional factorial designs
resolution, aliasing, minimum aberration, and screening strategies for many factors.
Fractionalfactorial
07
Plackett-Burman and definitive screening designs
efficient factor screening under resource constraints.
Plackett-Burmandefinitive
08
Response surface methodology I
first-order models, steepest ascent, and path of improvement.
Responsemethodology
09
Response surface methodology II
second-order models, central composite designs, and Box-Behnken designs.
Responsemethodology
10
Process optimization with desirability functions
multi-response optimization, trade-offs, and Pareto frontiers.
optimizationdesirability
11
Robust parameter design
Taguchi methods, orthogonal arrays, signal-to-noise ratios, and inner-outer array strategies.
parameterorthogonal
12
Mixture experiments
simplex-lattice, simplex-centroid, and constrained mixture designs for formulation problems.
experimentssimplex-lattice
13
Split-plot and hard-to-change factors
design construction, analysis, and practical implications for industrial experiments.
Split-plothard-to-change
14
Nested and hierarchical designs
variance components, random effects, and measurement system analysis.
hierarchicalvariance
15
Statistical process control fundamentals
control charts for variables and attributes, and process stability assessment.
Statisticalvariables
16
Process capability analysis
Cp, Cpk, Pp, Ppk, and non-normal data considerations for specification compliance.
capabilityanalysis
17
Measurement systems analysis
gauge R&R, bias, linearity, and the impact of measurement error on DOE.
Measurementanalysis
18
Regression modeling for process optimization
variable selection, model diagnostics, and validation strategies.
Regressionmodeling
19
Generalized linear models in DOE
logistic and Poisson regression for non-normal responses.
GeneralizedDOE
20
Bayesian approaches to experimental design
prior elicitation, sequential design, and decision-theoretic optimization.
Bayesianapproaches
21
Optimal design theory
D-, A-, G-, and I-optimality, and algorithmic construction for constrained regions.
I-optimalityalgorithmic
22
Computer experiments and surrogate modeling
space-filling designs, Gaussian processes, and Kriging metamodels.
Computerexperiments
23
Sequential and adaptive designs
response-adaptive randomization, group sequential methods, and real-time optimization.
Sequentialadaptive
24
Design and analysis of computer experiments for simulation-based optimization
calibration and validation.
analysiscomputer
25
Case study
optimizing a chemical reaction yield using fractional factorial and response surface methods.
optimizingchemical
26
Case study
robust design of a injection molding process for reduced variability and improved quality.
injectionvariability
27
Case study
mixture design for pharmaceutical formulation development and stability optimization.
pharmaceuticalformulation
28
Case study
statistical process control and capability improvement in semiconductor manufacturing.
statisticalcapability
29
Case study
multi-response optimization in food processing using desirability and Pareto methods.
multi-responseoptimization
30
Integration and emerging trends
AI-assisted DOE, automated experimentation, and real-time statistical process optimization.
Integrationemerging