Devoptimize

 Design of Experiments (Unit 5) Design of Experiments - Unit 5 - DOE - SRMIST Syllabus Types One-way ANOVA - 1 factor & at least 2 independent levels. Define NULL & Alternate hypothesis. State Alpha. Calculate DOF. State decision rule. Calculate the test statistic. State result. State conclusion. Two-way ANOVA - At least 2 factors that have dependent or independent levels. Three-way ANOVA Diagrammatic understanding. Assumptions of ANOVA Normality of sampling distribution of means I ndependent of errors Absence of outliers. Homogeneity of variances. Hypothesis of ANOVA H0 is the initial condition. H1 is the secondary condition. NOTE *Sums have been omitted in this article, this can be used for reference.

 Design of Experiments (Unit 4) Design of Experiments - DOE - Unit 4 - SRMIST Syllabus RSM A sequential procedure is a collection of statistical techniques for the analysis of problems in which the response is influenced by other variables. The objective is to optimize the response. First-order model - A linear function of independent variables. Second order model - Curvature of a polynomial of higher degree. Contour Plot - A 2-D projection of the curvatures. Canonical form & optimization. Types Steepest Ascent - Upward of region of fitted. Steepest Descent - Downward of the region of fitted. Central Composite Designs Fractional factorial Plackett-Burman Box-Behnken Design Points CCC CCI CCF NOTE *Certain sums are possible from this unit which has not been mentioned in this article.

Design of Experiments (Unit 3) Design of Experiments - Unit 3 - DOE - SRMIST Syllabus Taguchi's Quality Loss Function Taguchi's loss function talks about the critical performance parameter value . The entire point being, we find the most nominal value of the parameter specification . Orthogonal array (OA) -   A unique table indicating how to do the experiments with the available variables and their settings. Larger the better -   Analysing to maximise the parameter being studied. Smaller the better - Analysing to minimize the parameter being studied. Nominal the better - Analysing to have nominal parameter being studied. Steps in Taguchi's Method NOTE *This unit has some numerical which are in the PPT. They are not covered in this since this article only covers important points for theory.

Design of Experiments (Unit 2) Design of Experiments - Unit 2 - DOE - SRMIST Syllabus Barriers Educational - Statistical & engineering understanding and implementation. Management - Managers opting for other options due to insufficient knowledge. Cultural - Main reason, wiz reluctant and fear of embracing DOE. Communication - Lack of knowledge and gaps between academia and industry. Other - Technical & non-technical issues. Methodology Planning Phase Problem Recognition & Formulation Selecting Response Factors Selecting process variables Classification of process variables Determination of levels of process variables List all required interactions Designing Phase - Selecting appropriate design for experiment. Conducting Phase - Carry out the experiment. Analysing Phase - Noting down the outcomes and finding any trends in the data. Tools Main Effects Plot - Mean response value at each level. Interaction Plots - Mean response value of 2 factors. Cube Plots - Average response

  Design of Experiments (Unit 1) Design of experiments - DOE - Unit 1 - SRMIST 2022 syllabus.  Experiments It is a series of conducted tests to analyse and understand behavioural patterns between certain factors and then to statistically predict outputs for future instances . An experiment involves taking multiple tests by disturbing the environment and noting down the reaction. The goal is to make a test yield better outcome (efficiency) by optimizing the variable factors in the experiment.  System Model When basic inputs are fed into a process or system model, we expect an output from the process which is dependent on external factor which are either controllable or uncontrollable. Objectives Determining the most influential control variables. Determining location of this variable for optimized output. The location of this variable must not lead to variable output. Effect of uncontrolled variable must be minimized.  Terms Response - Outcome of an experiment. Factors - Var