JNTU
JNTU B.Tech III DESIGN AND ANALYSIS OF EXPERIMENTS Previous Question Paper (March-2024 - R18)
Note: i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. iii) In Part B, Answer any one question from each unit.
PART - A
What are the key elements of a successful experimentation strategy?
Answer
- Clear Objective Definition: A successful strategy begins with a precise statement of the problem and the specific goals of the experiment. This involves identifying the response variables (dependent variables) that need to be measured and the factors (independent variables) that are expected to influence the outcome.
- Factor Selection and Level Setting: Identifying the critical process parameters and determining the appropriate range or levels for each factor is essential. This ensures that the experiment covers the relevant operating space without being overly complex.
- Experimental Design Selection: Choosing an appropriate design (e.g., Factorial, Fractional Factorial, or Response Surface Methodology) is crucial to balance the amount of information gathered against the resources available. The design must ensure that the effects of interest can be estimated with sufficient precision.
- Randomization and Replication: Incorporating randomization helps to eliminate the effects of lurking variables and bias, while replication allows for the estimation of experimental error, which is necessary for statistical inference.
- Data Analysis and Interpretation: A robust strategy includes a plan for statistical analysis, such as Analysis of Variance (ANOVA) or regression modeling, to validate the significance of the results and draw actionable conclusions.
- Iterative Refinement: Experimentation is rarely a one-time process. Successful strategies often follow an iterative cycle where preliminary results inform subsequent, more refined experiments to optimize the process or product.
Explain how factors, levels, and interactions are defined and manipulated in experimental studies.
Answer
- Factors are the independent variables or input parameters that are deliberately varied by the experimenter to observe their effect on the response variable. They represent the controlled inputs in a study, such as temperature, pressure, or concentration in a chemical process.
- Levels are the specific values or settings assigned to each factor during the experiment. For example, if 'Temperature' is a factor, the levels might be 100°C, 150°C, and 200°C. Manipulating levels allows researchers to map the relationship between the input and the output across a defined range.
- Interactions occur when the effect of one factor on the response variable depends on the level of another factor. An interaction is present if the change in the response produced by changing the level of factor A is not the same at all levels of factor B.
- Manipulation Strategy: In experimental design, factors and levels are manipulated through systematic variation. By using structured designs like Factorial Designs, researchers can simultaneously vary multiple factors at different levels. This approach ensures that the main effects of individual factors and the interaction effects between them can be estimated independently and with statistical significance, minimizing the influence of extraneous variables.
Define the Latin Square Design.
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What is the rationale behind using blocks in the RCBD?
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Define confounding in factorial experiments.
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Discuss the basic principles behind 2k factorial experiments.
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Explain the concept of confounded asymmetrical factorials.
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What distinguishes asymmetrical factorial designs from symmetrical ones?
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What are the main components of Response Surface Methodology?
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Describe the process of analyzing response surface data.
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PART - B
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Explain how researchers address issues such as confounding, efficiency, and balance when designing asymmetrical factorial experiments.
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How can you achieve optimum values with Response Surface Methodology? Explain with suitable example.
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