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Fractional Factorial Project 2 Report Guide

Report Guide Purpose

This report guide is intended to provide additional information to enable students to write better project reports. Before reading this guide, please print the Project 2 Report Assignment and Project 2 Rubric and read these documents carefully before reading the report guide. It will also be very helpful to have your DOE Project 1 report and any instructor comments available as you prepare your second project report.

Technical Report Purpose

The second DOE assignment is intended to improve the current operating status of an injection-molding machine operating in the Mouse Factory using a fractional factorial experimental design. To improve the operating status of the injection-molding machine you will design and analysis a fractional factorial report. Your analysis should determine which factors are important and which factors are not important. Additionally, you will suggest a new set point to operate the injection-molding machine. You are to provide evaluate the effectiveness of the new set point by establishing a new benchmark and comparing the benchmark from report 1 to the benchmark established in project 2.

Technical Report

You should follow all of the rules mentioned in the Design of Experiments Project 1 Report guide. Once again, your report should follow the sequence of topics provided in the rubric when creating your second DOE report. Please write your report from the point of view of a quality improvement engineer at the Mouse Factory and not a student completing a class project. Your goal is to clearly communicate the results of your project and demonstrate significant improvement.

Experimental Design

This section of your report should provide all the information necessary to understand and reproduce your experimental design. Important information describing your experimental design includes:

  • Number of runs,

  • Number of replicates,

  • Number of center points,

  • Number of blocks used, and

  • Resolution

Modeling Process

Your report should describe the process of determining the correct or final model. Initially, you will not be able to use hypothesis testing to determine which factors are significant and non-significant. For each model (or iteration) that you evaluate, you should include the following information:

  • List of factors in the starting model,

  • Results from your analysis,

  • Recommendation for next model (if needed), and

  • Justification of recommendation.

Do not forget to perform residual analysis as you are evaluating models.

Proposed Model

The final result from the modeling process is selection of a final or proposed model. Provide a detailed explanation of the model and the relationship between the selected factors and the density.

Aliasing

Aliasing is a common complication of analyzing and interpreting fractional factorial designs. If your proposed model contains aliasing, clearly explain the aliasing structure and its impact on interpreting your proposed model. If your proposed model contains no aliasing, clearly state that no aliasing is present in your model.

Interactions

Interactions can important information regarding the relationship between factors and the response variable. If your proposed model contains interactions, clearly state that fact in this section. You should interpret the impact of interactions contained in your model. When interpreting the interaction terms, it is important to express this relationship by describing the interaction effect term(s) in the model as well as by including interaction plot(s).

New Settings

To improve the operation of the injection-molding machine, a new set point must be found. Your new setting cannot exceed the range of the factors used in your experimental design. You should clearly identify which factors must be set and their new values and which factors can be ignored.

Comparison of New and Initial Settings

In this section, you will prove that your new setting (derived from a design of experiments analysis) is superior to the initial settings. The first step is to gather data and prepare a benchmark of the performance of the injection-molding machine at the improved setting. The establishment of a benchmark will require that a sample size be selected and sample observations collected at the new setting.

After the new setting data is available. You will provide three comparisons of the new and initial setting results. The first comparison is a table containing summary statistics for the new and initial settings. You should interpret the contents of this table. The second comparison is a graphical analysis. You are to prepare a graph that contains both the new and initial settings. Your graph should show the location, variability (or spread) and shape of the two sets of data. Please interpret your graph in this section. The final comparison is to perform a hypothesis test for equal means between the new and initial settings. Clearly state the significance level (or p-value) used for this test and validate all assumptions used in performing a hypothesis test.

Future Recommendations

The final section of your report should contain any recommendations to improve the operation of the injection-molding machine. Examples of material that could be included in future recommendations include:

  • Is a fold-over experiment required to discover additional information?

  • Was the optimal setting for the injection-molding machine (or just a better setting) found?