Articles » Experiments in Engineering

Experiments in Engineering Date: October 12. 10:32:12, 2022
Source : doksi.net/en

In modern industries, most of the organizations use some kind of methodologies to improve product, processes, or just the daily work. By changing several input factors, engineers can come up with better products and sometimes they can reduce production times and costs dramatically.

Design of experiment (DOE) or experimental design (also called as DOX) is one strategy that quality professionals, engineers and designers employ in numerous sectors. The Engineering experiments’ approach is an operable alternative to other experimentation methods like one factor at a time (OFAT) and expert trial-and-error. DOE tends to be more rigorous than these two options. But before deep diving into engineering experiments, you should be familiar with its methodology and background.

Conducting surveys or experiments can provide the proper data for statistical studies, while experimental design is a special field in statistics that operate with the analysis of experiments and design. The method of the first one is used in a lot of special situations, especially in the fields of industrial production, agriculture, marketing research, medicine and biology.

Variables of interest are always identified in experimental studies. Factors of the study are one or more variables controlled so that data may be obtained about how the factors mentioned above affect the response variable. In other words, an experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables (also called “predictor variables” and “input variables”).

To get a deeper understanding of experiment design, let’s see the next video:



7 Steps to Conduct a DOE in Engineering

1. Set objectives: Clearly defined goals of the experiment are important to get the intended result. It’s probably the best to have your team do a brainstorming.

2. Select process variables: it should contain inputs and outputs, factors and responses.

3. Select an experimental design: there are some methods you should consider like: screening objective, comparative objective, method objective, optimizing responses (when factors are proportions of a mixture of objectives), optimal fitting of a regression model objective.

4. Execute the design: there is no need to unfold. The implementation is based on your approach you set off in the first three points above.

5. Check that the data are aligned with the experimental assumptions: we make assumptions when building models, and we also require certain conditions to be met for purposes of estimation. This section looks at some of the engineering and mathematical assumptions we typically make.

6. Analyze your results: After the necessary runs of your experiment, analyze the data obtained as a result of the experiment. Histograms, flowcharts, scatter diagrams can give a great insight on the effects of various factors on different responses. You can find correlations between input and output, the interactive impacts of the many factors in this way. The results and findings of DOE allow you to make the necessary tweaks and adjustments in a system to streamline the processes.

7. Use the results: it may lead to further runs, or just simply make the final conclusion.


Source: MoreStream
Source: MoreStream


Why/Which to Use?
- To determine whether a collection of factors or just a singular factor, has an effect on the response. This also means the understanding of the combined effect of the factors.
- To determine whether factors interact in their effect on the response.
- To model the behavior of the response variable as a function of the factors.
- To optimize the response.

Completely randomized experimental design is one of the three widely used experimental design methods. In this case the treatments are accidentally assigned to the experimental units. You can hear also about the randomized block design method, and the factorial design method, so let’s dig deeper!

To get familiar with Randomized block design, the next video is a great summary at a low level:



The method of factorial design enables researchers to study the joint effect of at least two factors on a dependent variable. Factorial experiments come in two flavors: full factorials and fractional factorials.

Center points method goes with experimental runs where variable X is set halfway between the low and high settings. Let’s have an example: suppose your DOE including variable X: the center point would be set midway at a temperature of 100 °C and a time of 15 seconds.

Central composite design is mostly used in response surface methodology for building a quadratic model for the response variable without needing a complete three-level factorial experiment.

How to Get Off to a Flying Start
There are several ways to start learning. The easiest way to start with an online design of experiments course and google for supplemental materials. Without a guide like this you will end up with low quality materials and a bumpy road to knowledge. You can also try books like “Practical Design of Experiments: DoE Made Easy!” to be a nice addition!



The best software to get familiar with is Minitab where you can manage all the 7 steps we mentioned above.

DOE in Software Engineering
DOE is often implemented in software testing. It’s not a common methodology to use, but it gives you the opportunity to excel in Enterprise IT, mostly by detecting and analyzing data driven errors.

The objective is to identify the factors that influence the degree to which the externalization of knowledge is generated in a software project based on knowledge, for which a factorial design was applied with two factors.

DOE in Architecture
In modern architecture, design of experiment became known as experimental architecture. It explores new paths and imagines new ways to meet the demand of humans in relation to the natural world. Designers and Visionary architects make the abstract into reality when they test their ideas in the real world, firstly on a small scale.

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