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Does anyone know of any? Before switching to Mac full time, when I was on the PC, I was using TopStyle for Windows. To be perfectly honest, I wish they’d make a Mac version of it one of these days, but hands down it’s the best XHTML/CSS editor on the PC simply because of it’s simplicity, live previews, and panel previews (seeing both IE. Design of experiments with full factorial design (left), response surface with second-degree polynomial (right) The design of experiments ( DOE, DOX, or experimental design ) is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation.
» How to Perform Design of Experiments. How to Perform a Design of Experiments. For simplicity, let's assume you are writing a cookbook and want to find the best directions for baking a cake (which is similar to baking paint on a car finish). You can save time by performing a design of experiments test. The (statistical) design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions. DOE begins with determining the objectives of an experiment and selecting the process factors for the study. Definition of Design of Experiments (DOE): A structured, organized method for determining the relationship between factors (Xs) affecting a process and the output of that process (Y). Other definitions: Conducting and analyzing controlled tests to evaluate the factors that control the value of a parameter or group of parameters.
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This course is part of our Lean Six Sigma Black Belt program, which consists of eight courses designed to prepare you for the International Association of Six Sigma Certification (IASSC) Black Belt exam. We recommend you take all eight courses in the program to be fully prepared for the exam.
What you will learn
In this Design of Experiments online course, you will learn the Design of Experiments or DOE. This design technique, which can be applied in several different methods, takes the results from a few carefully designed experiments and uses those results to create equations that explain how the product, process or system works.
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By the end of the course, you will know what the keys to a successful DOE analysis are, and you will be able to conduct a Full Factorial DOE and a Fractional Factorial DOE. If you are a member or leader of an analysis team using a methodology such as Lean Six Sigma, this technique will be a significant aid when your problem resolution requires a major change to your systems.
In addition to covering experimental design approaches and methodologies, you will learn to use popular statistical analysis application Minitab to conduct your DOE and apply your results.
The design of experiments technique is incredibly powerful when working with new products, new technologies, or when migrating an existing technology into a new application. It is also very helpful for identifying the critical few parameters that will drive the performance of the product, process, or system. When you are in a discovery mode of analysis, this technique provides a path to important insights.
Who this Design of Experiments online course is for
This course can be taken as part of the GoSkills Lean Six Sigma Black Belt training program, to prepare for certification with IASSC. It is also a good stand-alone course to improve proficiency and expand your skill set in any industry with responsibility for technology deployment or product and process development.
This course will be from the standpoint of helping you to make wise decisions about your product and process design and management, not conducting mathematical proofs or solving complex matrix mathematics.
Highlights:
- 24 practical tutorials with videos, reference guides, exercises and quizzes.
- Designed to prepare you in part for the IASSC Black Belt exam. To prepare in full, you should take all eight courses in our Lean Six Sigma Black Belt program.
- Identify when and why to do a DoE, and recognize the steps of the process.
- Understand the difference between the full factorial approach and fractional factorial approaches, and their pros and cons.
- Recognize when to use a Plackett-Burman and Taguchi DoE and how to design these types of study.
- Learn how to conduct DoE analysis in popular statistical analysis program, Minitab.
- Understand how design and problem solving teams can apply DOE results to make wise decisions.
- Master the key principles for success when conducting a DOE study.
- Gain critical skills for your role in technology deployment, product and process development, or Lean Six Sigma team.
- Earn 7 PDUs or contact hours toward your Project Management education for certification with PMI.
Once enrolled, our friendly support team and tutors are here to help with any course related inquiries.
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GoSkills has been reviewed and approved as a provider of project management training by the Project Management Institute(PMI)®.
GoSkills Ltd is an IASSC Accredited Training Organization™.
Experiments and Design
The design process typically relies on experiments to create and analyze data that is used when making design decisions. This data is invaluable to the design team as they strive to create a superior design. There are several approaches to the experimental process that design teams use.Trial and Error
The simplest experimental design approach is trial and error. If subject matters experts are generating the trial design, this can be successful. However, if the trial fails, this approach can lead to delays and overruns.One Factor At A Time
The OFAAT method is often considered the best scientific method for creating a plan of experiments. It is very controlled, and the design performance often grows in capability over time. But it is also the most timing consuming and expensive approach when conducting a set of experiments.Full Factorial Design of Experiments
A full factorial DOE conducts a set of experiments with carefully controlled configurations of the independent or control factors in the design. The results are statistically analyzed to create a design space equation that can be used to optimize the design. It is faster and cheaper than OFAAT, but longer and more costly than a lucky guess with Trial and Error.Fractional Factorial Design of Experiments
A fractional factorial DOE conducts only a fraction of the experiments done with the full factorial DOE. It then statistically analyzes the results to fine tune the design and normally does a second optimizing study. Even though there are typically several sets of experiments, the total is still less than the number conducted with a full factorial study and much less than OFAAT.Theory of Design of Experiments
This lesson provides a high level description of the DOE process that applies to any type of DOE. It also answers the questions of when to do a DOE and why to do a DOE.DOE Studies
This lesson explains the preparation needed to initiate a DOE study of any type. It includes a discussion on setting the DOE objective and has a checklist of questions that will need to be answered either before the study starts or early in the study design.Full Factorial DOE Methodology
This lesson describes the eight steps to be followed when conducting a full factorial DOE.Factor Selection
This lesson explains the different types of factors that are involved in the DOE study design including control factors and response factors. The characteristics that should be used when selecting each type of factor are discussed.Full Factorial DOE Study Design
This lesson explains how to design the study so that the statistical analysis can be performed. The preparation of the test sample configurations is explained. The use of design features of replication, center points and blocking are also addressed.Conducting the Study
This lesson addresses how to execute and control each of the experimental runs in the study. It also explains the importance of the measurement system that is used.DOE Functional Equation
The statistical analysis of the full factorial DOE results in the determination of the coefficients for a design space equation that relates all the control factors to the response factors. This equation includes interaction effects between control factors. This equation can then be used by designers to solve for the best overall system performance.
DOE in Minitab
Minitab is the statistical analysis software application that is most often used with Lean Six Sigma projects. Minitab has a Wizard that guides you through the setup and design of a Design of Experiments study. This lesson demonstrates how to use that Wizard.
Fractional Factorial Pros and Cons
This lesson compares the difference between the full factorial approach and fractional factorial approaches. It explains the pros and cons of using a fractional factorial methodology.Fractional Factorial DOE Methodology
This lesson describes the nine steps to be followed when using one of the fractional factorial DOE methods. The emphasis is one how the steps differ from the full factorial DOE methodology.Confounding Effects
This lesson explains the importance of designing a fractional factorial DOE study using a set of experiments that is balanced and orthogonal. Otherwise the runs can become confounded and that will invalidate the statistical analysis of the results.Factor Selection
This lesson builds on the previous factor selection lesson. However, now it addresses how the factor selection process changes as a fractional factorial DOE progresses through two or three levels of studies.Plackett-Burman DOE
The Plackett-Burman DOE is a special case fractional factorial DOE. It is used as a screening study when there are a large number of control factors. This lesson explains when to use Plackett-Burman DOE and how to design this type of study.Taguchi DOE
The Taguchi DOE is a special case fractional factorial DOE. It is used primarily for analyzing manufacturing processes. The Taguchi DOE separates the control factors into two categories and analyzes them with different DOE approaches. This lesson explains the characteristics of this type of study.DOE Analysis in Minitab
This lesson reviews the different types of graphical and tabular results for a DOE study that are generated by Minitab. Each of these types of results provides a different perspective on the analysis of the design that is being studied.DOE Factorial Plots
One of the most common techniques for analyzing the results of a DOE study in Minitab is to review the factor plots. These will provide insight into the optimal settings for control factors. The interactive plots will also highlight the settings associated with local maximum or minimum performance levels.
DOE in Design Creation
The DOE results can be used by design teams to make wise design decisions. This lesson will address how to use the DOE results in predicting system performance, designing system controls and establishing tolerances on system control and response factors.
Path of Steepest Ascent/Descent
Some DOE analyses will indicate that the optimal performance of the system would occur when control factors are set beyond the bounds of the study. When this occurs, it is best to shift the study to the likely region of optimal performance and then determine the best control factor settings. Following the path of steepest ascent or descent will ensure that the new analysis is conducted in a region with maximum or minimum performance.
DOE in Design Optimization
The DOE results can be used by design teams to improve and optimize an existing design based upon new needs or uses. The structure of the DOE study, particularly the fractional factorial DOE methodologies, allows the design team to easily establish optimal performance in a variety of settings.DOE in Problem Solving
The DOE results can be used by problem solving teams, such as Lean Six Sigma project teams, to identify which factors provide the major contribution to the problem or problem performance. It can also be used to explain the expected benefit from implementing different types of solutions.DOE Keys to Success
This final lesson reviews the key principles that must be followed when conducting a DOE study. It highlights the benefit of each and the dangers if the principle is not properly applied.
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Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.
Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.
Using Design of Experiments (DOE) techniques, you can determine the individual and interactive effects of various factors that can influence the output results of your measurements. You can also use DOE to gain knowledge and estimate the best operating conditions of a system, process or product.
See an example of DOE in action:
DOE applies to many different investigation objectives, but can be especially important early on in a screening investigation to help you determine what the most important factors are. Then, it may help you optimize and better understand how the most important factors that you can regulate influence the responses or critical quality attributes.
Another important application area for DOE is in making production more effective by identifying factors that can reduce material and energy consumption or minimize costs and waiting time. It is also valuable for robustness testing to ensure quality before releasing a product or system to the market.
What’s the alternative?
In order to understand why Design of Experiments is so valuable, it may be helpful to take a look at what DOE helps you achieve. A good way to illustrate this is by looking at an alternative approach, one that we call the “COST” approach. The COST (Change One Separate factor at a Time) approach might be considered an intuitive or even logical way to approach your experimentation options (until, that is, you have been exposed to the ideas and thinking of DOE).
Let’s consider the example of a small chemical reaction where the goal is to find optimal conditions for yield. In this example, we can vary only two elements, or factors:
- the volume of the reaction container (between 500 and 700 ml), and
- the pH of the solution (between 2.5 and 5).
We change the experimental factors and measure the response outcome, which in this case, is the yield of the desired product. Using the COST approach, we can vary just one of the factors at time to see what affect it has on the yield.
So, for example, first we might fix the pH at 3, and change the volume of the reaction container from a low setting of 500ml to a high of 700ml. From that we can measure the yield.
Below is an example of a table that shows the yield that was obtained when changing the volume from 500 to 700 ml. In the scatterplot on the right, we have plotted the measured yield against the change in reaction volume, and it doesn’t take long to see that the best volume is located at 550 ml.
Next, we evaluate what will happen when we fix the volume at 550 ml (the optimal level) and start to change the second factor. In this second experimental series, the pH is changed from 2.5 to 5.0 and you can see the measured yields. These are listed in the table and plotted below. From this we can see that the optimal pH is around 4.5.
The optimal combination for the best yield would be a volume of 550 ml and pH 4.5. Sounds good right? But, let’s consider this a bit more.
Gaining a better perspective with DOE
What happens when we take more of a bird’s eye perspective, and look at the overall experimental map by number and order of experiments?
For example, in the first experimental series (indicated on the horizontal axis below), we moved the experimental settings from left to right, and we found out that 550 was the optimal volume.
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Then in the second experimental series, we moved from bottom to top (as shown in the scatterplot below) and after a while we found out that the best yield was at experiment number 10 (4.5 pH).
The problem here is that we are not really certain whether the experimental point number 10 is truly the best one. The risk is that we have perceived that as being the optimum without it really being the case. Another thing we may question is the number of experiments we used. Have we used the optimal number of runs for experiments?
Zooming out and picturing what we have done on a map, we can see that we have only been exploiting a very small part of the entire experimental space. The true relationship between pH and volume is represented by the Contour Plot pictured below. We can see that the optimal value would be somewhere at the top in the larger red area.
So the problem with the COST approach is that we can get very different implications if we choose other starting points. We perceive that the optimum was found, but the other— and perhaps more problematic thing—is that we didn’t realize that continuing to do additional experiments would produce even higher yields.
See another example:
How to design better experiments
Instead, using the DOE approach, we can build a map in a much better way. First, consider the use of just two factors, which would mean that we have a limited range of experiments. As the contour plot below shows, we would have at least four experiments (defining the corners of a rectangle.)
These four points can be optimally supplemented by a couple of points representing the variation in the interior part of the experimental design.
The important thing here is that when we start to evaluate the result, we will obtain very valuable information about the direction in which to move for improving the result. We will understand that we should reposition the experimental plan according to the dashed arrow.
However, DOE is NOT limited to looking at just two factors. It can be applied to three, four or many more factors.
If we take the approach of using three factors, the experimental protocol will start to define a cube rather than a rectangle. So the factorial points will be the corners of the cube.
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In this way, DOE allows you to construct a carefully prepared set of representative experiments, in which all relevant factors are varied simultaneously.
DOE is about creating an entity of experiments that work together to map an interesting experimental region. So with DOE we can prepare a set of experiments that are optimally placed to bring back as much information as possible about how the factors are influencing the responses.
Plus, we will we have support for different types of regression models. For example, we can estimate what we call a linear model, or an interaction model, or a quadratic model. So the selected experimental plan will support a specific type of model.
Read more:
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Why is DOE a better approach?
We can see three main reasons that DOE Is a better approach to experiment design than the COST approach.
- DOE suggests the correct number of runs needed (often fewer than used by the COST approach)
- DOE provides a model for the direction to follow
- Many factors can be used (not just two)
In summary, the benefits of DOE are:
- An organized approach that connects experiments in a rational manner
- The influence of and interactions between all factors can be estimated
- More precise information is acquired in fewer experiments
- Results are evaluated in the light of variability
- Support for decision-marketing: map of the system (response contour plot)
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