Outcome 3b of the Engineering Criteria of 2000, states that engineering graduates must have “an ability to design and conduct experiments, as well as to analyze and interpret data”.
Engineers typically perform one of three types of experiments:
a. A theoretical relationship between two or more variables is already known (or at least suspected) and an experiment is needed to verify or quantify this relationship.
b. A theoretical relationship between two or more variables is not available but rather sought through an experiment.
c. A new product is being developed and a test is needed to confirm that it meets the design specifications, before committing it to production.
The first two types of experiments are shared between engineering and …show more content…
We also note that stages 2 and 3 are often done simultaneously or in reverse order.
1. Recognition of and statement of the problem.
It is not often simple to realize that a problem that requires experimentation exists, nor is it simple to develop a clear and generally accepted statement of the problem. All ideas about the objectives for the experiment must be developed in this stage. In order to fully understand the problem, it is often best to seek input from other stakeholders related to the issue, for example, engineers, quality assurance personnel, as well as management. This is referred to as a team approach to designing experiments.
It is also important to keep the overall objective in mind when determining the problem statement. For example is this a new system or process, it is more likely we are going to be concerned with characterization or factor screening, as opposed to existing systems that may focus on optimization.
It is also important to note that the problem being formulated may not fully be solved by a single experiment, in which case a sequential approach may be employed that will utilize a series of small experiments each with a specific objectives. This method helps to curb time and material …show more content…
By this point we know what response we are going to measure, and which variables we are going to alter to achieve this, now we find the most effective experimental type to achieve this.
There are many interactive statistical software packages that support this phase, simply by entering information about the number of factors, levels and ranges the program will present a selection of designs to choose from.
Traditional experiments will alter one factor at a time and record the response variable. These experiments can be laborious, expensive and time-consuming, as many iterations and combinations must be tested in order to gain adequate amounts of information. A new design type is referred to as the Modern Design of Experiments (MDOE) technique. MDOE generates more information per data point than OFAT. This is accomplished by changing the levels of multiple factors rather than just changing one factor at a time. The MDOE technique results in fewer data points which reduces direct operating costs and also cycle time – which can be the real cost driver in a range of practical research and technology development scenarios. However, MDOE requires complex mathematical analysis to decouple the multiple variables and their
Engineers typically perform one of three types of experiments:
a. A theoretical relationship between two or more variables is already known (or at least suspected) and an experiment is needed to verify or quantify this relationship.
b. A theoretical relationship between two or more variables is not available but rather sought through an experiment.
c. A new product is being developed and a test is needed to confirm that it meets the design specifications, before committing it to production.
The first two types of experiments are shared between engineering and …show more content…
We also note that stages 2 and 3 are often done simultaneously or in reverse order.
1. Recognition of and statement of the problem.
It is not often simple to realize that a problem that requires experimentation exists, nor is it simple to develop a clear and generally accepted statement of the problem. All ideas about the objectives for the experiment must be developed in this stage. In order to fully understand the problem, it is often best to seek input from other stakeholders related to the issue, for example, engineers, quality assurance personnel, as well as management. This is referred to as a team approach to designing experiments.
It is also important to keep the overall objective in mind when determining the problem statement. For example is this a new system or process, it is more likely we are going to be concerned with characterization or factor screening, as opposed to existing systems that may focus on optimization.
It is also important to note that the problem being formulated may not fully be solved by a single experiment, in which case a sequential approach may be employed that will utilize a series of small experiments each with a specific objectives. This method helps to curb time and material …show more content…
By this point we know what response we are going to measure, and which variables we are going to alter to achieve this, now we find the most effective experimental type to achieve this.
There are many interactive statistical software packages that support this phase, simply by entering information about the number of factors, levels and ranges the program will present a selection of designs to choose from.
Traditional experiments will alter one factor at a time and record the response variable. These experiments can be laborious, expensive and time-consuming, as many iterations and combinations must be tested in order to gain adequate amounts of information. A new design type is referred to as the Modern Design of Experiments (MDOE) technique. MDOE generates more information per data point than OFAT. This is accomplished by changing the levels of multiple factors rather than just changing one factor at a time. The MDOE technique results in fewer data points which reduces direct operating costs and also cycle time – which can be the real cost driver in a range of practical research and technology development scenarios. However, MDOE requires complex mathematical analysis to decouple the multiple variables and their