Question

Describe the design experiments method for product and process optimization

Answer 1

Response surface methodology (RSM) is a collection of statistical and mathematical techniques useful for developing, improving, and optimizing products and processes. The most extensive applications of RSM are particularly in situations where several input variables have potentially influence on some performance measures or quality characteristics of the product or process. RSM initiates from design of experiments (DOE) to determine the factors’ values for conducting experiments and collecting data. The data are then used to develop an empirical model that relates the process response to the factors. Subsequently, the model facilitates to search for better process response, which is validated through experiments. The above procedure iterates until an optimal process is identified or the limit on experimental resources is reached. RSM is a very important tool for product and process improving in the improvement phase of Six Sigma Management.

Also there is another method called Taguchi method.This method is an alternative method to review loss due to poor quality.It's also known as robust design and seeks to minimize the sensitivity to noise for any product or process.The conventional loss function states that products may be accepted if they are within statistical tolerance limits. Taguchi's loss function states that there is a loss unless the product exactly adheres to the target.The product then, must be designed in such a way that the control factors settings are optimal and minimize sensitivity to noise.The main advantages of robust design are in design transferability and in less experiments conducted.

The advantage of RSM over taguchi method-

Taguchi designs help you determine, the parameter settings for experiments that can give best or optimal settings. However this optimal setting value depends on the number of experiment you can afford to do as experimentation itself involves great cost.

RSM on the other hand involves more experimentation, however it can take you closer to global optimum.

Thus when you have to compromise on number of experiments you can perform, Taguchi is preferred. But if rigorous experimentation is possible, RSM will always take you much closer to global optimum settings as compared to Taguchi.