It is easy to optimize the performance when only one or two parameters need to be varied, but it is not straightforward when several variables are involved. When many variables are involved, it becomes significantly more complex to (1) determine the critical variables and (2) optimize them. Although one can use a design of experiments approach, it requires significant resources to run these experiments, which translates to a lot of time and money. An alternative approach is Machine Learning, which is a rapid method to maximize the performance of a material by optimizing the ingredients in the formulation using Artificial Intelligence.
Advances in computational algorithms and the significant increase in computational resources currently available has led to the new and exciting field of Artificial Intelligence, and Machine Learning in particular. This has reduced the need to perform a large number of experiments in order to obtain the optimized process parameters, by using Machine Learning Algorithms. The ML algorithms can determine a limited number of experiments (called training set) to predict and optimize the value of the desired characteristic. Machine Learning can also be used to determine which parameters are more important (and need to be observed more closely during manufacturing) and which parameters are less important.