We are working with a complex application i.e. a physical measurement in a lab, that has approximately 230 different input parameters, many of which are ranges or multiple-value.The application produces a single output, which is then verified in an external (physical) process. At the end of the process the individual tests are marked as success or fail. That is, despite the many input parameters, the output is assessed in a boolean manner.When tests fail, the parameters are 'loosened' slightly and re-tested.We have about 20,000 entries in our database, with both success and fail, and we are considering a machine learning application to help in two areas:1) Initial selection of optimum parameters2) Suggestions for how to tune the parameters after a fail    Many of the input parameters are strongly related to each other. I studied computer science in the mid-90s, when the focus was mostly expert systems and neural networks. We also have access to some free CPU hours of Microsoft Azure Machine Learning. What type of machine learning would fit these use-cases?
Which type of machine learning to use
machine learning;neural network
With using R, You could look at trees / randomforests. Since you have correlated variables, you could look into Projection pursuit classification trees (R package pptree). And there soon will be a ppforest package. But this is still under  development.  You could also combine randomforest with the package forestFloor to see the curvature of the randomforest and work from there.