Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining Martensite start temperature (MsT) is an important characteristic of steels, knowledge of which is vital for materials engineers to guide the structural design process of steels. It is defined as the highest temperature at which the austenite phase in steel begins to transform to martensite phase during rapid cooling. Here we describe the development and deployment of predictive models for MsT, given the chemical composition of the material. The datadriven models described here are built on a dataset of about 1000 experimental observations reported in published literature, and the best model developed was found to significantly outperform several existing MsT prediction methods. The datadriven analyses also revealed several interesting insights about the relationship between MsT and the constituent alloying elements of steels. The most accurate predictive model resulting from this work has been deployed in an online webtool that takes as input the elemental alloying composition of a given steel and predicts its MsT. The online MsT predictor is available at http:info.eecs.northwestern.eduMsTpredictor.

A RealTime Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes Additive Manufacturing (AM) is a manufacturing paradigm that builds threedimensional objects from a computeraided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and timeconsuming for predicting multiscale multiphysics phenomena in AM, physicsinformed datadriven machinelearning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower realtime control systems using insitu data. In this paper, we design and develop essential components of a scientific framework for developing a datadriven modelbased realtime control system. Finite element methods are employed for solving timedependent heat equations and developing the database. The proposed framework uses extremely randomized trees  an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https:github.compaularindammliteradditive.