DataDriven Insights from Predictive Analytics on Heterogeneous Experimental Data of Industrial Magnetic Materials Datadriven methods are becoming increasingly popular in the field of materials science. While most datadriven models are trained on simulation data as it is relatively easier to collect a large amount of data from physicsbased simulations, there are many challenges in applying datadriven methods on experiments: 1) experimental data is usually not clean; and 2) it generally has a greater degree of heterogeneity. In this project, we have developed a datadriven methodology to address these challenges on an industrial magnet dataset, where the goal is to predict magnetic properties (forward models) at different stages of the experimental workflow. The datadriven methodology consists of data cleaning, data preprocessing, feature extraction, and model development using traditional machine learning and deep learning methods to accurately predict magnet properties. In particular, we have developed three different types of predictive models: 1) numerical model using only numerical data containing composition and processing information; 2) image model using image data representing structure information; and 3) combination model using both types of data together. In addition to predictive models, the analysis and comparison of results across the models provide several interesting datadriven insights. Such datadriven analytics has the potential to help guide future experiments and realize the inverse models, which could significantly reduce costs and accelerate the discovery of new magnets with superior properties. The proposed models are already deployed in Toyota Motor Corporation.

Datadriven modeling of semibatch manufacturing: a rubber compounding test case The continuously growing amount of available data from manufacturing processes supports the development of datadriven models. The typical target application of these models is optimal control and continuous quality management within an objective of zerodefect manufacturing. However, data obtained from batch processes are characterized by its high dimensionality that exceeds the computational capabilities of online applications and datadriven modelu0027s reliability must be guaranteed for proper industrial implementation. We explore two approaches to reduce problemu0027s size: feature extraction and feature selection; several multivariate regression methods are also compared regarding it precision and robustness. We base our analysis on an industrial rubber compounding process where natural rubber is blended in a semibatch mixer with several additives, then it is further mixed up using cylinders and it is conditioned in bands for storing. For this process, real production data is collected and stored in the manufacturing execution system of the company. The objective of the analysis is to predict mechanical properties of the rubber at the end of the processes. Based on the provided data, several datadriven models are built and tested. From the comparison among them it is concluded: models based on feature extraction and artificial neural networks yield the highest accuracy, while featureselected models provide better physical interpretability and increased robustness regarding industrial deployment.