Manufacturing process encoding through natural language processing for prediction of material properties

Knowledge of manufacturing processes is crucial to determine the final properties of a material, thus this work focuses on analyzing the relationship between final properties, chemical composition, and manufacturing process through data analysis. Furthermore, techniques of natural language processing are used to encode the manufacturing process as input in the neural network. The work consisted of two main parts: firstly, the relevant data was gathered, cleaned-up, and analyzed using statistical and probabilistic methods, K-means, and Principal Components Analysis (PCA), and secondly, a model was developed to predict elongation, yield, and tensile strength. Fully Connected Neural Network (FCNN) algorithms were used to build the aforementioned model. In addition, in order to avoid overfitting and evaluate the model dropout function and K-fold cross-validation are incorporated. Results demonstrated reasonable accuracy in elongation, yield, and tensile strength. Two cases of mechanical properties prediction of stainless steel alloy were presented, first, an existing alloy that was not in the training and test set, and second a suggestion of a new stainless steel alloy, which combines good YS, UTS, and Pitting resistance equivalent number (PREN). Additionally, it was considered an example from the TRIP family to showcase the tool's versatility across various steel types.