Prediction of the tensile strength of tablets using LSTM networks on compression profiles

Abstract

This publication's objective was to predict the tensile strength of tablets using an analysis of process data comprising compression pressure, sampling timestamps, and punch positions. A recurrent neuronal network, specifically designed with Long Short-Term Memory layers, was utilized to accommodate the time-series characteristics of the data. A dataset from 344 tablet compression cycles was employed for model training, after which the model demonstrated a predictive ability with a coefficient of determination of 0.954 on test data from 804 tableting cycles. The foundational database incorporated data from both pure substances and mixtures consisting of up to four components compressed at various compression pressures and with three different tablet masses. Interestingly, the prediction errors did not exhibit any significant correlation with specific materials, mixtures, maximum compression pressures, or tablet weights. With the aid of the model, it was possible to calculate the entire tabletability profile of twelve substances from just a single compression process each. Models of this nature bear promising potential for future application in the research and development of formulations as well as in production processes to predict tensile strength.