Machine-interactive decision-assistance using a pre-trained natural language processing model for 4D printing technique selection

The present research showcases a machine-interactive approach for making decisions using a pre-trained natural language processing (NLP) model. The method is developed for 4D (4-dimensional) printing technique selection, as a plurality of variables is involved, such as process, material, design, and sequence selections. Due to the availability of numerous options, arriving at a preferred choice of technique requires expertise and time. The developed method aids in finding assistance from a single source. The approach incorporates bidirectional encoder representations from transformers (BERT), which accommodates parallel meanings of user requests, such as synonyms and adjectives, among others. The closed-loop system is programmed with a set of 7 prompts. It also introduces additional affirmation prompts to navigate both ambiguous phrasing and out-of-scope detection in order to receive a meaningful recommendation from the machine. The rule-governed technique (lightweight rule set) guides the selection of the conformable request during each prompt. The inference-based approach takes user requests, performs objective classification using BERT according to selected criteria, then dynamically filters the data, and recommends suggestions, with an inference time of 0.79 s. The modified model also establishes multi-level relationships among prompts for text classification. k-fold validation reached highest possible accuracy upon training with optimal hyperparameters. The fine-tuned method developed in Python environment can be generalized for other systems. The present research demonstrates the possibility of adapting an openly accessible model for developing a decision-assistance system with minimal personal computational resources.