Figure 1.

A demonstration of the multi‐steps taken in this study to explain the printability. The flow of this study is depicted from left to right of the figure. First, the formulations based on different additives and starting HA solutions were prepared. In the second step, rheological characterizations and printing experiments were performed. The quantification step involved the extraction of various rheological features and quantitative analysis of the printability of formulations. In the next step, datasets based on combining all acquired features and printability scores were fabricated. By using the generated datasets, a random forest ML algorithm was trained. In the final step, a post‐analysis of the obtained model revealed the correlations between data and the influence on making a decision by the model.