The property palette: A rapid printing of performance-tunable blended polymers guided by artificial neural network

Abstract

Existing commercial material extrusion (ME) polymer filaments frequently fail to meet the requirements of actual workpieces and often need improvement through modification. However, traditional modification methods are time-consuming and expensive. Here, we develop a 3D printer capable of blending three polymers and programming the component ratios, which can speed up such a modification process. Additionally, workpieces with varying properties can be quickly produced (just like mixing colors according to the three primary colors) with the guidance of machine learning algorithms. As a demonstration, we used polylactic acid (PLA), thermoplastic polyurethane (TPU), and polyethylene terephthalate glycol (PETG) three commercial filaments as base materials to printout different objects. The tensile strength, tensile modulus, flexural strength, flexural modulus, and hardness of 67 groups of different PLA/TPU/PETG blend components were tested. Based on these data, artificial neural network (ANN) optimization algorithms were used to discover the relationship between the components and properties. The results show that using ANN to predict the mechanical properties of blended polymers can effectively accelerate the development of materials. Materials with tunable properties and complex structures can be manufactured from simple raw materials through 3D printing quickly.