INVESTIGATION OF THE CORRELATION BETWEEN MICRO-SCALE PARTICLE DISTRIBUTION IN 3D PRINTING AND MACROSCOPIC COMPOSITE PERFORMANCE

To date, various multi-material and multi-functional Additive Manufacturing technologies have been developed for the production of multi-functional smart structures. Those technologies are capable of controlling the local distributions of materials, hence achieving gradient or heterogeneous properties and functions. Such multi-material and multi-functional manufacturing capability opens up new applications in many fields. However, it is still largely unknown that how to design the localized material distribution to achieve the desired product properties and functionalities. To address this challenge, the correlation between the micro-scale material distribution and the macroscopic composite performance needs to be established. In our previous work, a novel Magnetic-field-assisted Stereolithography (M-PSL) process has been developed, for fabricating magnetic particle-polymer composites. Hence, in this work, we focus on the study of magnetic-field-responsive particle-polymer composite design, with the aim of developing some guidelines for predicting the magnetic-field-responsive properties of the composite fabricated by M-PSL process. Micro scale particle distribution parameters, including particle loading fraction, particle magnetization, and distribution patterns, are investigated. Their influences on the properties of particle-polymer liquid suspensions, and the properties of the 3D printed composites, are characterized. By utilizing the magnetic anisotropy properties of the printed composites, different motions of the printed parts could be triggered at different relative positions under the applied magnetic field. Physical models are established, to predict the particle-polymer liquid suspension properties and the trigger conditions of fabricated parts. Experiments are performed to verify the physical models. The predicted results agree well with the experimental measurements, indicating the effectiveness of predicting the macroscopic composite performance using micro-scale distribution data, and the feasibility of using the physical models for guiding the multi-material and multi-functional composite design.