Efforts to miniaturize and customize electronic devices have attracted considerable amounts of attention in many industrial fields. Recently, due to its innovative printing technology with the capability of printing fine features onto non-planar substrates without masks, aerosol jet printing (AJP) is emerging as a promising printed-electronics technology capable of meeting the requirements of various advanced electronic applications. In this research, a novel manufacturing process based on AJP is proposed in order to fabricate highly flexible and conductive customized temperature sensors. To improve the flexibility and conductivity of the printed tracks, a silver nanoparticle/carbon nanotubes composite ink is developed. Customized temperature sensors are then designed and fabricated based on the optimized process parameters of AJP. It was found that the CNTs served as bridges to connect silver nanoparticles and defects, which could be expected to reduce the contact resistivity and enhance the flexibility of the printed sensor.

Additive manufacturing is an innovative manufacturing process that enables complex topological structures and low-volume, high-variety production. One of the major adaptations of this method is in the tire industry. Thin-walled sipes slit the tires to improve drainage and traction. The material properties of thin-walled structures manufactured by additive manufacturing are different and more sensitive than those of conventional cube-shaped specimens. Thin-walled maraging steel specimens are considered to be able to model the relationship between the process parameters and the properties of the sipes adequately. Tire sipes are made of maraging steel. Maraging steels are a class of low-carbon high-alloy martensitic steel generally providing high strength, ductility, and good fracture toughness. In particular, these alloys exhibit a good combination of strength and toughness at elevated temperatures, which has been desirable for applications in aerospace and tooling. In order to consider productivity, multi-objective process parameter optimization with a build-time-constrained model is proposed.