Process Parameter Optimization on Developing Self-sensing Textile

Manufacturing defects of Textile-reinforced composites can be minimized by FDM modeling of conductive polymers.

Half factorial design of experiment approach is used to determine parameters having greatest influence over mechanical and electrical properties of 3D printed sensor material, made of conductive polymer composite of Graphene and PLA.

Carried out Multi Attribute Decision Making (MADM) methods and Entropy weighting to optimize manufacturing process parameters for maximum sensor performance, achieving overall influence of the parameters, ranking as Layer height, extrusion multiplier, nozzle temperature and print speed.

Characterization of textile-embedded-sensor:

• Mechanical characterization performed using an Instron 5969 universal load frame with a 50kN load cell and double action wedge grips following ASTM D638 tensile test using a strain rate of 1mm/minute.
• Electrical measurements were done using digital multimeter and Arduino Uno. The Arduino recorded the resistance through a voltage-divider circuit. A python script was used to record Arduino output to text files as well as for post processing.