Effect of Infill Patterns with Machine Learning Techniques on the Tensile Properties of Polylactic Acid-Based Ceramic Materials with Fused Filament Fabrication

The field of additive manufacturing is quickly evolvingfrom prototypingto manufacturing. Researchers are looking for the best parametersto boost mechanical strength as the demand for three-dimensional (3D)printers grows. The goal of this research is to find the best infillpattern settings for a polylactic acid (PLA)-based ceramic materialwith a universal testing machine; the impact of significant printingconsiderations was investigated. An X-ray diffractometer and energy-dispersiveX-ray spectroscopy with an attachment of scanning electron microscopywere used to investigate the crystalline structure and microstructureof PLA-based ceramic materials. Tensile testing of PLA-based ceramicsusing a dog bone specimen was printed with various patterns, as perASTM D638-10. The cross pattern had a high strength of 16.944 MPa,while the tri-hexagon had a peak intensity of 16.108 MPa. Cross3Dand cubic subdivisions have values of 4.802 and 4.803 MPa, respectively.Incorporating the machine learning concepts in this context is topredict the optimal infill pattern for robust strength and other mechanicalproperties of the PLA-based ceramic model. It helps to rally the precisionand efficacy of the procedure by automating the job that would entailsubstantial physical effort. Implementing the machine learning techniqueto this work produced the output as cross and tri-hexagon are theefficient ones out of the 13 patterns compared.