Effect of Process Parameters on the Quality of Additively Manufactured PETG-Silk Composite
Source abstract
Abstract
Optimization of the print setting is important to achieve the desired product characteristics, which, in turn, reduce the effort of the manufacturer and the cost of production. Natural fiber reinforced PETG composites can be a potential 3D printing raw material for additive manufacturing of medical products like scaffolds, implants, prostheses, and orthoses. But the process parameters of this unique composite material need to be optimized for the fabrication of complex structures. Here, PETG-silk fibroin (PETG-SF) composites were printed by optimizing the process parameters based on single and multi-objective optimization techniques for responses such as minimum porosity, maximum dimensional accuracy, and maximum yield load. For accurate measurement of porosity and dimensional accuracy, micro-CT analysis was employed. Single objective optimization results indicate layer height and printing temperature as the highest contributing factor for maximum dimensional accuracy, maximum yield load, and for minimum porosity. Multi objective optimization result shows high temperature, low layer height, and low speed are the best settings for minimum porosity, maximum yield load, and maximum dimensional accuracy. Finally, the optimized parameters were used to print an organic shaped object to validate the results for the production of patient specific anatomically conformed prosthesis and implants. Altogether, our results show that the process parameters have a significant influence on the 3D printing of natural fiber reinforced composites and a complex structure can be effectively fabricated with the optimized parameter settings.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Silk-based biomaterials and applications · Bone Tissue Engineering Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (65)
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