AI-Assisted Optimization of 3D Printing Parameters For Quality Enhancement of Dental Prostheses

Abstract

The dental quality of printed dental prostheses is highly affected by process parameters, including the layer height, printing speed, printing orientation, exposure time, and some resin-related parameters. Traditional ways of parameter selection often involve empirical methods, resulting in dimensional inaccuracies, surface flaws, high material consumption, and also, high production time. An artificial intelligence (AI)-aided optimization frame-work is presented in this work for choosing the right 3D printing parameters to boost the quality and manufacturing effectiveness of personalized dental prostheses. The proposed approach involves data acquisition followed by collection or preprocessing, feature analysis, prediction using machine learning models, and parameter optimization to identify relation-ships among the printing conditions and quality factors, including dimensional accuracy, surface roughness, manufacturing time and material consumption. Various performance metrics are employed to analyze the different machine learning models and then the best one is utilized to determine the optimum parameter. It is planned to reduce the number of trial-anderror procedures and to achieve data driven parameter selection. The improved parameters are expected to yield reduced time and material requirements for manufacturing when compared to commonly used parameters, along with improved dimensional accuracy and surface finishes. It’s here where the use of AI-assisted optimization could enhance the reliability, repeatability, and success of digitally manufactured dental prostheses.