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Searching the optimal parameters of a 3D scanner in surface reconstruction of a dental model using central composite design coupled with metaheuristic algorithms
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Searching the optimal parameters of a 3D scanner in surface reconstruction of a dental model using central composite design coupled with metaheuristic algorithms
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Journal Article
Searching the optimal parameters of a 3D scanner in surface reconstruction of a dental model using central composite design coupled with metaheuristic algorithms
2024
Overview
Determining the right process parameters for 3D scanning is crucial for rigorously inspecting reverse-engineered dental models. However, it is seen that various parameters, such as scanning distance, light intensity, and scanning angle, are rarely examined during preliminary experimental trials. The proposed research examines a method for estimating the ideal values of the aforementioned scanning parameters that minimize acquisition error. The face-centered, central composite design suggested twenty runs of experimentation with varying input parameter combinations. In each of these twenty scans, a physical denture model was scanned to extract a 3D CAD model, and the standard deviation of each model was calculated to investigate into the scan accuracy of the recorded data. A neural network architecture is used to train a model across input and output, and then the model is optimized by a genetic algorithm for the best results. Through a scanning distance of 208.28 mm, scanning angle of 54.1 degrees, and light intensity of 18 W/meter square, in a total of twenty trial runs, the lowest possible standard deviation of 0.2626. The standard deviation is minimized for achieving maximum accuracy using a heuristic GA-ANN algorithm with a scanning distance of 152.4 mm, scanning angle of 61.8 degrees, and light intensity of 14 watts per square meter and same has been validated experimentally.
Publisher
Springer Paris,Springer Nature B.V
Subject
