Searching the optimal parameters of a 3D scanner in surface reconstruction of a dental model using central composite design coupled with metaheuristic algorithms

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...

Full description

Saved in:
Bibliographic Details
Published in:International journal on interactive design and manufacturing 2024-12, Vol.18 (10), p.7401-7411
Main Authors: Kaushik, Ashish, Garg, Ramesh Kumar
Format: Article
Language:English
Subjects:
Accuracy
CAE) and Design
Computer-Aided Engineering (CAD
Dental materials
Dentures
Design
Digitization
Electronics and Microelectronics
Engineering
Engineering Design
Factorial experiments
Genetic algorithms
Heuristic methods
Image reconstruction
Industrial Design
Instrumentation
Lasers
Light
Luminous intensity
Measurement techniques
Mechanical Engineering
Neural networks
Original Paper
Parameter estimation
Process parameters
Prostheses
Scanners
Search algorithms
Software
Standard deviation
Three dimensional composites
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1955-2513
1955-2505
DOI:10.1007/s12008-023-01587-z