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<p>While the dental 3D scanner market is projected to surge towards a $2.61&#xa0;billion valuation by 2032, with a robust 9.1% compound annual growth rate (CAGR), the fundamental accuracy underpinning its diagnostic promise is crucial for addressing the oral health of nearly 3.5&#xa0;billion individuals, which remains intrinsically tied to the often-overlooked optimization of its scanning parameters. Building upon this confluence of rapidly increasing technology and pressing oral health crisis, proposed research aims to optimize the process parameters of a handheld 3D scanner for accurate and expedite scanning of patient-specific denture models. Scanning experiments were conducted using selected parametric combinations of Scanning distance (SD), Scanning angle (SA), and light intensity (LI), determined through a design of experiments (DoE) approach for evaluating output responses such as standard deviation and scanning time. Furthermore the present research employ the potential of metaheuristic optimization algorithms, specifically an implementation of the NSGA-II (Non dominating sorted genetic algorithm) framework. The Artificial Neural Network model trained on an initial dataset of scan runs, to predict the accuracy and scan time across the parameter space, thereby significantly reducing the computational cost associated with exhaustive experimental trials. The optimization process identifies solutions that represent the best trade-offs between scanning accuracy—evaluated through detailed metrological analysis measuring deviations of digital models from physical standards—and scanning time, thereby balancing precision and efficiency. The primary emphasis of this research is to establish a scientifically validated, data-driven protocol for optimizing dental 3D scanning, thereby ensuring that this transformative technology realizes its full potential in delivering precise, efficient, and ultimately, improved patient care on a global scale.</p>

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Metrological Analysis and Multi Objective Optimization of 3D Scanning Parameters for Precise Scanning of Patient-Specific Dental Models

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Sumit Gahletia,
  • Ramesh Kumar Garg

摘要

While the dental 3D scanner market is projected to surge towards a $2.61 billion valuation by 2032, with a robust 9.1% compound annual growth rate (CAGR), the fundamental accuracy underpinning its diagnostic promise is crucial for addressing the oral health of nearly 3.5 billion individuals, which remains intrinsically tied to the often-overlooked optimization of its scanning parameters. Building upon this confluence of rapidly increasing technology and pressing oral health crisis, proposed research aims to optimize the process parameters of a handheld 3D scanner for accurate and expedite scanning of patient-specific denture models. Scanning experiments were conducted using selected parametric combinations of Scanning distance (SD), Scanning angle (SA), and light intensity (LI), determined through a design of experiments (DoE) approach for evaluating output responses such as standard deviation and scanning time. Furthermore the present research employ the potential of metaheuristic optimization algorithms, specifically an implementation of the NSGA-II (Non dominating sorted genetic algorithm) framework. The Artificial Neural Network model trained on an initial dataset of scan runs, to predict the accuracy and scan time across the parameter space, thereby significantly reducing the computational cost associated with exhaustive experimental trials. The optimization process identifies solutions that represent the best trade-offs between scanning accuracy—evaluated through detailed metrological analysis measuring deviations of digital models from physical standards—and scanning time, thereby balancing precision and efficiency. The primary emphasis of this research is to establish a scientifically validated, data-driven protocol for optimizing dental 3D scanning, thereby ensuring that this transformative technology realizes its full potential in delivering precise, efficient, and ultimately, improved patient care on a global scale.