A Neural Network-Based Framework of Real-Time Heat Map Generation for Surgical Hand-Held Drilling

IF 1 Q4 ENGINEERING, MANUFACTURING

Journal of Micro and Nano-Manufacturing Pub Date : 2022-06-27 DOI:10.1115/msec2022-85693

Pei-Ching Kung, M. Heydari, Bruce L. Tai

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引用次数: 1

Abstract

Understanding heat generation can help improve one’s surgical drilling skill to avoid thermal injury. Surgical drilling is mostly done manually, so it can be time-consuming to create personalized thermal models to assess each drilling. For this reason, this paper presents a framework for 2D real-time heat map generation for a moving, varying heat source problem based on neural networks (NN) and linear time-invariant system (LTI). In this framework, several location-specific heat maps and their temporal responses are calculated by finite element analysis (FEA) and trained through NN to build a surrogate model. The total heat map of any given moving heat source can be generated by the superposition of a series of location-specific heat maps along the moving path. The NN training shows a correlation over 99%, indicating a highly representative surrogate model. The validation study of comparing two FEA-based moving heat source problems with the framework predicted results show overall good agreement. Error sources and improvement methods are discussed in this paper.

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基于神经网络的外科手持式钻孔实时热图生成框架

了解热的产生可以帮助提高手术钻孔技巧，避免热伤。外科手术钻孔大多是手工完成的，因此创建个性化的热模型来评估每个钻孔可能非常耗时。为此，本文提出了一种基于神经网络(NN)和线性时不变系统(LTI)的移动变热源问题二维实时热图生成框架。在该框架中，通过有限元分析(FEA)计算几个特定位置的热图及其时间响应，并通过神经网络训练建立代理模型。任何给定的移动热源的总热图都可以通过沿着移动路径叠加一系列特定位置的热图来生成。神经网络训练显示相关性超过99%，表明代理模型具有很高的代表性。将两个基于有限元的移动热源问题与框架预测结果进行对比验证，结果表明两者总体上吻合较好。本文讨论了误差来源和改进方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Micro and Nano-Manufacturing ENGINEERING, MANUFACTURING-

CiteScore

2.70

自引率

0.00%

发文量

期刊介绍： The Journal of Micro and Nano-Manufacturing provides a forum for the rapid dissemination of original theoretical and applied research in the areas of micro- and nano-manufacturing that are related to process innovation, accuracy, and precision, throughput enhancement, material utilization, compact equipment development, environmental and life-cycle analysis, and predictive modeling of manufacturing processes with feature sizes less than one hundred micrometers. Papers addressing special needs in emerging areas, such as biomedical devices, drug manufacturing, water and energy, are also encouraged. Areas of interest including, but not limited to: Unit micro- and nano-manufacturing processes; Hybrid manufacturing processes combining bottom-up and top-down processes; Hybrid manufacturing processes utilizing various energy sources (optical, mechanical, electrical, solar, etc.) to achieve multi-scale features and resolution; High-throughput micro- and nano-manufacturing processes; Equipment development; Predictive modeling and simulation of materials and/or systems enabling point-of-need or scaled-up micro- and nano-manufacturing; Metrology at the micro- and nano-scales over large areas; Sensors and sensor integration; Design algorithms for multi-scale manufacturing; Life cycle analysis; Logistics and material handling related to micro- and nano-manufacturing.