发表机构
Faculty of Mechanical Engineering Technical University of Cluj-Napoca（机械工程学院克卢日-纳波卡技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究V型梁热传感器数据驱动逆向设计，通过五次探索性试验得出两阶段解决方案，即训练神经网络正向模型并结合梯度下降逆向优化，利用3000样本数据集，预测位移MAPE达4.76%，超70%预测MAPE低于5%。
AI 中文摘要
本文提出了一个用于V型梁热传感器数据驱动逆向设计的机器学习框架。目标是确定最佳传感器几何形状：在给定温度下实现目标位移的梁倾斜角度、梁长度和梁宽度。设计还应提供具有最小结构体积和传感器必须承受的最小机械应力的几何形状。该问题不适定，因为对于给定位移有多种可能的几何配置，导致直接回归方法失败。我们记录了一系列五次探索性试验，最终形成了两阶段解决方案：训练神经网络正向模型将几何形状和材料常数映射到传感器响应，在冻结的正向模型上进行梯度下降逆向优化，同时最小化应力和体积。所提出的管道利用3000个样本数据集，预测位移的平均绝对百分比误差（MAPE）为4.76%，超过70%的预测MAPE低于5%。
英文摘要
This paper presents a machine learning framework for data-driven inverse design of V-beam thermal sensors. The goal is to determine the optimal sensor geometry: beam inclination angle, beam length and beam width that achieves a target displacement under a given temperature. The design should also provide the geometry with minimum structure volume and minimum mechanical stress the sensor must support. This problem is ill-posed as for a given displacement there are multiple possible geometric configurations, causing direct regression methods to fail. We document a series of five exploratory trials that progressively revealed the nature of the problem culminating in a two-phase solution: a neural network forward model trained to map geometry and material constants to sensor responses, a gradient-descent inverse optimization over the frozen forward model, minimizing stress and volume simultaneously. The proposed pipeline utilizes a 3000-sample dataset and achieves a MAPE of 4.76% for predicting the displacement, more than 70% of predictions having MAPE of under 5%.
