用神经网络从3D模型直接预测制造步骤和时长，误差低于3秒。
Technology prediction of a 3D model using Neural Network
- 将3D模型转为多视角2D图像，输入神经网络预测工序时间
- 平均绝对误差低于3秒，在多样化产品中提升计划效率
- 适合需要快速排产的定制化制造场景
准确估算生产时间对高效制造调度至关重要，但依赖专家经验或历史数据的传统方法在动态或定制化生产环境中常显不足。本文提出一种数据驱动方法，直接从具有暴露几何结构的产品3D模型中预测制造步骤及其持续时间。通过将3D模型渲染为多视角2D图像，并采用受生成查询网络启发的神经网络，该方法学习将几何特征映射到预定义生产步骤的时间估计，平均绝对误差低于3秒，显著提升了跨多种产品类型生产计划的可行性。
原文摘要 · Abstract (English)
Accurate estimation of production times is critical for effective manufacturing scheduling, yet traditional methods relying on expert analysis or historical data often fall short in dynamic or customized production environments. This paper introduces a data-driven approach that predicts manufacturing steps and their durations directly from 3D models of products with exposed geometries. By rendering the model into multiple 2D images and leveraging a neural network inspired by the Generative Query Network, the method learns to map geometric features into time estimates for predefined production steps with a mean absolute error below 3 seconds making planning across varied product types easier.
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