论文标题
基于图像的人工智能授权替代模型和形状变形器，以实时空白形状优化在热冲压过程中
Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
论文作者
论文摘要
随着现代制造技术的复杂性的提高，需要迭代且昂贵的模拟的传统反复试验设计变得不可靠且耗时。这种困难对于设计热踩安全性关键组件的设计尤其重要，例如超高强度钢（UHSS）B柱。为了降低设计成本并确保可制造性，已经研究和实施了基于标量的人工授权替代建模（SAISM），这可以允许实时可制造的结构设计优化。但是，SAISM的精度和普遍性低，通常需要大量的训练样本。为了解决这个问题，在这项研究中开发了一种基于图像的人工授权替代建模（IAISM）方法，并结合了基于自动码头的空白形生成器。基于面罩的IASISS，训练了基于掩模的se-u-net架构，以预测给定任意空白形状的形式组成部分的完整稀疏场。只有256个培训样本可以实现IAIS的出色预测性能，这表明使用结构化数据表示，工程AI任务的小型学习性质。训练有素的自动码头编码器，受过训练的面具RES-SE-U-NET和ADAM优化器通过修改潜在矢量来进行空白优化。优化器可以迅速找到满足制造性标准的空白形状。作为高准确性和可推广的替代建模和优化工具，提议的管道有望将其集成到全链数字双胞胎中，以进行实时，多目标设计优化。
As the complexity of modern manufacturing technologies increases, traditional trial-and-error design, which requires iterative and expensive simulations, becomes unreliable and time-consuming. This difficulty is especially significant for the design of hot-stamped safety-critical components, such as ultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensure manufacturability, scalar-based Artificial-Intelligence-empowered surrogate modelling (SAISM) has been investigated and implemented, which can allow real-time manufacturability-constrained structural design optimisation. However, SAISM suffers from low accuracy and generalisability, and usually requires a high volume of training samples. To solve this problem, an image-based Artificial-intelligence-empowered surrogate modelling (IAISM) approach is developed in this research, in combination with an auto-decoder-based blank shape generator. The IAISM, which is based on a Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field of the as-formed component given an arbitrary blank shape. Excellent prediction performance of IAISM is achieved with only 256 training samples, which indicates the small-data learning nature of engineering AI tasks using structured data representations. The trained auto-decoder, trained Mask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blank optimisation by modifying the latent vector. The optimiser can rapidly find blank shapes that satisfy manufacturability criteria. As a high-accuracy and generalisable surrogate modelling and optimisation tool, the proposed pipeline is promising to be integrated into a full-chain digital twin to conduct real-time, multi-objective design optimisation.
