In the realm of forging processes, the challenge of real-time process control amid inherent variabilities is prominent. To tackle this challenge, this article introduces a Proper Orthogonal Decomposition (POD)-based surrogate model for a one-blow cold upsetting process in copper billets. This model effectively addresses the issue by accurately forecasting energy setpoints, billet geometry changes, and deformation fields following a single forging operation. It utilizes Bézier curves to parametrically capture billet geometries and employs POD for concise deformation field representation. With a substantial database of 36,000 entries from 60 predictive numerical simulations using FORGE® software, the surrogate model is trained using a multilayer perceptron artificial neural network (MLP ANN) featuring 300 neurons across 3 hidden layers using the Keras API within the TensorFlow framework in Python. Model validation against experimental and numerical data underscores its precision in predicting energy setpoints, geometry changes, and deformation fields. This advancement holds the potential for enhancing real-time process control and optimization, facilitating the development of a digital twin for the process.

在锻造工艺领域，固有变化中实时过程控制的挑战非常突出。为了应对这一挑战，本文介绍了一种基于本征正交分解 (POD) 的替代模型，用于铜坯的一次性冷镦工艺。该模型通过准确预测单次锻造操作后的能量设定值、钢坯几何形状变化和变形场，有效解决了该问题。它利用 Bézier 曲线以参数方式捕获坯料几何形状，并利用 POD 进行简洁的变形场表示。凭借使用 FORGE® 软件进行的 60 次预测数值模拟的 36,000 个条目的大量数据库，代理模型使用多层感知器人工神经网络 (MLP ANN) 进行训练，该网络具有跨 3 个隐藏层的 300 个神经元，并使用 Python TensorFlow 框架内的 Keras API 。针对实验和数值数据的模型验证强调了其在预测能量设定点、几何变化和变形场方面的精度。这一进步有可能增强实时过程控制和优化，促进过程数字孪生的开发。