During the powder compaction process, process parameters are required for product quality prediction. However, the inadequacy of compaction data leads to difficulties in constructing models for quality prediction. Meanwhile, the existing data generation methods can only generate required data partially, and fail to generate data for extreme operating conditions and difficult-to-measure quality parameters. To address this issue, a digital twin (DT) enhanced quality prediction method for powder compaction process is presented in this paper. First, a DT model of the powder compaction process with multiple dimensions is constructed and validated. Then, to solve the data inadequacy problem, data of process parameters are generated through an orthogonal experimental design, and are imported into the DT model to generate quality parameters, so as to obtain the virtual data. Finally, the quality prediction for the powder compaction process is achieved by the generative adversarial network-deep neural network (GAN-DNN) method. The effectiveness of the generated virtual data and the GAN-DNN method is verified through experimental comparison. On top of point-to-point validation, a quality prediction system applied in a powder compaction line is developed and implemented to demonstrate the end-to-end practicability of the proposed method.

中文翻译：

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粉末压制过程数字孪生增强质量预测方法

在粉末压制过程中，需要工艺参数来预测产品质量。然而，压实数据的不足导致了构建质量预测模型的困难。同时，现有的数据生成方法只能生成部分所需的数据，无法生成极端工况和难以测量的质量参数的数据。为了解决这个问题，本文提出了一种粉末压制过程的数字孪生（DT）增强质量预测方法。首先，构建并验证了多维粉末压制过程的DT模型。然后，针对数据不足的问题，通过正交实验设计生成工艺参数数据，并将其导入到DT模型中生成质量参数，从而获得虚拟数据。最后，通过生成对抗网络-深度神经网络（GAN-DNN）方法实现粉末压实过程的质量预测。通过实验对比验证了生成的虚拟数据和GAN-DNN方法的有效性。在点对点验证的基础上，开发并实现了应用于粉末压实生产线的质量预测系统，以证明该方法的端到端实用性。