Lacking the understanding of the first principles leads to the apparent black box attributes of complex industrial processes. How to understand complex industrial processes from data and guiding industrial decision-making has become an urgent problem to solve. However, the existing data-driven models are also black boxes, focusing only on the correlation relationships between data without reflecting causal relationships. Therefore, this study addresses the challenge of double black boxes in complex industrial decision-making, proposing a research framework of "causal analysis → performance prediction → process optimization". Firstly, nonparametric copula entropy, network deconvolution , and information geometric causal inference are integrated to construct the causal relations network. Also, the observability and controllability of complex industrial processes are analyzed to provide valuable insights for improving the dataset. Then, drawing inspiration from the transformational machine learning idea, an explainable predictive model is constructed for predicting key performance indicators . Lastly, taking this predictive model as the process surrogate model , the optimal process parameters are solved using the particle swarm optimization algorithm. Moreover, the dataset of 16600 samples from a real-world injection molding process is used for application validation. The research results show that by reconstructing the causal relations network from data, the proposed framework can support the analysis, prediction, and optimization of complex industrial processes, achieving the decision-making goals of safety, robustness, improving quality and efficiency.

Research on the health diagnosis of mechanical equipment has developed unprecedentedly in recent years, and a large number of diagnostic solutions have considerably improved the stability of mechanical equipment in industrial production. However, such satisfactory diagnostic performance relies on a large number of data samples, which are frequently difficult to obtain in real industrial scenarios. The traditional strategy of data sharing is no longer advisable due to the potential conflict of interest among users. A federated transfer learning scheme is proposed to alleviate the data island problem in industrial production while protecting data privacy. This solution adopts a distributed structure, which includes local model training and global model update. A differential training scheme is proposed to enhance the domain adaptability of the local model. The central server evaluates the contribution ability of each local model to the target task. It also weights and aggregates each client model on the basis of parameter importance ranking in the form of model fusion. The target task of the experiment is performed on two sets of bearing datasets. By comparing with other diagnostic methods, a conclusion can be drawn that the proposed scheme provides a promising federated learning method while protecting client data privacy.

The digital economy is a new economic form taking data as an important production factor and digital and intelligent technology as a driving force for transformation. The core idea is to extract and fuse the knowledge implicit in data and transform it into intelligence to drive the transformation of traditional manufacturing industries, and one of its key technologies is multi-modal data fusion. In this paper, an improved MPGA-ACO-BP algorithm is proposed, and combined with an improved entropy-weighted TOPSIS method comprehensive evaluation system, which effectively solves the problem of “data scale inconsistency” between modal data leading to difficult model fusion and fusion accuracy. Finally, the validity of the theory and methods of this paper are verified using the example of multi-modal data fusion tool wear prediction in an intelligence workshop. By distilling the corresponding evaluation metrics inductively, the improved comprehensive evaluation system in this paper can also be extended to different production control scenarios to provide them with the corresponding integration information, which has a certain practical value.