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Hybrid energy supply systems are widely utilized in modern manufacturing processes, where accurately predicting energy consumption is essential not only for managing productivity but also for driving sustainable development. Effective energy management is a cornerstone of sustainable manufacturing, reducing waste and enhancing efficiency. However, conventional studies often focus solely on predicting single types of energy consumption and overlook the integration of physical laws and information, which are essential for a comprehensive understanding of energy dynamics. In this context, this paper introduces a multi-task physics-informed multi-gate mixture-of-experts (pi-MMoE) model that not only considers multiple forms of energy consumption but also incorporates physical principles through the integration of physical information and multi-task modeling. Specifically, a detailed analysis of manufacturing processes and energy patterns is first conducted to study various energy types and extract relevant physical laws. Next, using industry insights and thermodynamic principles, key equations for energy balance and conversion are derived to create a physics-based loss function for model training. Finally, the pi-MMoE model framework is constructed, featuring multi-expert networks and gating mechanisms to balance cross-task knowledge sharing and expert learning. In a case study of a textile factory, the pi-MMoE model reduced electricity and steam prediction errors by 14.28% and 27.27%, respectively, outperforming traditional deep learning methods. This demonstrates that the model can improve prediction performance, providing a novel approach to intelligent energy management and promoting sustainable development in manufacturing. [ABSTRACT FROM AUTHOR]

Graphite/polymer composites are brittle materials that are prone to producing cracks and concavities on machined surfaces, and their surface quality shows greater randomness. This work aims to overcome the large fluctuations in the machined surface quality of graphite/polymer composites, realize the prediction of machined surface roughness under different machining conditions and optimize the process parameters. A graphite/polymer composite material was cut orthogonally using different machining parameters, and the machined surface roughness of the cut samples was measured by a noncontact surface profiler to obtain training samples for Artificial Neural Network (ANN). In this study, a trained radial basis function neural network was used to predict the machined surface roughness, and the prediction accuracy was more than 93%. A Genetic Algorithm (GA) was used to optimize the established ANN, and then grey relational analysis was used to compare the accuracy of the GA optimization results. The ANN prediction after GA optimization showed that the lowest machined surface roughness of the graphite/polymer composites was 1.81 μm, and the corresponding optimal cutting speed, cutting depth, tool rake angle, and rounded edge radius were 11.2 m/min, 0.1 mm, 6.85°, and 11.16 μm, respectively. A verification experiment showed that the lowest machined surface roughness was obtained when the above process parameters were selected, which was only 1.95 μm, and the prediction error of the ANN was approximately 7%. The combination of a GA and an ANN can accurately predict the surface roughness of graphite/polymer composite materials and optimize the process parameters. [ABSTRACT FROM AUTHOR]