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The wide use of machining processes has imposed a large pressure on environment due to energy consumption and related carbon emissions. The total power required in machining include power consumed by the machine before it starts cutting and power consumed to remove material from workpiece. Accurate prediction of energy consumption in machining is the basis for energy reduction. This paper investigates the prediction accuracy of the material removal power in turning processes, which could vary a lot due to different methods used for prediction. Three methods, namely the specific energy based method, cutting force based method and exponential function based method are considered together with model coefficients obtained from literatures and experiments. The methods have been applied to a cylindrical turning of three types of workpiece materials (carbon steel, aluminum and ductile iron). Methods with model coefficients obtained from experiments could achieve a higher prediction accuracy than those from literatures, which can be explained by the inability of the coefficients from literatures to match the specific machining conditions. When the coefficients are obtained from literatures, the prediction accuracy is largely dependent on the sources of coefficients and there is no definitive dominance of one approach over another. With model coefficients from experiments, the cutting force based model achieves the best accuracy, followed by 2 the exponential function based method and specific energy based method. Furthermore, the power prediction methods can be used in process design stage to support energy consumption reduction of a machining process.

and tool change plan will vary based on the different PSFP. This paper firstly aims to un-6 derstand the relationship between the PSFP and the energy consumption of tool change 7 and tool path during the feature transitions. Then, a model is introduced for the single ob-8 jective optimisation problem that minimises the energy consumption of machine tools 9 during the feature transitions which include all the tool path and tool change operations. 10Finally, optimisation approaches including depth-first search and genetic algorithm are 11 modified and applied to find the optimal PSFP which results in the minimisation of the 12 energy consumption of feature transitions (EFT). In the case study, the optimal and near-13 optimal sequences of features, in terms of the minimum EFT, of a 15 features part which 14 is processed by a machining centre have been found. The optimal PSFP achieves a 15

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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.