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Thermal error modeling (TEM) stands as a pivotal factor in maintaining the machining accuracy of electric spindles. Deep learning (DL) techniques have shown promising potential in this area, however, they face formidable challenges including domain shift issue between training and testing datasets, as well as scarce samples due to the dynamic nature of variable working conditions. Deep transfer learning (DTL) emerges as an encouraging tool, leveraging knowledge obtained from the source domain to elevate learning outcomes within the target domain, notably addressing complicated cross-domain modeling predicaments. Nonetheless, prevailing research largely focuses on enhancing the prediction accuracy within the specific working condition, inadvertently sidestepping the problems of domain shift and scarce sample modeling. To address these issues, a novel weakly supervised adversarial network (WSAN) is proposed for cross-domain TEM with scarce samples. Firstly, the multi-scale convolutional neural network (MSCNN) is constructed to adeptly extract predictive information from multi-sensor data, effectively capturing the time-series patterns embedded within. Furthermore, the adversarial training technique is adopted to address distribution discrepancies that often manifest between distinct domains. To overcome the challenge of scarce labeled samples in the target domain, a weakly supervised learning strategy is employed to deftly control the gradient within the target domain, significantly enhancing the efficacy of positive transfer. The effectiveness and superiority of the proposed method are assessed through extensive experiments using datasets from variable working conditions. The experimental results demonstrate that the proposed method achieves satisfactory performance and outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The rapid development of advanced technologies provides futuristic option to enhance product value with digital services, which the concept of Smart Product-Service System (SPSS) was proposed. SPSS focuses on usage data, the whole lifecycle, and the iterative optimisation. A large amount of data and context information can be collected during the usage stage, which can be used to elicit requirements to guide further design. How to realise the requirement elicitation with cognitive intelligence based on usage data is the key step for design optimisation. Therefore, this research proposes a cognitive intelligence-enabled requirements elicitation framework to help designers promote the design optimisation process. First, the digital twin-driven data collection architecture of SPSS is proposed by considering multi-sources and multi-stages. Second, the behaviour of SPSS is modelled based on Auto-Encoder which is constructed using deep learning to cognise SPSS performance. Third, the corresponding behaviour model is interpreted based on Shapley Addictive exPlanations (SHAP) to discover key factors with cognitive intelligence. Finally, the domain knowledge graph is used to generate the optimisation requirements for further design. To demonstrate the feasibility and advantages of the proposed framework, this study adopts the case study of wind turbines for illustration and verification. [ABSTRACT FROM AUTHOR]

With the increasing demand for automotive glass, improving the efficiency of automotive glass manufacturing systems can make full use of production resources and reduce the waste of natural and social resources. Therefore, this work aims to provide an efficient method for a real-world two-stage hybrid flow shop scheduling problem with small batches in an automotive glass manufacturing system. For the investigated problem, there is a significant setup time at the first stage, the second stage is served by machines that should not be interrupted, and each batch has a due date. Such constraints make this scheduling problem challenging. To solve this problem, a mixed integer linear programming model is established. Also, two properties and three theorems are given to enhance the problem-solving process. Subsequently, an efficient genetic algorithm is carefully designed to solve large-scale problems by considering the properties of the system. Meanwhile, an improvement scheme is proposed to decrease the running time of the algorithm, and experimental results show that this scheme can reduce the running time by 280 s at most from the average results of different scale problems. Finally, extensive experiments are carried out and a real-world case is solved to demonstrate the efficiency and effectiveness of the designed genetic algorithm. Also, the Taguchi method is adopted to tune the parameters for the designed algorithm. [ABSTRACT FROM AUTHOR]