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The cyber-physical data of equipment whole life cycle are interrelated and play an important role in decision-making for business activities. However, multi-stage cyber-physical data are numerous, heterogeneous, and massive; non-real-time information data and real-time physical data are challenging to correspond to each other because of the inconsistency in space-time and granularity. As a result, equipment multi-stage heterogeneous cyber-physical data are difficult to effectively integrate and apply. Therefore, multi-stage cyber-physical data fusion of equipment is a key problem to be solved in manufacturing. To the best of our knowledge, a practicable framework and method to effectively fuse and apply equipment multi-stage data are still missing. To overcome this gap, this study proposes a multidimensional and multilevel cyber-physical fusion method for equipment. First, a four-dimensional cyber-physical fusion framework based on time, data, model, and structure is put forward. Then, the four kinds of fusion, including data fusion, model fusion, knowledge fusion, and digital twin (DT) application fusion, are discussed detailedly. Besides, the five types of algorithms set to support the fusion process are given. Finally, equipment life cycle activity is regarded as a DT application process using dynamic association of data and models. Through cyber-physical fusion, the data of equipment multi- stages could be applied for different activities to solve problems by means of DT. As a primary verification of the feasibility of the proposed approach, a case study of metro vehicle performance evaluation has been carried out, and the results have well confirmed that the multi-stage cyber-physical fusion method is feasible. [ABSTRACT FROM AUTHOR]

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development. [ABSTRACT FROM AUTHOR]

The forming grinding process is a crucial method for helical flute grinding of the end mill and screw tap, and the grinding wheel profile is the key to ensure the precision of the helical flute, which is constrained by the grinding wheel posture. However, the existing methods have some deficiencies such as the invalidity or uneven wear problems. In this paper, an optimized calculation method of the grinding wheel profile for the forming grinding of the helical flute is proposed, including the suitable grinding posture correspondingly. Firstly, the parametric models of the helical cutting edge and the helical flute surface are established. Secondly, the contact condition between the grinding wheel and the flute is deduced to calculate the grinding wheel profile by the envelop theory. Thirdly, an optimization method for grinding wheel profile is proposed, which could predict of the solution interval, avoid the profile intersection, and improve wear resistance. Finally, the method was verified by a series of simulations and experiments, and the results show that the method could meet the grinding precision requirements and expand the application range of forming grinding technology in helical flute. [ABSTRACT FROM AUTHOR]

Crowd intelligence innovation (CII) is an open problem-solving model, typically performs on a crowdsourcing-based online innovation platform, which is highly dependent on the engagement and high-quality work of participants. On an online platform, many demanders publish tasks, and innovators submit schemes, which forms a complex CII system. To ensure such a CII system operates sustainably, it is critical for the platform operators to know the current system's health statuses before taking proper management actions. However, the large influx of participators with varied behaviors makes it extremely difficult to evaluate the health statuses of the system and capture its characteristics. Practicable method for quantitatively evaluating the health status of a CII system is still missing. Therefore, this study proposed a new quantitative health status evaluation method from an ecosystem perspective. First, the crowd intelligence innovation ecosystem (CIIE) was established by mapping the CII system onto the natural ecosystem based on ecological theory. Second, the health status of CIIE was described by its internal health status and external health status using a mixed method. The internal health status of the CIIE was defined and evaluated based on its health characteristics in terms of stability, ascendency, development capacity, and redundancy measurements using ecological network analysis. The external health status was evaluated using the ecological entropy index system. As the primary verification of the feasibility of our proposed approach, a case study of Zhubajie was conducted. These results confirmed the feasibility of our evaluation method. With this method, the health status of CIIEs could be accurately measured, which supported the management and organization the CII systems. Future work will focus on adapting suitable measures to improve the operation of CII. [ABSTRACT FROM AUTHOR]

• A consistency association is established among different conditions signals. • Tool structure and process parameters are introduced to the model structure design. • The model's generalization boundary in tool wear monitoring is studied. • The proposed method is generalized and interpretable on extensive experiments. Cross-conditions tool wear monitoring has a wide application prospect in manufacturing. However, the data distribution discrepancies caused by the inconsistency of process elements restrict the generalization of models under cross-conditions or even similar conditions. The existing methods based on diversity enhancement make it difficult to effectively establish the correlation between the source domain and target domains, which limits the improvement of model generalization. Therefore, this paper details the cause of data distribution discrepancies and proposes a discontinuous physical property-constrained single-source domain generalization network for milling tool wear monitoring. Firstly, a spatial attention mechanism is introduced to weight key signal segments adaptively. Secondly, the generation module is constrained by a standard sample and is used to generate diverse samples with physical properties. Thirdly, extensive recognition experiments on an open dataset and machining experiments with three distribution discrepancy levels were conducted to verify the effectiveness of the proposed method. Finally, feature visualizations provide consistency and interpretability. [ABSTRACT FROM AUTHOR]

This research paper presents an analytical mechanics model for studying the degraded failure mechanics of an adhesively bonded corrugated sandwich structure (ABCSS) subjected to three-point bending and hygrothermal ageing loads. The model incorporates a detailed stress state model that considers the stress distribution of the adhesive layer, including the adhesive fillet, to more accurately reflect the failure mechanics of ABCSS. The Fickian Diffusion model and a degradation coefficient are used to describe the characteristics of water diffusion and hygrothermal mechanical degradation for the adhesive joint in ABCSS. The hygrothermal ageing experiments are conducted by immersing the ABCSS into deionized water and 5 wt% NaCl water, followed by three-point bending tests after hygrothermal ageing. The proposed two-step finite element (FE) method is developed to simulate both moisture diffusion and three-point bending test. The presented FE method is validated through comparison with corresponding experimental results, then it is utilized to demonstrate that the proposed analytical model is accurate enough in initial failure modes and initial failure loads predictions. At last, the hygrothermal effects on degraded stress distribution and degraded failure performance are investigated through analytical method, and the degraded failure mechanics maps are constructed to further visually demonstrate failure mechanics with the aid of the analytical model. [ABSTRACT FROM AUTHOR]