Abstract Fault diagnosis of rolling bearings using imbalance data has always been a particularly challenging problem. The proposed digital twin technique, which complements the unbalanced dataset, provides a completely new approach that will greatly improve the effectiveness of the diagnosis. In this paper, A digital twin method based on feature embedding in a diffusion model to generate fault data with more accurate feature representation was proposed and applied to fault diagnosis. First, A bearing mechanism model is constructed from the dynamics to enable the generation of raw one-dimensional signals with fault characteristics. Second, diffusion models based on variational Autoencoders (VAE) are constructed, aiming at the application of diffusion models to low-dimensional signals to form a stable signal generative approach. Finally, A feature embedding method in a diffusion model is proposed to enable the model to be generated to include more accurate feature representations, thus enhancing the datasets and improving fault diagnosis performance. In addition, comparison experiments with digital twins based on Generative Adversarial Networks (GANs) show that the method is more accurate and robust.
Abstract Optimizing the energy consumption (EC) of industrial robots plays a crucial role in promoting their large-scale application to support the realization of Industry 4.0. Notably, robotic grinding is particularly energy-intensive, attributed to the complex coupling between robot dynamics and the continuous contact forces required for surface finishing. Furthermore, given the intricate nature of the EC process, it is challenging to establish a precise numerical relationship between robotic process parameters and EC relying exclusively on physical mechanisms. Data-driven models require large datasets and often fail to achieve high accuracy under small-sample conditions. To address this, we propose a physics-guided data-driven EC (EC-PGDD) prediction model. The heat conduction equation is employed to characterize the underlying relationship between temperature and EC, and a regularization term within the loss function of the temperature prediction model—by embedding differential operators—imposes a smoothness prior, enhancing extrapolation in sparse data regions. Based on the physical information provided by this equation, EC-PGDD integrates predicted temperature with process parameters, encoding richer information than purely data-driven approaches. To validate the effectiveness of the proposed model and provide targeted strategies for process parameter optimization, validation experiments on a battery end-plate robotic grinding unit, utilizing the Nondominated Sorting Genetic Algorithm II (NSGA-II) for multiobjective optimization, demonstrated a 7.67% reduction in energy consumption and a 0.23% reduction in execution time.
Abstract To avoid the blind spots and brittleness of imperatively simulating a physical system, modelers often turn to declarative methods that can autonomously execute a model. Such solvers rely on a model structure that allows procedures for transforming inputs to outputs to be automatically discovered. However, the model structures used for traditional declarative solvers are insular, in that they are often isolated to specific modeling domain. Furthermore, these specific model types are unable to be understood by the advanced computing applications required to simulate complex systems. Here, we provide a general purpose modeling framework that can integrate software functionalities into the declarative simulation of a model, offering for the first time the ability to define a multiphysics, multiscale system independent of its eventual simulation. This is accomplished by representing a system as a constraint hypergraph. System models are deconstructed into state variables and relationships in the graph. The the Application-Programmer Interfaces (APIs) of external tools are integrated into the model as intervariable functions. By encoding these functionalities in the graph, a solver is able to autonomously arrange these relations into executable simulation processes, enabling fully declarative simulation. This is demonstrated by integrating the capabilities of three software platforms together into a single model of a crankshaft from a piston engine: solid geometry (onshape), structural mechanics (ansys mechanical), and kinematic analysis (matlab). The result is a holistic modeling framework that allows for flexible simulation of a complex system, integrates directly with otherwise sequestered platforms, and reveals cross-cutting interactions between system elements.
Abstract In aircraft stringer assembly, flatness out-of-tolerance is caused by the cumulative propagation of multisource coupled deviations, and it is difficult to perceive this phenomenon dynamically. To address this problem, this study proposes a dynamic prediction method that integrates assembly deviation mechanisms and machine learning. First, based on screw theory, a full-process deviation propagation model for manipulator positioning–clamping–transplanting is established. This model quantifies the coupling effects of positioning errors, flexible clamping deformations, and inertial disturbances. On this basis, a physics-informed machine learning (PIML) framework is designed. The Lagrangian dynamic equation is embedded into the spatiotemporal graph convolutional network (ST-GCN) as a physical constraint. A dual-stream architecture of Attention-long short-term memory (LSTM) is combined to process multisensor time-series data, and an online Bayesian update mechanism is introduced to adapt to changes in working conditions. Finally, case verification is carried out on the fuselage assembly line of a certain type of civil aircraft. The results show that the root mean square error (RMSE) of the proposed method for stringer flatness deviation prediction is reduced to 0.21 mm, which is about 19% higher in accuracy than the traditional LSTM model. Moreover, this method can reduce the assembly out-of-tolerance rate by about 23%, providing a theoretical tool and technical support for high-precision aircraft assembly.
In the assembly process design, knowledge question-answering is a crucial scenario for promoting the sharing of knowledge resources and enhancing the process design accuracy and efficiency. Simultaneously, knowledge graph technology enables efficient semantic modeling of knowledge, allowing for more accurate capture of user semantics and intentions. This, in turn, enhances the accuracy and flexibility of knowledge question-answering. However, due to the high complexity and specialization of assembly processes, current knowledge graph question-answering for assembly processes still faces challenges, such as difficulty in understanding complex queries. In response to this, this article proposes a large language model (LLM)-enhanced knowledge graph multi-hop reasoning method for assembly process question-answering. This method decomposes the multi-hop knowledge graph question-answering task into three subtasks: LLM-tuning-based question-answering chain generation task, which transforms the question into one or more question-answering chains, multi-hop question-answering chain reasoning task, and LLM-based natural language answer generation task. Among them, a graph path-based multi-hop reasoning model for assembly processes is constructed for question-answering chain generation. This model employs a "core reasoning + attribute constraints" strategy and a task-oriented negative sample setting method to enable rapid and precise reasoning between the knowledge graph and question-answering chains. The effectiveness of the proposed method is validated through the comparative experiments with existing mature knowledge graph question-answering models. In the comparative experiments, three datasets were constructed for LLM fine-tuning and question-answering chain reasoning of multi-hop questions, and the performance index HITS@5 of 0.913 surpassed the existing mature Hete-MF model and could fully meet the process designer needs.
