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During a long-term structural health monitoring (SHM), large-scale machine tool structural response observed from various sensors show obvious big data characteristics. However, serious "data island" issues existing in traditional SHM inevitably limit the efficiencies of sensor data analysis. Besides, most existing identification methods for these diverse and complex signal data are manual methods, which are extremely time-consuming and inefficient. In this paper, a novel method based on knowledge graph (KG) was proposed to deal with the fine-grained domain knowledge modeling and multi-source sensor data integration problems in the field of machine tool SHM. With the help of KG-based querying and reasoning, it is more intelligent and convenient to retrieve and analyze sensor data. To verify the effectiveness of the proposed method, a long-term condition monitoring experiment was conducted on a CNC machine tool in an automobile factory. The dynamic properties of the machine tool structure were automatically identified based on KG, and a condition monitoring indicator based on the similarity of dynamic properties was applied to monitor the health condition of the machine tool. The final result showed the effectiveness of the KG for health monitoring of the machine tool structure. [ABSTRACT FROM AUTHOR]

In ultra-precision flying cutting (UPFC), a vibration is a unique dynamic response due to periodic intermittent excitation of cutting forces, which has a significant impact on surface generation. In UPFC, cutting forces act on the machine tool system intermittently and periodically, which may cause a self-excited vibration. This self-excited vibration will cause chatter, which maybe occurs during machining operations and even catastrophically degrades surface quality and limits productivity. This study has been motivated by the fact that chatter occurrence is generally in relation to the tool wear, which has seldom been investigated comprehensively in literature. In this paper, a chatter model for UPFC that can account for the effects of tool wear and process damping was presented. A number of experiments with different machining conditions were conducted to investigate the effect of the tool wear on the chatter stability. These experiments proved that the stability lobe is closely related to tool wear and with the tool wear increase, which increases the tendency for chatter in UPFC. Finally, it was found that increasing the tool clearance angle and reducing the spindle speed can improve the stability limit. [ABSTRACT FROM AUTHOR]

The dynamics of the machine tool structure are significant in high-precision machining and are meaningful to be evaluated. This paper proposes a new approach based on the modal mass distribution matrix to evaluate the dynamics and identify the weak components of machine tool structure and then to investigate the various components' effects on the machining process in different frequency bands. First, the approach is developed and verified by a designed mass-spring model whose weak components are known. Then, the proposed approach is used to identify the weak components of a machine tool structure whose modal parameters are obtained by experimental modal analysis. The result shows that the same component presents different weakness levels at different modes and that the various components of the structure present different weakness levels at the same mode. Finally, a cutting experiment is then conducted to verify the identification result. The result meets the former weak components' identification result well. [ABSTRACT FROM AUTHOR]

• Assembly error transferred to carriage of a translational axis. • Volumetric error of a horizontal machine tool linked with carriage. • Combined error sensitivity for effects of assembly geometric error. • Decomposition and measurement on translational axis as base, slider and carriage. The geometric errors of a translational axis are important factors influencing the volumetric error of machine tools. Error relationship in the assembly process of a translational axis quantitatively remains an open problem. In the paper, the slide guideway, forming the translational axis, has been decomposed into three key parts as the base, slider and carriage. New error transfer relationship model has been proposed in the assembly process. Volumetric error model of the horizontal machine tool is established and influence of the assembly error parameter on the volumetric error is analyzed with the two error models. Measurement experiments for the slide guideway has been carried out and the error transfer model has been verified. The proposed error transfer relationship model, as well as the error effects analysis method, provides important theoretical guide basis to optimize manufacture and assembly of a translational axis in a scientific and reasonable way. [ABSTRACT FROM AUTHOR]

Abstract: Environmental temperature has an enormous influence on large machine tools with regards to thermal deformation, which is different from the effect on ordinary-sized machine tools. The thermal deformation has hysteresis effects due to environmental temperature, and the hysteresis time fluctuates with seasonal weather. This paper focused on the hysteresis nonlinear characteristic, analyzing the thermal effect caused by external heat sources. Fourier synthesis, time series analysis and the Newton cooling law were combined to build a time-varying analytical model between environmental temperature and the corresponding thermal error for a large machine tool. A multiple linear regression model based on the least squares principle was used to model the internal heat source effects simultaneously. The two models were united to make up a synthetic thermal error prediction model called the environmental temperature consideration prediction model (ETCP model). A series of experiments were performed using a large gantry type machine tool to verify the accuracy and efficiency of the predicted model under random environments, random times and random machining conditions throughout an entire year. The proposed model showed high robustness and universality, with over 85% thermal error, with up to 0.2mm was predicted. The mathematical model was easily integrated into the NC system and could greatly reduce the thermal error of large machine tools under ordinary workshop conditions, especially for long-period cycle machining. [Copyright &y& Elsevier]

• A novel graph-based semi-supervised method is proposed to predict position- and speed-dependent tool tip dynamics with limited labeled data. • To avoid the over-fitting occurrence, the conventional GCN is extended by stacking a transposed GCN. • The accuracy of proposed method for predicting the milling stability is validated through chatter tests. The tool tip frequency response function (FRF), as one of the essential inputs to calculate the stability lobe diagram (SLD), varies with the position and speed of changes in the moving components within the machine tool work volume. How to predict the position- and speed-dependent tool tip dynamics accurately has become one of the most challenging tasks in obtaining SLDs while avoiding chatter. However, traditional finite element analyses or kinematic modelling-based methods are costly and time consuming. Data-driven machine learning methods require large amounts of labeled data to train a model, but labeled industrial data are limited and extremely valuable in real manufacturing industries. To minimize experimentation, an improved semi-supervised graph convolutional network (GCN) is proposed to predict position- and speed- dependent tool tip dynamics with limited labeled data. First, the inverse stability solution is applied to identify dominant modal parameters under cutting conditions to obtain labeled samples. Subsequently, both the limited labeled samples and large amounts of unlabeled samples are converted into graph data to train a GCN regression model integrated with a multilayer perceptron. To avoid overfitting under limited data conditions, the conventional GCN is extended by stacking a transposed GCN, which is utilized to reconstruct the initial node features. The results demonstrate an improved prediction performance by adding the unsupervised reconstruction error into the loss function. Compared with other machine learning methods, our proposed method has superior performance in predicting tool tip dynamics with only 20% of the labeled samples. Finally, the SLDs were reconstructed with predicted FRFs, and the accuracy of the SLDs was validated through chatter tests. [ABSTRACT FROM AUTHOR]