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• 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]

• An innovative partly explainable deep learning model has been proposed for tool wear intelligent monitoring. • The proposed Conv-PhyFormer model is a novel model driven by both physical and data. • The embedded hard physical constraint and soft physical constraint in the model make the model partially interpretable and credible. • The proposed model shows a significantly superior high prediction accuracy and robustness. • The proposed model has superior ability to learn global and local sensitive tool wear features. With the trend of lightweight in the field of intelligent electric vehicles and 3C, the demand for high precision machining of aluminum alloy parts is growing. And tool condition monitoring (TCM) is very important for quality control of parts, so intelligent high-accuracy wear prediction of aluminum alloy high precision machining tools has great industrial application value at present and in the future. This paper presents a novel TCM model (Conv-PhyFormer) of Transformer with physics informed. The model has excellent ability to capture short-term and long-term dependencies from nonlinear cutting time series data when there are few training samples. The embedded hard physical constraint and soft physical constraint in the model make the model partially interpretable. Soft physical constraint in the form of one-dimensional causal convolution can help the proposed model better learn the local context. Hard physical constraint in the form of the mathematical equation representing cutting physical knowledge are embedded, thus the model does not need to learn this knowledge from time series data from scratch. A large number of analysis results of aluminum alloy machining experimental data show that the proposed Conv-PhyFormer has significantly superior prediction accuracy and robustness compared with the current three popular deep learning models for TCM. Embedded soft and hard physical constraints can significantly reduce the training epochs of Transformer prediction model. [ABSTRACT FROM AUTHOR]

Unlabeled time series signals collected during manufacturing typically have low value density and must be labeled and intercepted according to the specific application scenario. During variable-parameter milling, particularly high-precision machining, machining parameters vary, and associated discrepancies in vibration signals are small. In this scenario, signal features that are extracted by hand or via deep learning methods cannot typically distinguish machining states via classification models. To solve this problem, a sequence labeling model developed using a stacked bidirectional long short-term memory network with a conditional random field layer (stacked-BiLSTM-CRF) is proposed in this study to automatically label and intercept vibration signals. The stacked BiLSTM receives the shallow features obtained by the short-time Fourier transform of the vibration signals and then outputs the extracted deep features to capture the before and after dependence of the signals. The stacked BiLSTM is then extended by stacking a CRF layer to explicitly model the dependence of signal labels. In a more accurate labeling scenario, the fast low-cost online semantic segmentation algorithm (FLOSS) is used to acquire more fine-grained signal boundary locations after obtaining the frame-level signal label using the stacked BiLSTM-CRF model. In addition, to evaluate model performance, a novel evaluation index for signal labeling is proposed. The feasibility and effectiveness of the proposed method are verified using the vibration signals collected from variable parameter cutting experiments, and results show that the proposed model achieves the best labeling performance of tested methods in nearly all scenarios. [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 weakly supervised defect segmentation method that does not require pixel-level annotations of defects for training is proposed based on the dynamic templates generated by an improved Cycle-GAN, which can achieve significantly higher performance than other weakly supervised methods and have competitive performance with supervised methods. • A novel defect attention module is proposed in the discriminator of Cycle-GAN to better eliminate defects with weak signals, such as small region and low contrast, for more accurate template images. • A defect consistency loss is proposed by adding SSIM to the L1 loss based on grayscale and texture, respectively, for better modeling the inner structure of defects. • The proposed method has been successfully applied at an industrial site for commutator surface defect detection and has exhibited excellent detection accuracy and significantly reduced manual labeling costs. • The proposed method can also be employed as a semiautomatic annotation tool combined with active learning. Surface defect segmentation is very important for the quality inspection of industrial production and is an important pattern recognition problem. Although deep learning (DL) has achieved remarkable results in surface defect segmentation, most of these results have been obtained by using massive images with pixel-level annotations, which are difficult to obtain at industrial sites. This paper proposes a weakly supervised defect segmentation method based on the dynamic templates generated by an improved cycle-consistent generative adversarial network (CycleGAN) trained by image-level annotations. To generate better templates for defects with weak signals, we propose a defect attention module by applying the defect residual for the discriminator to strengthen the elimination of defect regions and suppress changes in the background. A defect cycle-consistent loss is designed by adding structural similarity (SSIM) to the original L1 loss to include the grayscale and structural features; the proposed loss can better model the inner structure of defects. After obtaining the defect-free template, a defect segmentation map can easily be obtained through a simple image comparison and threshold segmentation. Experiments show that the proposed method is both efficient and effective, significantly outperforms other weakly supervised methods, and achieves performance that is comparable or even superior to that of supervised methods on three industrial datasets (intersection over union (IoU) on the DAGM 2007, KSD and CCSD datasets of 78.28%, 59.43%,and 68.83%, respectively). The proposed method can also be employed as a semiautomatic annotation tool combined with active learning. [ABSTRACT FROM AUTHOR]