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Production system flexibility has become an important factor to meet the needs of diversified markets in an ever-changing and turbulent market environment. Thus, this paper addresses the problems of flexibility evaluation for decision making by evaluation method driven by digital twin, which models, predicts, and evaluates the flexibility of a given production system design scheme (PSDS). Based on the definition and characteristic analysis of flexibility in the production system design phase, and combined with data modelling (static function model (SFM) and dynamic prediction model (DPM)) and data analysis (AHP-based and simulation-based analysis), the proposed framework evaluates system flexibility from the static and dynamic levels, helps enterprises quickly obtain the system's comprehensive flexibility evaluation index, realizing the rapid screening of the production system design scheme (PSDS). The successful application of the evaluation framework in case enterprise has fully proved the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

The tool tip vibration measurement has a great significance for online surface topography prediction and active vibration control. Many measurement methods have been developed. However, most of these methods are difficult to be applied in actual machining process for the following problems: the chips and cutting fluid will cause damage to the sensor or distort its measurement results and the sensor must be installed again when replacing the cutting tool. In order to overcome the above problems, this paper proposes an indirect measurement method: the vibration of the tool tip is obtained from the vibration of the measurement point on the turret. The relationship between the tool tip vibration and the turret measurement point vibration is obtained through impact tests. The vibration of the measurement point on the turret is measured via an accelerometer, which is easy to install. The measurement point is so far away from the cutting area and chip evacuation zone that the accelerometer and its measurement results will not be affected by the chips and cutting fluid. The accuracy of the measurement method is checked by comparing the calculated tool tip vibration calculated from the vibration of the turret measurement point to the directly measured results, which is measured via an accelerometer installed below the tool tip. The comparison results show that the calculated tool tip vibration at main frequencies as well as low frequencies has a high accuracy. Moreover, the effectiveness of the measurement method is verified through machined surface profile. [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]