Explore the vast range of titles available.

The widespread use and continuous improvements of machine tools have had a significant impact on productivity in manufacturing industry ever since the Industrial Revolution. At the dawn of the new era of industrialization, the need to advance machine tools to a new level that accords to the concept of Industrie 4.0 has to be recognised and addressed. Muck like the different stages of industrialisation, machine tools have also gone through different stages of technological advancements, i.e., Machine Tool 1.0, Machine Tool 2.0 and Machine Tool 3.0. Industrie 4.0 pleads for a new generation of machines—Machine Tool 4.0. This paper describes some of the key and desired characteristics of Machine Tool 4.0 such as Cyber-physical Machine Tools, vertically and horizontally integrated machine tools and more intelligent, autonomous and safer machine tools.

Thermal error of machine tools has a huge influence on the accuracy of the workpiece. However, the nonlinearity of the thermal error limits the accuracy and robustness of the prediction model. With the rapid advancement in artificial intelligence, this paper presents a novel thermal error modeling method based on random forest. The model’s hyper-parameters are easy to be optimized by grid searching method integrating with cross validation. The temperature features are measured as the model input. Based on the out-of-bag data generated during modeling process, the proposed model itself can simultaneously evaluate the temperature feature importance through comparing the decrease in model’s the prediction accuracy after randomly shuffling the value of the target feature. Moreover, to enhance the model performance and reduce the measurement and computational cost, the method of selecting key temperature points are presented to exclude the redundant features through iteratively eliminating the least important feature and comparing the prediction accuracy under different feature combinations. Furthermore, the hysteresis effect between temperature and deformation is also considered. The method of determining the time lag is proposed through permuting the original time series of the target feature while keeping the remainder constant and comparing the resultant relative importance. A thermal error experiment validates the accuracy and robustness of the proposed model which can continuously maintain the prediction accuracy of over 90% in spite of varying operation conditions. Compared to conventional machine learning methods, the proposed model requires less training data, enables faster and more intuitive parameter tuning, achieves higher prediction accuracy, and has stronger robustness.

With the development of manufacturing, machining data applications are becoming a key technological component of enhancing the intelligence of manufacturing. The new generation of machine tools should be digitalized, highly efficient, network-accessible and intelligent. An intelligent machine tool (IMT) driven by the digital twin provides a superior solution for the development of intelligent manufacturing. In this paper, a real-time machining data application and service based on IMT digital twin is presented. Multisensor fusion technology is adopted for real-time data acquisition and processing. Data transmission and storage are completed using the MTConnect protocol and components. Multiple forms of HMIs and applications are developed for data visualization and analysis in digital twin, including the machining trajectory, machining status and energy consumption. An IMT digital twin model is established with the aim of further data analysis and optimization, such as the machine tool dynamics, contour error estimation and compensation. Examples of the IMT digital twin application are presented to prove that the development method of the IMT digital twin is effective and feasible. The perspective development of machining data analysis and service is also discussed.

To improve the spindle thermal error prediction accuracy, the least absolute shrinkage and selection operator (LASSO) is used to directly select the temperature-sensitive point subset to guarantee the prediction performance of the thermal error model built by least squares support vector machines (LS-SVM). Taking a horizontal machining center as a test stand, the thermal error experiments with different spindle speed states are carried out. Then the temperature-sensitive points are selected using LASSO. The number of temperature-sensitive points is reduced from 20 to 7. Afterward, the thermal error model is designed by LS-SVM. The prediction performance and generalization performance of the thermal error model are compared with another two thermal error models using gray model (GM) and multiple linear regression (MLR), respectively. The comparison results indicate that the thermal error model derived from LS-SVM shows better prediction performance and generalization performance than those derived from GM and MLR with the highest prediction accuracy increasing about 74.6 and 54.3%, respectively. Thus, the feasibility and effectiveness of the proposed spindle thermal error robust modeling method are validated.

High-speed motorized spindles play a vital role in manufacturing. In the process of high-speed rotation of the motorized spindle, the built-in motor and the high-speed running bearing will produce a lot of heat, and its heating will seriously affect the machining accuracy of the machine tool and the life of the spindle assembly. Therefore, the research on the thermal characteristics and cooling technology of the high-speed motorized spindle is an important measure to reduce the fire and explosion of the machine tool, improve the stability of the spindle and the safety of the machine tool, and is of great significance to ensure the stable operation of the high-speed motorized spindle. Based on the heat source, this paper puts forward two heating ways of high-speed motorized spindle, including bearing heating and motor heating, and probes into the thermal characteristic mechanism of high-speed motorized spindle and the modeling technology of thermal error compensation of high-speed motorized spindle in detail, and summarizes the bearing cooling technology, the principle, scope of application, advantages and disadvantages of motor cooling technology, and put forward corresponding improvement measures. And the future development of high-speed motorized spindle is prospected, and some new ideas are provided for the improvement of its technology.

Tool wear of machine tools not only directly affects the processing quality, but also leads to the life of processing equipment and production costs. It is of great significance to correctly identify, classify and predict the state of tool wear. In this paper, through the collection of machine tool operation data, using machine learning modeling, using the model to identify the tool wear state, and then predict and classify the tool wear state, using the classification results to determine whether the tool can continue to use. After simulation verification, the results show that the model can identify and predict the wear state more realistically, and has strong practicability.

As part of the fourth industrial revolution, the movement to apply various enabling technologies under the name of Industry 4.0 is being promoted worldwide. Because of the wide range of applications and the capacity of manufacturing workpieces flexibly, machine tools are regarded as essential industrial elements. Hence, much research has been concerned with applying various enabling technologies such as cyber-physical systems to machine tools. To realize a machine tool suitable for Industry 4.0, development should be done in a systematic manner rather than the ad-hoc application of enabling technologies. In this paper, we propose a functional architecture for the Industry 4.0 version of machine tools, namely smart machine tool system. To reflect the voices of various stakeholders, stakeholder requirements are identified and transformed into design considerations. The design considerations are incorporated into the conceptual model and functional modeling, both of which are used to derive the functional architecture. The implementation procedure and an illustrative case study are presented for the application of the functional architecture.

As a typical manufacturing equipment, CNC machine tool (CNCMT), which is the mother machine of industry, plays an important role in the new trend of smart manufacturing. As the requirement of smart manufacturing, the abilities of its self-sensing, self-prediction and self-maintenance are necessary. In order to make CNCMT become more intelligent, a research about Digital twin (DT) for CNCMT is conducted. In this research, a multi-domain unified modeling method of DT is established, a mapping strategy between physical space and digital space is explored, and an autonomous strategy of DT is proposed. These methods can optimize the running mode, reduce the sudden failure probability and improve the stability of CNCMT. Finally, this paper provides a demonstration of DT model building and using strategy in fault prediction and diagnosis for CNC milling machine tool.

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force (Fy) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of Fy signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones.

This paper presents a survey of the applications of prognostics and health management maintenance strategy to machine tools. A complete perspective on this Industry 4.0 cutting-edge maintenance policy, through the analysis of all its preliminary phases, is given as an introduction. Then, attention is given to prognostics, whose different approaches are briefly classified and explained, pointing out their advantages and shortcomings. After that, all the works on prognostics of machine tools and their main subsystem are reviewed, highlighting current open research areas for improvement.