This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

A new spindle error measurement system has been developed in this paper. It employs a design development rotational fixture with a built-in laser diode and four batteries to replace a precision reference master ball or cylinder used in the traditional method. Two measuring devices with two position sensitive detectors (one is designed for the measurement of the compound X-axis and Y-axis errors and the other is designed with a lens for the measurement of the tilt angular errors) are fixed on the machine table to detect the laser point position from the laser diode in the rotational fixture. When the spindle rotates, the spindle error changes the direction of the laser beam. The laser beam is then divided into two separated beams by a beam splitter. The two separated beams are projected onto the two measuring devices and are detected by two position sensitive detectors, respectively. Thus, the compound motion errors and the tilt angular errors of the spindle can be obtained. Theoretical analysis and experimental tests are presented in this paper to separate the compound errors into two radial errors and tilt angular errors. This system is proposed as a new instrument and method for spindle metrology.

Linear laser encoders have been widely used for precision positioning control of a linear stage. We develop a five-degrees-of-freedom (5-DOF) laser linear encoder to simultaneously measure the position, straightness, pitch, roll, and yaw errors along one moving axis. This study integrates the circular polarized interferometric technique with the three-dimensional diffracted ray-tracing method to develop a novel laser encoder with 5-DOF. The phases encoded within the +1 and -1 order diffraction lights reflected from the diffraction grating are decoded by the circular polarized interferometric technique to measure the linear displacement when the diffraction grating moves. The three-dimensional diffracted ray tracing of the +1- and -1-order diffraction lights induced by the motion errors of the moved grating were analyzed to calculate the other motion errors based on the detection of light spots on two quadrant photodiode detectors. The period of the grating is 0.83 microm and the experimental results show that the measurement accuracy was better than +/-0.3 microm/+/-41 microm for straightness, +/-1 arc sec/+/-215 arc sec for angular error components, and +/-160 nm/2 mm for linear displacement.

In recent years, as there is demand for smart mobile phones with touch panels, the alignment/compensation system of alignment stage with vision servo control has also increased. Due to the fact that the traditional stacked-typeXYθstage has cumulative errors of assembly and it is heavy, it has been gradually replaced by the coplanar stage characterized by three actuators on the same plane with three degrees of freedom. The simplest image alignment mode uses two cameras as the equipments for feedback control, and the work piece is placed on the working stage. The work piece is usually engraved/marked. After the cameras capture images and when the position of the mark in the camera is obtained by image processing, the mark can be moved to the designated position in the camera by moving the stage and using alignment algorithm. This study used a coplanarXXYstage with 1 μm positioning resolution. Due to the fact that the resolution of the camera is about 3.75 μm per pixel, thus a subpixel technology is used, and the linear and angular alignment repeatability of the alignment system can achieve 1 μm and 5 arcsec, respectively. The visual servo motion control for alignment motion is completed within 1 second using the coplanarXXYstage.

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