Surface acoustic wave propagation in semiconductor systems is strongly influenced by coupled thermal, electromagnetic, and mechanical interactions, particularly under high-frequency operating conditions encountered in advanced microelectronic and sensing devices. Existing thermoelastic wave models generally neglect the simultaneous interaction of Hall current effects, rotational dynamics, temperature-dependent material behavior, and non-Fourier thermal relaxation, which limits their capability for accurately characterizing multiphysics wave phenomena in semiconductor media. This study investigates Rayleigh surface wave propagation in a rotating magneto-thermoelastic silicon semiconductor half-space by developing a unified multiphysics framework incorporating Hall current effects and a multi-dual-phase-lag heat conduction model with temperature-dependent material properties. The coupled governing equations were transformed into dimensionless form and analytically solved using normal-mode analysis to derive the secular equation governing Rayleigh-type surface waves. Numerical simulations were performed using experimentally validated silicon parameters to evaluate the phase velocity, attenuation coefficient, penetration depth, and specific heat loss under different thermal, electromagnetic, and rotational conditions. A variance-based global sensitivity analysis based on Sobol indices was additionally conducted to quantify the relative influence of the governing multiphysical parameters on wave behavior. The results showed that rotational effects increased phase velocity and penetration depth, whereas temperature-dependent thermal softening reduced wave propagation capability and enhanced attenuation. Hall current effects and magnetic field intensity exhibited competing influences on wave kinematics and damping characteristics. The sensitivity analysis revealed that electromagnetic parameters primarily governed wave kinematics, while the thermal softening parameter dominated thermodynamic energy dissipation behavior. Nearly uniform sensitivity distributions were observed for phase velocity and penetration depth, indicating strong multiphysical coupling among thermal, elastic, and electromagnetic fields within the semiconductor system. The results indicate that the proposed framework provides a physically consistent and quantitatively interpretable platform for analyzing coupled wave propagation phenomena in semiconductor engineering systems. The developed model offers practical guidance for the design and optimization of surface acoustic wave devices, semiconductor sensors, and thermo-electromagnetic microelectronic systems operating under complex coupled-field environments.
Industry 4.0 transforms modern manufacturing systems through the integration of cyber-physical systems, the Industrial Internet of Things, artificial intelligence (AI), machine learning (ML), and digital twin (DT) technologies. Autonomous industrial control remains a critical challenge in complex engineering environments because conventional control architectures often struggle to handle nonlinear dynamics, distributed decision-making, system uncertainties, and real-time operational variability. This review investigates the role of AI-, ML-, and DT-enabled autonomous control systems in improving adaptive intelligence, predictive capability, operational optimization, and resilient decision-making within smart industrial environments. A comprehensive technical review was conducted to examine recent developments in intelligent system modeling, predictive analytics, adaptive and self-learning control, real-time anomaly detection, multi-objective optimization, quality control, and energy-efficient industrial operations. The architectures and operational mechanisms of the AI–ML–DT-integrated control frameworks were analyzed from the perspective of complex cyber-physical industrial systems. The interrelationships among distributed sensing, intelligent data processing, virtual simulation, and autonomous control layers were also evaluated to identify current technological capabilities and implementation limitations. The analysis showed that the integration of AI, ML, and DT technologies significantly improved predictive maintenance performance, adaptive process control, fault diagnosis accuracy, operational flexibility, and energy optimization in Industry 4.0 environments. The reviewed studies demonstrated that DT-assisted virtual environments enabled safe real-time optimization and intelligent decision validation before physical deployment. The results also revealed that autonomous control architectures enhanced the resilience and self-adaptive capability of industrial systems operating under dynamic and uncertain conditions. However, several limitations were identified, including interoperability constraints, model synchronization challenges, computational complexity, cybersecurity risks, and scalability issues in distributed industrial networks. This study demonstrates that the convergence of AI, ML, and DT technologies establishes an important foundation for next-generation autonomous cyber-physical industrial systems. The proposed review provides a comprehensive engineering perspective for understanding intelligent industrial control architectures and offers valuable insights into the development of scalable, adaptive, and energy-efficient autonomous manufacturing systems for future Industry 4.0 applications.
Rotating machinery commonly operates under coupled mechanical and electrical excitations, where closely spaced vibration frequencies can generate complex dynamic responses and interfere with accurate fault diagnosis. The beating phenomenon represents a critical form of amplitude modulation in rotating systems and serves as a valuable diagnostic indicator for identifying resonance interactions, electromechanical coupling, and instability mechanisms in industrial equipment. This study investigates the dynamic characteristics of beating phenomena in industrial rotating machinery through analytical modeling, vibration signal analysis, and industrial case studies. A mathematical formulation based on sinusoidal superposition was developed to describe the interaction between adjacent frequency components and the resulting amplitude modulation behavior. Time-domain and frequency-domain analyses were performed to evaluate the relationship between beat frequency, modulation envelope, and vibration response characteristics. Two industrial case studies involving a centrifugal pump and a variable-frequency-drive-driven induction motor were examined using vibration monitoring data, fast Fourier transform (FFT) analysis, envelope analysis, and MATLAB-based numerical simulations. The results demonstrated that closely spaced frequency components generated periodic amplitude modulation and produced distinct beating patterns in both the time and frequency domains. In the pump system, the interaction between vibration components at 202.875 Hz and 202.785 Hz produced a measurable beat response that was strongly associated with unstable vibration behavior. In the variable-frequency-drive-driven motor, interference between the 2X and 2LF components was identified as the primary source of beating and abnormal vibration amplification. The implemented corrective actions, including the elimination of unintended current paths and the installation of an insulated bearing, significantly reduced vibration severity and restored stable operating conditions. The findings indicate that beating behavior is strongly associated with coupled electromechanical interactions and provides valuable diagnostic information for identifying closely spaced excitation sources, bearing degradation, and modulation-induced instabilities in rotating equipment. Furthermore, the combined application of FFT analysis, envelope analysis, and vibration condition monitoring enables the reliable identification of fault-related modulation effects and enhances diagnostic accuracy in complex industrial machinery. The proposed analytical and monitoring framework offers an effective approach for vibration-based condition monitoring, early fault detection, and reliability enhancement in complex industrial machinery systems.
Nonlinear plasma evolution in microgravity cannot be reliably characterized under terrestrial gravity because buoyancy-driven convection modifies or suppresses the intrinsic instability mechanisms. Consequently, the predictive design and safe operation of electromagnetically actuated plasma engineering systems require a unified theoretical framework capable of distinguishing gravity-independent behavior from phenomena that emerge only under microgravity conditions. A microgravity nonlinear plasma platform was therefore established as a multi-physics governance framework that defines the physical and mathematical conditions under which nonlinear plasma evolution becomes microgravity-dependent while providing quantitative criteria for operational stability. A dimensionless governance ratio was introduced as the principal classification metric, coupling the electromagnetic control bandwidth with the nonlinear instability growth rate. The framework was further integrated with a three-tier distributed intelligent governance of stabilized plasmas supervisory architecture, through which electromagnetic actuation, thermal-ionization energy balance, and structural boundary response are coordinated across multiple interacting physical domains. Three operating regimes were thereby defined: admissible (R > 10), marginal (1 < R ≤ 10), and runaway (R ≤ 1), each associated with prescribed electromagnetic control actions, a diagnostic latency constraint, and mandatory termination logic. An analytical microgravity threshold was derived. Recent observations from the Plasma Kristall-4 (PK-4) complex plasma facility aboard the International Space Station (ISS) were shown to be consistent with the predicted emergence of field-aligned filamentary structures and anisotropic nonlinear transport under reduced-gravity conditions. Finally, five quantitative and experimentally falsifiable predictions were formulated to establish a systematic validation pathway for future microgravity plasma experiments. Collectively, the proposed framework provides a rigorous theoretical foundation for the analysis, governance, and engineering design of high-energy-density plasma systems operating in microgravity and establishes a general methodology for the development of next-generation plasma propulsion technologies, advanced confinement architectures, and reaction-boundary control systems in coupled multi-physics environments.
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