Search Results (24)

A comprehensive numerical method was developed to ensure energy-efficient operating modes of a linear motion module powered by an induction motor. The proposed approach is based on minimizing inertial torque, accounting for the inertial properties of the drive components and the load carriage, followed by structural-parametric optimization and dynamic modeling. For the optimization of the drive system, comprising an intermediate gear stage and a primary ball screw mechanism, a normalization-based method combined with numerical parameter sweep was employed. The optimization process yielded optimal values of the screw lead and the number of gear teeth, which were further validated in terms of Pareto optimality. The carriage design was optimized with respect to mass, strength constraints, and dynamic stiffness using the finite element method. For the developed linear motion module, dynamic behavior was simulated by means of a system of nonlinear differential equations, taking into account the electromagnetic characteristics of the induction motor and the nonlinearities of the gear mesh. As a result of the comprehensive approach, the kinematic, force, and energy characteristics of the linear motion module, which was optimized at the design stage, were determined. Full article

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool. Full article

The dynamic performance of parallel robots directly determines the machining accuracy in large optical mirror processing (LOMP). However, limitations in traditional dynamic modeling methods hinder their application in real-time control, constraining further improvements in robotic precision. This paper aims to establish a high-precision and practical dynamic model that considers joint friction for parallel robots used in LOMP, and to design an efficient real-time friction compensation control strategy to effectively enhance trajectory tracking and repetitive positioning accuracy. The novelty of this work lies in proposing a dynamic modeling approach that integrates the static mechanics-based “Disassembly Method” with a “Coulomb + Viscous” friction model. First, static analysis of the mechanism is conducted using the “Disassembly Method” to accurately compute the joint constraint reactions in any pose, providing critical input for friction calculation. Subsequently, a complete dynamic model incorporating friction in joints such as Hooke joints, composite spherical hinges, and ball screws is developed based on the Newton–Euler formulation. This method overcomes the shortcomings of traditional approaches in solving joint reactions and managing model complexity. Numerical simulations demonstrate that, compared to conventional friction-neglected models, the proposed model reveals a maximum increase of approximately 350 N in driving chain joint reaction forces and significant peaks in driving forces at motion reversal instants (e.g., 0.28 s, 0.45 s), quantitatively proving that neglecting friction severely underestimates the actual system loads. Experimental validation shows that the feedforward PD friction compensator designed based on this model reduces the rotational tracking errors of the moving platform around the X- and Y-axis from 0.295° and 0.286° to 0.134° and 0.128°, respectively, achieving an error reduction of about 55% and effectively improving motion control accuracy. This study provides a reliable dynamic modeling foundation and an effective real-time control compensation solution to address force output errors and trajectory deviations caused by joint friction in high-precision LOMP. Full article

Planetary gearbox serves as a key transmission component in planetary ball screw actuator systems. Under the action of alternating loads, the stress concentration locations of the planet carrier in actuators with planetary gear trains are prone to fatigue cracks, which can lead to catastrophic system breakdowns. However, due to the complex vibration transmission path and the interference of uninterested vibration components, the characteristic modulation signal is ambiguous, so it is challenging to diagnose this fault. Therefore, this paper proposes a new fault diagnosis method. Firstly, a vibration signal model is established to accurately characterize the amplitude and phase modulation effects caused by cracked carriers, providing theoretical guidance for fault feature identification. Subsequently, three novel sideband evaluators of the modulation signal bispectrum (MSB) and their parameter selection ranges are proposed to efficiently locate the optimal fault-related bifrequency signatures and reduce computational cost, leveraging the effects identified by the model. Finally, a novel health indicator, the mean absolute root value (MARV), is used to monitor the state of the planet carrier. The effectiveness of this method is verified by experiments on the planetary gearbox test rig. The results show that the robustness of the amplitude and phase modulation effect of the cracked carrier in the low-frequency band is significantly higher than that in the high-frequency band, and the initial carrier crack can be accurately identified using this phenomenon under different operating conditions. This study provides a reliable solution for the condition monitoring and health management of the actuation system, which is helpful to improve the safety and reliability of operation. Full article

In the context of carbon neutrality and smart manufacturing, balancing the challenge of carbon and operational efficiency has become a hotspot issue. However, within the specific stage of multi-equipment collaborative manufacturing operational coupling in the production process, multi-state characteristics of equipment operation, multidependencies among operational states, the multi-source of carbon emissions, and spatiotemporal sequence coupling raise the dynamics and complexity of carbon emission modeling and carbon efficiency evaluation. Therefore, a novel methodology to integrate carbon efficiency into a multi-equipment collaboration manufacturing service cell (MECMfg-SC) is proposed in this paper. The stage of multi-equipment collaboration manufacturing operational coupling (MECMfg-OC) in the process of multi-equipment collaboration manufacturing is presented and explained. Then, the operational coupling energy consumption model is constructed based on the MECMfg-OC. The environmental cost performance indicators for smart manufacturing systems, including energy efficiency evaluation (EEe) indicators and carbon efficiency evaluation (CEe) indicators, are proposed. At last, a ball screw smart workshop in a leading Chinese NEV enterprise is introduced to verify the proposed approach. Empirical results confirm the approach’s effectiveness and practical viability. Full article

Mechanochemical methods have received much attention in the synthesis and design of all-solid-state battery materials in recent years due to their advantages of being green, efficient, easy to operate, and solvent-free. In this review, common mechanochemical methods, including high-energy ball milling, twin-screw extrusion (TSE), and resonant acoustic mixing (RAM), are introduced with the aim of providing a fundamental understanding of the subsequent material design. Subsequently, the discussion focuses on the application of mechanochemical methods in the construction of solid-state electrolytes, anode materials, and cathode materials, especially the research progress of mechanical energy-induced polymerization strategies in building flexible composite electrolytes and enhancing interfacial stability. Through the analysis of representative work, it is demonstrated that mechanochemical methods are gradually evolving from traditional physical processing tools to functional synthesis platforms with chemical reaction capabilities. This review systematically organizes its development and research trends in the field of all-solid-state battery materials and explores potential future breakthrough directions. Full article

In integrated permanent magnet synchronous motors (IPMSMs) coupled with mechanical devices such as ball screws and reducers, complex nonlinear friction characteristics often arise, leading to asymmetrical distortions such as position “flat-top” and speed “ramp-up”. These phenomena significantly degrade the system’s positioning accuracy. To address this issue, this paper introduces a symmetry-inspired nonlinear predictive speed control approach based on the Stribeck piecewise linearized friction compensation and a generalized proportional integral (GPI) observer. The proposed method leverages the inherent symmetry in the Stribeck friction model to describe the nonlinear behavior, employing online piecewise linearization via the least squares method. A GPI observer was designed to estimate the lumped disturbance, including time-varying components in the speed dynamics, friction model deviations, and external loads. By incorporating these estimates, a nonlinear predictive controller was developed, employing a quadratic cost function to derive the optimal control law. The experimental results demonstrate that, compared to traditional integral NPC and PI controllers, the proposed method effectively restores system symmetry by eliminating the “flat-top” and “ramp-up” distortions while maintaining computational efficiency. Full article

Studying the interactions between biological organisms and their environment provides engineers with valuable insights for developing complex mechanical systems and fostering the creation of novel technological innovations. In this study, we introduce a novel bio-inspired three degrees of freedom (DOF) spherical robotic manipulator (SRM), designed to emulate the biomechanical properties observed in nature. The design utilizes the transformation of spherical Complex Spatial Kinematic Pairs (CSKPs) to synthesize bio-inspired robotic manipulators. Additionally, the use of screw theory and the Levenberg–Marquardt algorithm for kinematic parameter computation supports further advancements in human–robot interactions and simplifies control processes. The platform directly transmits motion from the motors to replicate the ball-and-socket mobility of biological joints, minimizing mechanical losses, and optimizing energy efficiency for superior spatial mobility. The proposed 3DOF SRM provides advantages including an expanded workspace, enhanced dexterity, and a lightweight, compact design. Experimental validation, conducted through SolidWorks, MATLAB, Python, and Arduino, demonstrates the versatility and broad application potential of the novel bio-inspired 3DOF SRM, positioning it as a robust solution for a wide range of robotic applications. Full article

In order to realise the efficient maintenance of industrial machines, Small and Medium-sized Enterprises (SMEs) need a system that utilises digital technology to handle everything from data collection to the visualisation of the collected data in an integrated manner. In this paper, an integrated monitoring platform using external sensor devices is proposed and implemented for the purpose of preventive maintenance of industrial machines. The proposed system performs edge processing to calculate features effective for monitoring on the sensor device, collects only the obtained features, and visualises them on a web server. In order to determine the features required by edge processing, a cycle waveform cut-out algorithm was proposed. As an evaluation experiment, the proposed system was used to detect the loosening of bolts on the support side of a ball screw. The results of the analysis showed that the dispersion value immediately after the start of uniform motion from the right end to the left end was valid, so the system was implemented as edge processing in the sensor device. In wireless transmission experiments on a testbed, an average of 20 consecutive cycles were used to achieve a 99.9% correct response rate and high detection accuracy, demonstrating the usefulness of the proposed system. Full article

System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. Studies on electric power consumption during the processing of the proposed structure of a neural network, implementing SHM, were carried out for three hardware platforms: computer, Raspberry Pi 4B, and Kria KV260. It was found that the average electrical power consumed during calculations is the lowest for the Kria platform using the FPGA system. However, the best ratio of the average power consumption to the accuracy of the neural network was obtained for the Raspberry Pi 4B. The concept of an efficient and energy-saving hardware platform that enables monitoring and analysis of the operation of the selected dynamic system was proposed. It allows for easy integration of many software environments (e.g., MATLAB and Python) with the System-on-a-Chip (SoC) platform containing an FPGA and a CPU. Full article

Lubricating grease plays an important role in the rolling screw transmission of the actuation system and is particularly concerned with the performance stability of long-term storage in aerospace applications. In this article, a batch of ball screws that were lying flat in the warehouse for about eight years were selected to extract lubricating grease from the screw raceway after being stored in situ. The oxidation performance, friction performance and rheological properties of lubricating grease were tested and compared to those of fresh grease to analyze and summarize the performance changes in lubricating grease. The transmission efficiency, friction torque, and temperature rise of ball screws without removing the original grease and those filled with fresh grease after cleaning were tested and compared. The impact of grease degradation on the transmission performance of ball screws was analyzed to provide a reference for ball screw lubrication schemes and further accelerated storage experimental design. Full article

In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods. Full article

Vibration in state-of-the-art machining impacts accuracy by diminishing the machine’s dynamic precision and the workpiece surface quality. The dependability of the cutters and productivity becomes a severe problem for optimizing the computer numerical control machine tools’ (CNCMT) efficiency. Therefore, investigating the twin ball screw drive system vibration properties as well as its corresponding control measures is vital. This paper thoroughly reviews the recent works on methods of analyzing and controlling vibration for dual-driven feed systems (DDFS). The research on vibration control technologies, parameter identification, and system modeling are identified and summarized; the merits and drawbacks of various methods are discussed for comparative purposes. Furthermore, the asymmetrical relation between DDFS and single-driven feed systems are thoroughly discussed based on their dynamic properties. Finally, based on existing studies, related research prospects are described systematically, and these research directions are sure to markedly contribute to developing methods for dampening vibrations on DDFS of CNCMT. Full article

The real-time compensation of thermal error in ball screws is an effective means to improve the accuracy of machining tools. However, the trade-off between robustness and computational efficiency of existing ball screw thermal error models is complicated and not conducive to practical, high-precision, real-time error compensation. Focusing on this problem, we propose an iterative prediction model of screw thermal error based on a finite difference equation. By assuming an approximately linear relationship between heat generation and the ball screw’s convection power and feed speed, a simplified and more efficient identification of physical parameters needed for the iterative model is achieved. The proposed method is integrated with a three-axis drilling and tapping machine powered by an HNC–848D controller. A test piece machine using the proposed real-time thermal error compensation method exhibited a maximum machining error of 13 µm, compared to the 71 µm of an uncompensated specimen. The proposed method is demonstrated to improve machining accuracy, especially in the X- and Y- axes, and overcome the limitations of traditional thermal error prediction models. Full article

The robustness of the control algorithm plays a crucial role in the precision manufacturing and measurement of the CNC machine tool. This paper proposes a fuzzy PID controller based on a sparse fuzzy rule base (S-FPID), which can effectively control the position of a nonlinear CNC machine tool servo system consisting of a rotating motor and ball screw. In order to deal with the influences of both the internal and external uncertainties in the servo system, fuzzy logic is used to adjust the proportion, and integral and differential parameters in real-time to improve the robustness of the system. In the fuzzy inference engine of FPID, a sparse fuzzy rule base is used instead of a full-order fuzzy rule base, which significantly improves the computational efficiency of FPID and saves a lot of RAM storage space. The sensitivity analysis of S-FPID verifies the self-tuning ability of its parameters. Furthermore, the proposed S-FPID has been compared with the PID and FPID via simulation and experiment. The results show that compared with the classical PID controller, the overshoot of the S-FPID controller is reduced by 74.29%, and the anti-interference ability is increased by 62.43%; compared with FPID algorithm, the efficiency of the SPID is improved by 87.25% on the premise of a slight loss in robustness. Full article