To improve the fuel economy of rail vehicles, this study presents the feasibility of using power regenerating dampers (PRDs) in the primary suspension systems of railway vehicles and evaluates the potential and recoverable power that can be obtained. PRDs are configured as hydraulic electromagnetic-based railway primary vertical dampers and evaluated in parallel and series modes (with and without a viscous damper). Hydraulic configuration converts the linear behavior of the track into a unidirectional rotation of the generator, and the electromagnetic configuration provides a controllable damping force to the primary suspension system. In several case studies, generic railway vehicle primary suspension systems that are configured to include a PRD in the two configuration modes are modeled using computer simulations. The simulations are performed on measured tracks with typical irregularities for a generic UK passenger route. The performance of the modified vehicle is evaluated with respect to key performance indicators, including regenerated power, ride comfort, and running safety. Results indicate that PRDs can simultaneously replace conventional primary vertical dampers, regenerate power, and exhibit desirable dynamic performance. A peak power efficiency of 79.87% is theoretically obtained in series mode on a top-quality German Intercity Express track (Track 270) at a vehicle speed of 160 mile/h (~257 km/h).
Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.
Optical interferometry is a powerful tool for measuring and characterizing areal surface topography in precision manufacturing. A variety of instruments based on optical interferometry have been developed to meet the measurement needs in various applications, but the existing techniques are simply not enough to meet the ever-increasing requirements in terms of accuracy, speed, robustness, and dynamic range, especially in on-line or on-machine conditions. This paper provides an in-depth perspective of surface topography reconstruction for optical interferometric measurements. Principles, configurations, and applications of typical optical interferometers with different capabilities and limitations are presented. Theoretical background and recent advances of fringe analysis algorithms, including coherence peak sensing and phase-shifting algorithm, are summarized. The new developments in measurement accuracy and repeatability, noise resistance, self-calibration ability, and computational efficiency are discussed. This paper also presents the new challenges that optical interferometry techniques are facing in surface topography measurement. To address these challenges, advanced techniques in image stitching, on-machine measurement, intelligent sampling, parallel computing, and deep learning are explored to improve the functional performance of optical interferometry in future manufacturing metrology.
Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.
The level set method (LSM), which is transplanted from the computer graphics field, has been successfully introduced into the structural topology optimization field for about two decades, but it still has not been widely applied to practical engineering problems as density-based methods do. One of the reasons is that it acts as a boundary evolution algorithm, which is not as flexible as density-based methods at controlling topology changes. In this study, a level set band method is proposed to overcome this drawback in handling topology changes in the level set framework. This scheme is proposed to improve the continuity of objective and constraint functions by incorporating one parameter, namely, level set band, to seamlessly combine LSM and density-based method to utilize their advantages. The proposed method demonstrates a flexible topology change by applying a certain size of the level set band and can converge to a clear boundary representation methodology. The method is easy to implement for improving existing LSMs and does not require the introduction of penalization or filtering factors that are prone to numerical issues. Several 2D and 3D numerical examples of compliance minimization problems are studied to illustrate the effects of the proposed method.
Experimental and finite element research was conducted on the bolted interference fit of a single-lap laminated structure to reveal the damage propagation mechanism and strength change law. A typical single-lap statically loading experiment was performed, and a finite element damage prediction model was built based on intralaminar progress damage theory. The model was programmed with a user subroutine and an interlaminar cohesive zone method. The deformation and damage propagation of the specimen were analyzed, and the failure mechanism of intralaminar and interlaminar damage during loading was discussed. The effect of secondary bending moment on load translation and damage distribution was revealed. The experimental and simulated load–displacement curves were compared to validate the developed model’s reliability, and the ultimate bearing strengths under different fit percentages were predicted. An optimal percentage was also recommended.
The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.
Capacitive sensors are efficient tools for biophysical force measurement, which is essential for the exploration of cellular behavior. However, attention has been rarely given on the influences of external mechanical and internal electrical interferences on capacitive sensors. In this work, a bionic swallow structure design norm was developed for mechanical decoupling, and the influences of structural parameters on mechanical behavior were fully analyzed and optimized. A bionic feather comb distribution strategy and a portable readout circuit were proposed for eliminating electrostatic interferences. Electrostatic instability was evaluated, and electrostatic decoupling performance was verified on the basis of a novel measurement method utilizing four complementary comb arrays and application-specific integrated circuit readouts. An electrostatic pulling experiment showed that the bionic swallow structure hardly moved by 0.770 nm, and the measurement error was less than 0.009% for the area-variant sensor and 1.118% for the gap-variant sensor, which can be easily compensated in readouts. The proposed sensor also exhibited high resistance against electrostatic rotation, and the resulting measurement error dropped below 0.751%. The rotation interferences were less than 0.330 nm and (1.829 × 10−7)°, which were 35 times smaller than those of the traditional differential one. Based on the proposed bionic decoupling method, the fabricated sensor exhibited overwhelming capacitive sensitivity values of 7.078 and 1.473 pF/µm for gap-variant and area-variant devices, respectively, which were the highest among the current devices. High immunity to mechanical disturbances was maintained simultaneously, i.e., less than 0.369% and 0.058% of the sensor outputs for the gap-variant and area-variant devices, respectively, indicating its great performance improvements over existing devices and feasibility in ultralow biomedical force measurement.
Earth rover is a class of emerging wheeled-leg robots for nature exploration. At present, few methods for these robots’ leg design utilize a side-mounted spatial parallel mechanism. Thus, this paper presents a complete design process of a novel 5-degree-of-freedom (5-DOF) hybrid leg mechanism for our quadruped earth rover BJTUBOT. First, a general approach is proposed for constructing the novel leg mechanism. Subsequently, by evaluating the basic locomotion task (LT) of the rover based on screw theory, we determine the desired motion characteristic of the side-mounted leg and carry out its two feasible configurations. With regard to the synthesis method of the parallel mechanism, a family of concise hybrid leg mechanisms using the 6-DOF limbs and an L1F1C limb (which can provide a constraint force and a couple) is designed. In verifying the motion characteristics of this kind of leg, we select a typical (3-UPRU&RRRR)&R mechanism and then analyze its kinematic model, singularities, velocity mapping, workspace, dexterity, statics, and kinetostatic performance. Furthermore, the virtual quadruped rover equipped with this innovative leg mechanism is built. Various basic and specific LTs of the rover are demonstrated by simulation, which indicates that the flexibility of the legs can help the rover achieve multitasking.
This study examines the development of the fluid and control technology of hydraulic wind turbines. The current state of hydraulic wind turbines as a new technology is described, and its basic fluid model and typical control method are expounded by comparing various study results. Finally, the advantages of hydraulic wind turbines are enumerated. Hydraulic wind turbines are expected to become the main development direction of wind turbines.
Spinning production is a typical continuous manufacturing process characterized by high speed and uncertain dynamics. Each manufacturing unit in spinning production produces various real-time tasks, which may affect production efficiency and yarn quality if not processed in time. This paper presents an edge computing-based method that is different from traditional centralized cloud computation because its decentralization characteristics meet the high-speed and high-response requirements of yarn production. Edge computing nodes, real-time tasks, and edge computing resources are defined. A system model is established, and a real-time task processing method is proposed for the edge computing scenario. Experimental results indicate that the proposed real-time task processing method based on edge computing can effectively solve the delay problem of real-time task processing in spinning cyber-physical systems, save bandwidth, and enhance the security of task transmission.
Selective laser melting (SLM) is a unique additive manufacturing (AM) category that can be used to manufacture mechanical parts. It has been widely used in aerospace and automotive using metal or alloy powder. The build orientation is crucial in AM because it affects the as-built part, including its part accuracy, surface roughness, support structure, and build time and cost. A mechanical part is usually composed of multiple surface features. The surface features carry the production and design knowledge, which can be utilized in SLM fabrication. This study proposes a method to determine the build orientation of multi-feature mechanical parts (MFMPs) in SLM. First, the surface features of an MFMP are recognized and grouped for formulating the particular optimization objectives. Second, the estimation models of involved optimization objectives are established, and a set of alternative build orientations (ABOs) is further obtained by many-objective optimization. Lastly, a multi-objective decision making method integrated by the technique for order of preference by similarity to the ideal solution and cosine similarity measure is presented to select an optimal build orientation from those ABOs. The weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchy process. Two case studies are reported to validate the proposed method with numerical results, and the effectiveness comparison is presented. Physical manufacturing is conducted to prove the performance of the proposed method. The measured average sampling surface roughness of the most crucial feature of the bracket in the original orientation and the orientations obtained by the weighted sum model and the proposed method are 15.82, 10.84, and 10.62 μm, respectively. The numerical and physical validation results demonstrate that the proposed method is desirable to determine the build orientations of MFMPs with competitive results in SLM.
Magnetorheological (MR) pin joint is a novel device in which its joint moment resistance can be controlled in real-time by altering the applied magnetic field. The smart pin joint is intended to be used as a controllable connector between the columns and beams of a civil structure to instantaneously shift the structural natural frequencies in order to avoid resonance and therefore to reduce unwanted vibrations and hence prevent structural damage. As an intrinsically nonlinear device, modelling of this MR fluid based device is a challenging task and makes the design of a suitable control algorithm a cumbersome situation. Aimed at its application in civil structure, the main purpose of this paper is to test and characterise the hysteretic behaviour of MR pin joint. A test scheme is designed to obtain the dynamic performance of MR pin joint in the dominant earthquake frequency range. Some unique phenomena different from those of MR damper are observed through the experimental testing. A computationally-efficient model is proposed by introducing a hyperbolic element to accurately reproduce its dynamic behaviour and to further facilitate the design of a suitable control algorithm. Comprehensive investigations on the model accuracy and dependences of the proposed model on loading condition (frequency and amplitude) and input current level are reported in the last section of this paper.
This paper presents a brief review of the current casting techniques for single-crystal (SC) blades, as well as an analysis of the solidification process in complex turbine blades. A series of novel casting methods based on the Bridgman process were presented to illustrate the development in the production of SC blades from superalloys. The grain continuator and the heat conductor techniques were developed to remove geometry-related grain defects. In these techniques, the heat barrier that hinders lateral SC growth from the blade airfoil into the extremities of the platform is minimized. The parallel heating and cooling system was developed to achieve symmetric thermal conditions for SC solidification in blade clusters, thus considerably decreasing the negative shadow effect and its related defects in the current Bridgman process. The dipping and heaving technique, in which thin-shell molds are utilized, was developed to enable the establishment of a high temperature gradient for SC growth and the freckle-free solidification of superalloy castings. Moreover, by applying the targeted cooling and heating technique, a novel concept for the three-dimensional and precise control of SC growth, a proper thermal arrangement may be dynamically established for the microscopic control of SC growth in the critical areas of large industrial gas turbine blades.
Device miniaturization is an emerging advanced technology in the 21st century. The miniaturization of devices in different fields requires production of micro- and nano-scale components. The features of these components range from the sub-micron to a few hundred microns with high tolerance to many engineering materials. These fields mainly include optics, electronics, medicine, bio-technology, communications, and avionics. This paper reviewed the recent advances in micro- and nano-machining technologies, including micro-cutting, micro-electrical-discharge machining, laser micro-machining, and focused ion beam machining. The four machining technologies were also compared in terms of machining efficiency, workpiece materials being machined, minimum feature size, maximum aspect ratio, and surface finish.
The smart toolholder is the core component in the development of intelligent and precise manufacturing. It enables in situ monitoring of cutting data and machining accuracy evolution and has become a focal point in academic research and industrial applications. However, current table and rotational dynamometers for milling force, vibration, and temperature testing suffer from cumbersome installation and provide only a single acquisition signal, which limits their use in laboratory settings. In this study, we propose a wireless smart toolholder with multi-sensor fusion for simultaneous sensing of milling force, vibration, and temperature signals. We select force, vibration, and temperature sensors suitable for smart toolholder fusion to adapt to the cutting environment. Thereafter, structural design, circular runout, dynamic balancing, static stiffness, and dynamic inherent frequency tests are conducted to assess its dynamic and static performance. Finally, the smart toolholder is tested for accuracy and repeatability in terms of force, vibration, and temperature. Experimental results demonstrate that the smart toolholder accurately captures machining data with a relative deviation of less than 1.5% compared with existing force gauges and provides high repeatability of milling temperature and vibration signals. Therefore, it is a smart solution for machining condition monitoring.
Servo-hydraulic actuators (SHAs) are widely used in mechanical equipment to drive heavy-duty mechanisms. However, their energy efficiency is low, and their motion characteristics are inevitably affected by uncertain nonlinearities. Electromechanical actuators (EMAs) possess superior energy efficiency and motion characteristics. However, they cannot easily drive heavy-duty mechanisms because of weak bearing capacity. This study proposes and designs a novel electromechanical-hydraulic hybrid actuator (EMHA) that integrates the advantages of EMA and SHA. EMHA mainly features two transmission mechanisms. The piston of the hydraulic transmission mechanism and the ball screw pair of the electromechanical transmission mechanism are mechanically fixed together through screw bolts, realizing the integration of two types of transmission mechanisms. The control scheme of the electromechanical transmission mechanism is used for motion control, and the hydraulic transmission mechanism is used for power assistance. Then, the mathematical model, structure, and parameter design of the new EMHA are studied. Finally, the EMHA prototype and test platform are manufactured. The test results prove that the EMHA has good working characteristics and high energy efficiency. Compared with the valve-controlled hydraulic cylinder system, EMHA exhibits a velocity tracking error and energy consumption reduced by 49.7% and 54%, respectively, under the same working conditions.
Edge preparation can remove cutting edge defects, such as burrs, chippings, and grinding marks, generated in the grinding process and improve the cutting performance and service life of tools. Various edge preparation methods have been proposed for different tool matrix materials, geometries, and application requirements. This study presents a scientific and systematic review of the development of tool edge preparation technology and provides ideas for its future development. First, typical edge characterization methods, which associate the microgeometric characteristics of the cutting edge with cutting performance, are briefly introduced. Then, edge preparation methods for cutting tools, in which materials at the cutting edge area are removed to decrease defects and obtain a suitable microgeometry of the cutting edge for machining, are discussed. New edge preparation methods are explored on the basis of existing processing technologies, and the principles, advantages, and limitations of these methods are systematically summarized and analyzed. Edge preparation methods are classified into two categories: mechanical processing methods and nontraditional processing methods. These methods are compared from the aspects of edge consistency, surface quality, efficiency, processing difficulty, machining cost, and general availability. In this manner, a more intuitive understanding of the characteristics can be gained. Finally, the future development direction of tool edge preparation technology is prospected.
The laser homogenizing equipment was devised using the ring scanning principle. Its working principle is explained. A laser scanning ring facula is obtained when the laser beam goes through the equipment’s optical system rotating with high-frequency. The scanning ring facula’s mathematic model is established based on the temperature field’s superposing principle. The ring facula’s light intensity distribution and temperature distribution characteristics are achieved by simulating its temperature field. By studying the effect of parameters on the temperature field, the best parameter can be found. Results show that favorable temperature distribution characteristics can be attained by choosing appropriate parameters, and even the thermal effect can be realized by utilizing the circumference power compensating for the heat exchange lost in the horizontal direction. The uniform hardness layer and better process quality can be attained using the ring facula optimized for metal laser heat treatment.
Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are cost-effective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.
Although the manufacturing industry has improved the quality of processing, optimization and upgrading must be performed to meet the requirements of global sustainable development. Sustainable production is considered to be a favorable strategy for achieving machining upgrades characterized by high quality, high efficiency, energy savings, and emission reduction. Sustainable production has aroused widespread interest, but only a few scholars have studied the sustainability of machining from multiple dimensions. The sustainability of machining must be investigated multidimensionally and accurately. Thus, this study explores the sustainability of machining from the aspects of equipment, process, and strategy. In particular, the equipment, process, and strategy of sustainable machining are systematically analyzed and integrated into a research framework. Then, this study analyzes sustainable machining-oriented machining equipment from the aspects of machine tools, cutting tools, and materials such as cutting fluid. Machining processes are explored as important links of sustainable machining from the aspects of dry cutting, microlubrication, microcutting, low-temperature cutting, and multidirectional cutting. The strategies for sustainable machining are also analyzed from the aspects of energy-saving control, machining simulation, and process optimization of machine tools. Finally, opportunities and challenges, including policies and regulations toward sustainable machining, are discussed. This study is expected to offer prospects for sustainable machining development and strategies for implementing sustainable machining.
The ever-increasing requirements for the scalable manufacturing of atomic-scale devices emphasize the significance of developing atomic-scale manufacturing technology. The mechanism of a single atomic layer removal in cutting is the key basic theoretical foundation for atomic-scale mechanical cutting. Material anisotropy is among the key decisive factors that could not be neglected in cutting at such a scale. In the present study, the crystallographic orientation effect on the cutting-based single atomic layer removal of monocrystalline copper is investigated by molecular dynamics simulation. When undeformed chip thickness is in the atomic scale, two kinds of single atomic layer removal mechanisms exist in cutting-based single atomic layer removal, namely, dislocation motion and extrusion, due to the differing atomic structures on different crystallographic planes. On close-packed crystallographic plane, the material removal is dominated by the shear stress-driven dislocation motion, whereas on non-close packed crystallographic planes, extrusion-dominated material removal dominates. To obtain an atomic, defect-free processed surface, the cutting needs to be conducted on the close-packed crystallographic planes of monocrystalline copper.
Carbon fiber reinforced plastic (CFRP) composites are extremely attractive in the manufacturing of structural and functional components in the aircraft manufacturing field due to their outstanding properties, such as good fatigue resistance, high specific stiffness/strength, and good shock absorption. However, because of their inherent anisotropy, low interlamination strength, and abrasive characteristics, CFRP composites are considered difficult-to-cut materials and are prone to generating serious hole defects, such as delamination, tearing, and burrs. The advanced longitudinal–torsional coupled ultrasonic vibration assisted drilling (LTC-UAD) method has a potential application for drilling CFRP composites. At present, LTC-UAD is mainly adopted for drilling metal materials and rarely for CFRP. Therefore, this study analyzes the kinematic characteristics and the influence of feed rate on the drilling performance of LTC-UAD. Experimental results indicate that LTC-UAD can reduce the thrust force by 39% compared to conventional drilling. Furthermore, LTC-UAD can decrease the delamination and burr factors and improve the surface quality of the hole wall. Thus, LTC-UAD is an applicable process method for drilling components made with CFRP composites.
Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.
Enabled by advancements in multi-material additive manufacturing, lightweight lattice structures consisting of networks of periodic unit cells have gained popularity due to their extraordinary performance and wide array of functions. This work proposes a density-based robust topology optimization method for meso- or macro-scale multi-material lattice structures under any combination of material and load uncertainties. The method utilizes a new generalized material interpolation scheme for an arbitrary number of materials, and employs univariate dimension reduction and Gauss-type quadrature to quantify and propagate uncertainty. By formulating the objective function as a weighted sum of the mean and standard deviation of compliance, the tradeoff between optimality and robustness can be studied and controlled. Examples of a cantilever beam lattice structure under various material and load uncertainty cases exhibit the efficiency and flexibility of the approach. The accuracy of univariate dimension reduction is validated by comparing the results to the Monte Carlo approach.
Many heat transfer tubes are distributed on the tube plates of a steam generator that requires periodic inspection by robots. Existing inspection robots are usually involved in issues: Robots with manipulators need complicated installation due to their fixed base; tube mobile robots suffer from low running efficiency because of their structural restricts. Since there are thousands of tubes to be checked, task planning is essential to guarantee the precise, orderly, and efficient inspection process. Most in-service robots check the task tubes using row-by-row and column-by-column planning. This leads to unnecessary inspections, resulting in a long shutdown and affecting the regular operation of a nuclear power plant. Therefore, this paper introduces the structure and control system of a dexterous robot and proposes a task planning method. This method proceeds into three steps: task allocation, base position search, and sequence planning. To allocate the task regions, this method calculates the tool work matrix and proposes a criterion to evaluate a sub-region. And then all tasks contained in the sub-region are considered globally to search the base positions. Lastly, we apply an improved ant colony algorithm for base sequence planning and determine the inspection orders according to the planned path. We validated the optimized algorithm by conducting task planning experiments using our robot on a tube sheet. The results show that the proposed method can accomplish full task coverage with few repetitive or redundant inspections and it increases the efficiency by 33.31% compared to the traditional planning algorithms.
In this paper, an uncertainty propagation analysis method is developed based on an extended sparse grid technique and maximum entropy principle, aiming at improving the solving accuracy of the high-order moments and hence the fitting accuracy of the probability density function (PDF) of the system response. The proposed method incorporates the extended Gauss integration into the uncertainty propagation analysis. Moreover, assisted by the Rosenblatt transformation, the various types of extended integration points are transformed into the extended Gauss-Hermite integration points, which makes the method suitable for any type of continuous distribution. Subsequently, within the sparse grid numerical integration framework, the statistical moments of the system response are obtained based on the transformed points. Furthermore, based on the maximum entropy principle, the obtained first four-order statistical moments are used to fit the PDF of the system response. Finally, three numerical examples are investigated to demonstrate the effectiveness of the proposed method, which includes two mathematical problems with explicit expressions and an engineering application with a black-box model.
Biomedical sensors have been widely used in various areas of biomedical practices, which play an important role in disease detection, diagnosis, monitoring, treatment, health management, and so on. However, most of them and their related platforms are generally not easily accessible or just too expensive or complicated to be kept at home. As an alternative, new technologies enabled from the mobile phones are gradually changing such situations. As can be freely available to almost everyone, mobile phone offers a unique way to improve the conventional medical care through combining with various biomedical sensors. Moreover, the established systems will be both convenient and low cost. In this paper, we present an overview on the state-of-art biomedical sensors, giving a brief introduction of the fundamental principles and showing several new examples or concepts in the area. The focus was particularly put on interpreting the technical strategies to innovate the biomedical sensor technologies based on the platform of mobile phones. Some challenging issues, including feasibility, usability, security, and effectiveness, were discussed. With the help of electrical and mechanical technologies, it is expected that a full combination between the biomedical sensors and mobile phones will bring a bright future for the coming pervasive medical care.
The manufacture and maintenance of large parts in ships, trains, aircrafts, and so on create an increasing demand for mobile machine tools to perform in-situ operations. However, few mobile robots can accommodate the complex environment of industrial plants while performing machining tasks. This study proposes a novel six-legged walking machine tool consisting of a legged mobile robot and a portable parallel kinematic machine tool. The kinematic model of the entire system is presented, and the workspace of different components, including a leg, the body, and the head, is analyzed. A hierarchical motion planning scheme is proposed to take advantage of the large workspace of the legged mobile platform and the high precision of the parallel machine tool. The repeatability of the head motion, body motion, and walking distance is evaluated through experiments, which is 0.11, 1.0, and 3.4 mm, respectively. Finally, an application scenario is shown in which the walking machine tool steps successfully over a 250 mm-high obstacle and drills a hole in an aluminum plate. The experiments prove the rationality of the hierarchical motion planning scheme and demonstrate the extensive potential of the walking machine tool for in-situ operations on large parts.
A primary permanent-magnet linear motor (PPMLM) has a robust secondary structure and high force density and is appropriate for direct-drive mechanical press. The structure of a four-side PPMLM drive press is presented based on our previous research. The entire press control system is constructed to realize various flexible forming processes. The control system scheme is determined in accordance with the mathematical model of PPMLM, and active disturbance rejection control is implemented in the servo controller. Field-circuit coupling simulation is applied to estimate the system’s performance. Then, a press prototype with 6 kN nominal force is fabricated, and the hardware platform of the control system is constructed for experimental study. Punch strokes with 0.06 m displacement are implemented at trapezoidal speeds of 0.1 and 0.2 m/s; the dynamic position tracking errors are less than 0.45 and 0.82 mm, respectively. Afterward, continuous reciprocating strokes are performed, and the positioning errors at the bottom dead center are less than 0.015 mm. Complex pulse trajectories are also achieved. The proposed PPMLM drive press exhibits a fast dynamic response and favorable tracking precision and is suitable for various forming processes.
Energy field-assisted machining technology has the potential to overcome the limitations of machining difficult-to-machine metal materials, such as poor machinability, low cutting efficiency, and high energy consumption. High-speed dry milling has emerged as a typical green processing technology due to its high processing efficiency and avoidance of cutting fluids. However, the lack of necessary cooling and lubrication in high-speed dry milling makes it difficult to meet the continuous milling requirements for difficult-to-machine metal materials. The introduction of advanced energy-field-assisted green processing technology can improve the machinability of such metallic materials and achieve efficient precision manufacturing, making it a focus of academic and industrial research. In this review, the characteristics and limitations of high-speed dry milling of difficult-to-machine metal materials, including titanium alloys, nickel-based alloys, and high-strength steel, are systematically explored. The laser energy field, ultrasonic energy field, and cryogenic minimum quantity lubrication energy fields are introduced. By analyzing the effects of changing the energy field and cutting parameters on tool wear, chip morphology, cutting force, temperature, and surface quality of the workpiece during milling, the superiority of energy-field-assisted milling of difficult-to-machine metal materials is demonstrated. Finally, the shortcomings and technical challenges of energy-field-assisted milling are summarized in detail, providing feasible ideas for realizing multi-energy field collaborative green machining of difficult-to-machine metal materials in the future.
Product innovation is often a process for improving existing products. Low-end disruptive innovation (LDI) enables a product to meet the most price-sensitive customers in the low-end market. The existing LDI methods are mainly based on unnecessary characteristics of disruptive innovations. Thus, they cannot easily identify and respond to the LDI design needs. This study proposes a hybrid method for the product LDI in two levels of the product design based on the summarized definition and essential characteristics of LDI. Feasible areas of the product LDI are determined using a hybrid relational function model to identify the maturity of dominant technologies. The technologies are identified through the technical search and evaluation of the feasible area for innovation to form an initial LDI scheme. Then, the product function is optimized using the trimming concept of theory of inventive problem solving based on the characteristics of LDI. The final LDI scheme is formed and evaluated based on the essential characteristics of the product LDI. The feasibility of the proposed method is verified in the design of a new dropping pill machine.
The parallel spindle heads with high rotational capability are demanded in the area of multi-axis machine tools and 3D printers. This paper focuses on designing a class of 2R1T (R: Rotation; T: Translation) parallel spindle heads and the corresponding collaborative 5-axis manipulators with 2-dimension (2D) large rotational angles. In order to construct 2D rotational degrees of freedom (DOFs), a platform with 2D revolute joints is proposed first. Based on the constraint screw theory, the feasible limbs that can be connected in the platform are synthesized. In order to provide constant rotational axis for the platform, a class of redundant limbs are designed. A class of redundant 2R1T parallel spindle heads is obtained by connecting the redundant limbs with the platform and the redundant characteristics are verified by the modified Grübler-Kutzbach criterion. The corresponding 5-axis collaborative manipulators are presented by constructing a 2-DOF series translational bottom moving platform. The inverse kinematics and the orientation workspace as well as the decoupling characteristics of this type of 2R1T parallel spindle heads are analyzed. The results show that these manipulators have large 2D rotational angles than the traditional A3/Z3 heads and can be potentially used in the application of multi-axis machine tools and the 3D printers.
Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.
Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on µ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and µ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.
This paper proposes a novel continuous footholds optimization method for legged robots to expand their walking ability on complex terrains. The algorithm can efficiently run onboard and online by using terrain perception information to protect the robot against slipping or tripping on the edge of obstacles, and to improve its stability and safety when walking on complex terrain. By relying on the depth camera installed on the robot and obtaining the terrain heightmap, the algorithm converts the discrete grid heightmap into a continuous costmap. Then, it constructs an optimization function combined with the robot’s state information to select the next footholds and generate the motion trajectory to control the robot’s locomotion. Compared with most existing footholds selection algorithms that rely on discrete enumeration search, as far as we know, the proposed algorithm is the first to use a continuous optimization method. We successfully implemented the algorithm on a hexapod robot, and verified its feasibility in a walking experiment on a complex terrain.
In recent years, the new technologies and discoveries on manufacturing materials have encouraged researchers to investigate the appearance of material properties that are not naturally available. Materials featuring a specific stiffness, or structures that combine non-structural and structural functions are applied in the aerospace, electronics and medical industry fields. Particularly, structures designed for dynamic actuation with reduced vibration response are the focus of this work. The bi-material and multifunctional concepts are considered for the design of a controlled piezoelectric actuator with vibration suppression by means of the topology optimization method (TOM). The bi-material piezoelectric actuator (BPEA) has its metallic host layer designed by the TOM, which defines the structural function, and the electric function is given by two piezo-ceramic layers that act as a sensor and an actuator coupled with a constant gain active velocity feedback control (AVFC). The AVFC, provided by the piezoelectric layers, affects the structural damping of the system through the velocity state variables readings in time domain. The dynamic equation analyzed throughout the optimization procedure is fully elaborated and implemented. The dynamic response for the rectangular four-noded finite element analysis is obtained by the Newmark’s time-integration method, which is applied to the physical and the adjoint systems, given that the adjoint formulation is needed for the sensitivity analysis. A gradient-based optimization method is applied to minimize the displacement energy output measured at a predefined degree-of-freedom of the BPEA when a transient mechanical load is applied. Results are obtained for different control gain values to evaluate their influence on the final topology.
Recently, advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate diagnosis results, multi-sensor fusion has long been studied in the fault diagnosis field. However, existing studies suffer from two weaknesses. First, the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types. Second, the localization for multi-source faults is seldom investigated, although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable. This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations (MSRs). First, an MSR model is developed to learn MSRs automatically and further obtain fault recognition results. Second, centrality measures are employed to analyze the MSR graphs learned by the MSR model, and fault sources are therefore determined. The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump. Results show the proposed method’s validity in diagnosing fault types and sources.
Soft-brittle crystal materials are widely used in many fields, especially optics and microelectronics. However, these materials are difficult to machine through traditional machining methods because of their brittle, soft, and anisotropic nature. In this article, the characteristics and machining difficulties of soft-brittle and crystals are presented. Moreover, the latest research progress of novel machining technologies and their applications for soft-brittle crystals are introduced by using some representative materials (e.g., potassium dihydrogen phosphate (KDP), cadmium zinc telluride (CZT)) as examples. This article reviews the research progress of soft-brittle crystals processing.
This study examines roll stability control for vehicles with an active roll-resistant electro-hydraulic suspension (RREHS) subsystem under steering maneuvers. First, we derive a vehicle model with four degrees of freedom and incorporates yaw and roll motions. Second, an optimal linear quadratic regulator controller is obtained in consideration of dynamic vehicle performance. Third, an RREHS subsystem with an electric servo-valve actuator is proposed, and the corresponding dynamic equations are obtained. Fourth, field experiments are conducted to validate the performance of the vehicle model under sine-wave and double-lane-change steering maneuvers. Finally, the effectiveness of the active RREHS is determined by examining vehicle responses under sine-wave and double-lane-change maneuvers. The enhancement in vehicle roll stability through the RREHS subsystem is also verified.
With the proposal of intelligent mines, unmanned mining has become a research hotspot in recent years. In the field of autonomous excavation, environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance. In this study, an unmanned electric shovel (UES) is developed, and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented. Initially, the point cloud of the material surface is collected and reconstructed by polynomial response surface (PRS) method. Then, by establishing the dynamical model of the UES, a point to point (PTP) excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption. Based on optimal trajectory command, the UES performs autonomous excavation. The experimental results show that the proposed surface reconstruction method can accurately represent the material surface. On the basis of reconstructed surface, the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency. Compared with the common excavation trajectory planning approaches, the proposed method tends to be more capable in terms of mining time and energy consumption, ensuring high-performance excavation of the UES in practical mining environment.
Laser polishing is a technology of smoothening the surface of various materials with highly intense laser beams. When these beams impact on the material surface to be polished, the surface starts to be melted due to the high temperature. The melted material is then relocated from the ‘peaks to valleys’ under the multidirectional action of surface tension. By varying the process parameters such as beam intensity, energy density, spot diameter, and feed rate, different rates of surface roughness can be achieved. High precision polishing of surfaces can be done using laser process. Currently, laser polishing has extended its applications from photonics to molds as well as bio-medical sectors. Conventional polishing techniques have many drawbacks such as less capability of polishing freeform surfaces, environmental pollution, long processing time, and health hazards for the operators. Laser polishing on the other hand eliminates all the mentioned drawbacks and comes as a promising technology that can be relied for smoothening of initial topography of the surfaces irrespective of the complexity of the surface. Majority of the researchers performed laser polishing on materials such as steel, titanium, and its alloys because of its low cost and reliability. This article gives a detailed overview of the laser polishing mechanism by explaining various process parameters briefly to get a better understanding about the entire polishing process. The advantages and applications are also explained clearly to have a good knowledge about the importance of laser polishing in the future.
Bone grinding is an essential and vital procedure in most surgical operations. Currently, the insufficient cooling capacity of dry grinding, poor visibility of drip irrigation surgery area, and large grinding force leading to high grinding temperature are the technical bottlenecks of micro-grinding. A new micro-grinding process called ultrasonic vibration-assisted nanoparticle jet mist cooling (U-NJMC) is innovatively proposed to solve the technical problem. It combines the advantages of ultrasonic vibration (UV) and nanoparticle jet mist cooling (NJMC). Notwithstanding, the combined effect of multi parameter collaborative of U-NJMC on cooling has not been investigated. The grinding force, friction coefficient, specific grinding energy, and grinding temperature under dry, drip irrigation, UV, minimum quantity lubrication (MQL), NJMC, and U-NJMC micro-grinding were compared and analyzed. Results showed that the minimum normal grinding force and tangential grinding force of U-NJMC micro-grinding were 1.39 and 0.32 N, which were 75.1% and 82.9% less than those in dry grinding, respectively. The minimum friction coefficient and specific grinding energy were achieved using U-NJMC. Compared with dry, drip, UV, MQL, and NJMC grinding, the friction coefficient of U-NJMC was decreased by 31.3%, 17.0%, 19.0%, 9.8%, and 12.5%, respectively, and the specific grinding energy was decreased by 83.0%, 72.7%, 77.8%, 52.3%, and 64.7%, respectively. Compared with UV or NJMC alone, the grinding temperature of U-NJMC was decreased by 33.5% and 10.0%, respectively. These results showed that U-NJMC provides a novel approach for clinical surgical micro-grinding of biological bone.
Topology optimization is a pioneer design method that can provide various candidates with high mechanical properties. However, high resolution is desired for optimum structures, but it normally leads to a computationally intractable puzzle, especially for the solid isotropic material with penalization (SIMP) method. In this study, an efficient, high-resolution topology optimization method is developed based on the super-resolution convolutional neural network (SRCNN) technique in the framework of SIMP. SRCNN involves four processes, namely, refinement, path extraction and representation, nonlinear mapping, and image reconstruction. High computational efficiency is achieved with a pooling strategy that can balance the number of finite element analyses and the output mesh in the optimization process. A combined treatment method that uses 2D SRCNN is built as another speed-up strategy to reduce the high computational cost and memory requirements for 3D topology optimization problems. Typical examples show that the high-resolution topology optimization method using SRCNN demonstrates excellent applicability and high efficiency when used for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.
This article is dedicated to present a review on existing challenges and latest developments in surgical robotics in attempts to overcome the obstacles lying behind. Rather than to perform an exhaustive evaluation, we would emphasize more on the new insight by digesting the emerging bio-inspired surgical technologies with potentials to revolutionize the field. Typical approaches, possible applications, advantages and technical challenges were discussed. Evolutions of surgical robotics and future trends were analyzed. It can be found that, the major difficulties in the field of surgical robots may not be properly addressed by only using conventional approaches. As an alternative, bio-inspired methods or materials may shed light on new innovations. While endeavors to deal with existing strategies still need to be made, attentions should be paid to also borrow ideas from nature.
To achieve the collision-free trajectory tracking of the four-wheeled mobile robot (FMR), existing methods resolve the tracking control and obstacle avoidance separately. Guaranteeing the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is difficult. To address this challenge, this paper proposes an obstacle-circumventing adaptive control (OCAC) framework. Specifically, a novel anti-disturbance terminal slide mode control with adaptive gains is formulated, incorporating specified control laws for different stages. This formulation guarantees rapid convergence and simultaneous chattering elimination. By introducing sub-target points, a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one, thereby reducing the burden of local path searching while ensuring system stability during obstacle circumvention. Comparative experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.
Counter-roller spinning (CRS), where the mandrel is replaced by rollers, is an effective means of manufacturing large-sized, thin-walled, cylindrical parts with more than 2500 mm diameter. CRS is very complex because of multi-axis rotation, multi-local loading along the circumference, and radial-axial compound deformation. Analytical or experimental methods cannot fully understand CRS. Meanwhile, numerical simulation is an adequate approach to investigate CRS with comprehensive understanding and a low cost. Thus, a finite element (FE) model of CRS was developed with the FORGE code via meshing technology, material modeling, determining the friction condition, and so on. The local fine mesh moving with the roller is one of highlights of the model. The developed 3D-FE model was validated through a CRS experiment by using a tubular blank with a 720 mm outer diameter. The developed 3D-FE model of CRS can provide a basis for parameter optimization, process control, die design, and so on. The data on force and energy predicted by the 3D-FE model can offer reasonable suggestions for determining the main mechanical parameters of CRS machines and selecting the motors. With the predicted data, an all-electric servo-drive system/machine with distributed power was designed in this work for CRS with four pairs of rollers to manufacture a large-sized, thin-walled, cylindrical part with 6000 mm diameter.
Machine tools are one of the most representative machining systems in manufacturing. The energy consumption of machine tools has been a research hotspot and frontier for green low-carbon manufacturing. However, previous research merely regarded the material removal (MR) energy as useful energy consumption and ignored the useful energy consumed by thermal control (TC) for maintaining internal thermal stability and machining accuracy. In pursuit of energy-efficient, high-precision machining, more attention should be paid to the energy consumption of TC and the coupling relationship between MR and TC. Hence, the cutting energy efficiency model considering the coupling relationship is established based on the law of conservation of energy. An index of energy consumption ratio of TC is proposed to characterize its effect on total energy usage. Furthermore, the heat characteristics are analyzed, which can be adopted to represent machining accuracy. Experimental study indicates that TC is the main energy-consuming process of the precision milling machine tool, which overwhelms the energy consumption of MR. The forced cooling mode of TC results in a 7% reduction in cutting energy efficiency. Regression analysis shows that heat dissipation positively contributes 54.1% to machining accuracy, whereas heat generation negatively contributes 45.9%. This paper reveals the coupling effect of MR and TC on energy efficiency and machining accuracy. It can provide a foundation for energy-efficient, high-precision machining of machine tools.