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.

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.

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.

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.

Cutting quality and efficiency have always been important indicators of glass laser cutting. Laser scanning modes have two kinds, namely, the spiral and concentric circle scanning modes. These modes can achieve high-performance hole cutting of thick solar float glass using a 532-nm nanosecond laser. The mechanism of the glass laser cutting under these two different scanning modes has been described. Several experiments are conducted to explore the effect of machining parameters on cutting efficiency and quality under these two scanning modes. Results indicate that compared with the spiral scanning mode, the minimum area of edge chipping (218340 µm²) and the minimum Ra (3.01 µm) in the concentric circle scanning mode are reduced by 9.4% and 16.4% respectively. Moreover, the best cutting efficiency scanning mode is 14.2% faster than that in the spiral scanning mode. The best parameter combination for the concentric circle scanning mode is as follows: Scanning speed: 2200 mm/s, number of inner circles: 6, and circle spacing: 0.05 mm. This parameter combination reduces the chipping area and sidewall surface roughness by 8.8% and 9.6% respectively at the same cutting efficiency compared with the best spiral processing parameters. The range of glass processing that can be achieved in the concentric circle scanning mode is wider than that in the spiral counterpart. The analyses of surface topography, white spots, microstructures, and sidewall surface element composition are also performed. The study concluded that the concentric circle scanning mode shows evident advantages in the performance of solar float glass hole cutting.

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.

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.

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.

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.

This paper presents a dynamic and static error transfer model and uncertainty evaluation method for a high-speed variable-slit system based on a two- dimensional orthogonal double-layer air-floating guide rail structure. The motion accuracy of the scanning blade is affected by both the moving component it is attached to and the moving component of the following blade during high-speed motion. First, an error transfer model of the high-speed variable-slit system is established, and the influence coefficients are calculated for each source of error associated with the accuracy of the blade motion. Then, the maximum range of each error source is determined by simulation and experiment. Finally, the uncertainty of the blade displacement measurement is evaluated using the Monte Carlo method. The proposed model can evaluate the performance of the complex mechanical system and be used to guide the design.

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.

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).

Surface accuracy directly affects the surface quality and performance of mechanical parts. Circular hole, especially spatial non-planar hole set is the typical feature and working surface of mechanical parts. Compared with traditional machining methods, additive manufacturing (AM) technology can decrease the surface accuracy errors of circular holes during fabrication. However, an accuracy error may still exist on the surface of circular holes fabricated by AM due to the influence of staircase effect. This study proposes a surface accuracy optimization approach for mechanical parts with multiple circular holes for AM based on triangular fuzzy number (TFN). First, the feature lines on the manifold mesh are extracted using the dihedral angle method and normal tensor voting to detect the circular holes. Second, the optimal AM part build orientation is determined using the genetic algorithm to optimize the surface accuracy of the circular holes by minimizing the weighted volumetric error of the part. Third, the corresponding weights of the circular holes are calculated with the TFN analytic hierarchy process in accordance with the surface accuracy requirements. Lastly, an improved adaptive slicing algorithm is utilized to reduce the entire build time while maintaining the forming surface accuracy of the circular holes using digital twins via virtual printing. The effectiveness of the proposed approach is experimentally validated using two mechanical models.

Studies on determining and analyzing the crushing response of tubular structures are of significant interest, primarily due to their relation to safety. Several aspects of tubular structures, such as geometry, material, configuration, and hybrid structure, have been used as criteria for evaluation. In this review, a comprehensive analysis of the important findings of extensive research on understanding the crushing response of thin-walled tubular structures is presented. Advancements in thin-walled structures, including multi-cell tube, honeycomb and foam-filled, multi wall, and functionally graded thickness tubes, are also discussed, focusing on their energy absorption ability. An extensive review of experimentation and numerical analysis used to extract the deformation behavior of materials, such as aluminum and steel, against static and dynamic loadings are also provided. Several tube shapes, such as tubes of uniform and nonuniform (tapered) cross sections of circular, square, and rectangular shapes, have been used in different studies to identify their efficacy. Apart from geometric and loading parameters, the effects of fabrication process, heat treatment, and triggering mechanism on initiating plastic deformation, such as cutouts and grooves, on the surface of tubular structures are discussed.

Microwave induced plasma torches find wide applications in material and chemical analysis. Investigation of a coaxial electrode microwave induced plasma (CE–MIP) torch is conducted in this study, making it available for glass surface modification and polishing. A dedicated nozzle is designed to inject secondary gases into the main plasma jet. This study details the adaptation of a characterisation process for CE–MIP technology. Microwave spectrum analysis is used to create a polar plot of the microwave energy being emitted from the coaxial electrode, where the microwave energy couples with the gas to generate the plasma jet. Optical emission spectroscopy analysis is also employed to create spatial maps of the photonic intensity distribution within the plasma jet when different additional gases are injected into it. The CE–MIP torch is experimentally tested for surface energy modification on glass where it creates a super-hydrophilic surface.

Carbon fiber reinforced polymer (CFRP) composites have excellent mechanical properties, specifically, high specific stiffness and strength. However, most CFRP composites exhibit poor impact resistance. To overcome this limitation, this study presents a new plain-woven CFRP composite embedded with superelastic shape memory alloy (SMA) wires. Composite specimens are fabricated using the vacuum-assisted resin injection method. Drop-weight impact tests are conducted on composite specimens with and without SMA wires to evaluate the improvement of impact resistance. The material models of the CFRP composite and superelastic SMA wire are introduced and implemented into a finite element (FE) software by the explicit user-defined material subroutine. FE simulations of the drop-weight impact tests are performed to reveal the superelastic deformation and debonding failure of the SMA inserts. Improvement of the energy absorption capacity and toughness of the SMA-CFRP composite is confirmed by the comparison results.

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.

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.

A novel hybrid perfusion manipulator (HPM) with five degrees of freedom (DOFs) is introduced by combining the 5PUS-PRPU (P, R, U, and S represent prismatic, revolute, universal, and spherical joint, respectively) parallel mechanism with the 5PRR reconfigurable base to enhance the perfusion efficiency of the large-scale spherical honeycomb thermal protection layer. This study mainly presents the dimensional synthesis of the proposed HPM. First, the inverse kinematics, including the analytic expression of the rotation angles of the U joint in the PUS limb, is obtained, and mobility analysis is conducted based on screw theory. The Jacobian matrix of 5PUS-PRPU is also determined with screw theory and used for the establishment of the objective function. Second, a global and comprehensive objective function (GCOF) is proposed to represent the Jacobian matrix’s condition number. With the genetic algorithm, dimensional synthesis is conducted by minimizing GCOF subject to the given variable constraints. The values of the designed variables corresponding to different configurations of the reconfigurable base are then obtained. Lastly, the optimal structure parameters of the proposed 5-DOF HPM are determined. Results show that the HPM with the optimized parameters has an enlarged orientation workspace, and the maximum angle of the reconfigurable base is decreased, which is conducive to improving the overall stiffness of HPM.

Crawling robots have elicited much attention in recent years due to their stable and efficient locomotion. In this work, several crawling robots are developed using two types of soft pneumatic actuators (SPAs), namely, an axial elongation SPA and a dual bending SPA. By constraining the deformation of the elastomeric chamber, the SPAs realize their prescribed motions, and the deformations subjected to pressures are characterized with numerical models. Experiments are performed for verification, and the results show good agreement. The SPAs are fabricated by casting and developed into crawling robots with 3D-printing connectors. Control schemes are presented, and crawling tests are performed. The speeds predicted by the numerical models agree well with the speeds in the experiments.

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.

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.

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.

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.

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.

Biological knowledge is becoming an important source of inspiration for developing creative solutions to engineering design problems and even has a huge potential in formulating ideas that can help firms compete successfully in a dynamic market. To identify the technologies and methods that can facilitate the development of biologically inspired creative designs, this research briefly reviews the existing biological-knowledge-based theories and methods and examines the application of biological-knowledge-inspired designs in various fields. Afterward, this research thoroughly examines the four dimensions of key technologies that underlie the biologically inspired design (BID) process. This research then discusses the future development trends of the BID process before presenting the conclusions.

Rapid prototyping (RP) or layered manufacturing (LM) technologies have been extensively used to manufacture prototypes composed mainly of plastics, polymers, paper, and wax due to the short product development time and low costs of these technologies. However, such technologies, with the exception of selective laser melting and sintering, are not used to fabricate metallic products because of the resulting poor life, short cycle, poor surface finish, and low structural integrity of the fabricated parts. The properties endowed by these parts do not match those of functional parts. Therefore, extensive research has been conducted to develop new additive manufacturing (AM) technologies by extending existing RP technologies. Several AM technologies have been developed for the fabrication of metallic objects. These technologies utilize materials, such as Ni-, Al-, and Ti-based alloys and stainless steel powders, to fabricate high-quality functional components. The present work reviews the type of materials used in laser-based AM processes for the manufacture of metallic products. The advantages and disadvantages of processes and different materials are summarized, and future research directions are discussed in the final section. This review can help experts select the ideal type of process or technology for the manufacturing of elements composed of a given alloy or material (Ni, Ti, Al, Pb, and stainless steel).

Passive variable stiffness joints have unique advantages over active variable stiffness joints and are currently eliciting increased attention. Existing passive variable stiffness joints rely mainly on sensors and special control algorithms, resulting in a bandwidth-limited response speed of the joint. We propose a new passive power-source-free stiffness-self-adjustable mechanism that can be used as the elbow joint of a robot arm. The new mechanism does not require special stiffness regulating motors or sensors and can realize large-range self-adaptive adjustment of stiffness in a purely mechanical manner. The variable stiffness mechanism can automatically adjust joint stiffness in accordance with the magnitude of the payload, and this adjustment is a successful imitation of the stiffness adjustment characteristics of the human elbow. The response speed is high because sensors and control algorithms are not needed. The variable stiffness principle is explained, and the design of the variable stiffness mechanism is analyzed. A prototype is fabricated, and the associated hardware is set up to validate the analytical stiffness model and design experimentally.

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.

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.