Cover illustration
Cover Story (Shuguo HU, Changhe LI, Zongming ZHOU, Bo LIU, Yanbin ZHANG, Min YANG, Benkai LI, Teng GAO, Mingzheng LIU, Xin CUI, Xiaoming WANG, Wenhao XU, Y. S. DAMBATTA, Runze LI, Shubham SHARMA. Nanoparticle-enhanced coolants in machining: mechanism, application, and prospects. Front. Mech. Eng., 2023, 18(4): 53)
Nanoparticle-enhanced coolants (NPECs) are increasingly being used in minimum quantit
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The noncontact blade tip timing (BTT) measurement has been an attractive technology for blade health monitoring (BHM). However, the severe undersampled BTT signal causes a significant challenge for blade vibration parameter identification and fault feature extraction. This study proposes a novel method based on the minimum variance distortionless response (MVDR) of the direction of arrival (DoA) estimation for blade natural frequency estimation from the non-uniformly undersampled BTT signals. First, based on the similarity between the general data acquisition model for BTT and the antenna array model in DoA estimation, the circumferentially arranged probes on the casing can be regarded as a non-uniform linear array. Thus, BTT signal reconstruction is converted into the DoA estimation problem of the non-uniform linear array signal. Second, MVDR is employed to address the severe undersampling issue and recover the BTT undersampled signal. In particular, spatial smoothing is innovatively utilized to enhance the estimation of covariance matrix of the BTT signal to avoid ill-condition or singularity, while improving efficiency and robustness. Lastly, numerical simulation and experimental testing are employed to verify the validity of the proposed method. Monte Carlo simulation results suggest that the proposed method behaves better than conventional methods, especially under a lower signal-to-noise ratio condition. Experimental results indicate that the proposed method can effectively overcome the severe undersampling problem of BTT signal induced by physical limitations, and has a strong potential in the field of BHM.
Compacted graphite iron (CGI) is considered to be an ideal diesel engine material with excellent physical and mechanical properties, which meet the requirements of energy conservation and emission reduction. However, knowledge of the microstructure evolution of CGI and its impact on flow stress remains limited. In this study, a new modeling approach for the stress–strain relationship is proposed by considering the strain hardening effect and stored energy caused by the microstructure evolution of CGI. The effects of strain, strain rate, and deformation temperature on the microstructure of CGI during compression deformation are examined, including the evolution of graphite morphology and the microstructure of the pearlite matrix. The roundness and fractal dimension of graphite particles under different deformation conditions are measured. Combined with finite element simulation models, the influence of graphite particles on the flow stress of CGI is determined. The distributions of grain boundary and geometrically necessary dislocations (GNDs) density in the pearlite matrix of CGI under different strains, strain rates, and deformation temperatures are analyzed by electron backscatter diffraction technology, and the stored energy under each deformation condition is calculated. Results show that the proportion and amount of low-angle grain boundaries and the average GNDs density increase with the increase of strain and strain rate and decreased first and then increased with an increase in deformation temperature. The increase in strain and strain rate and the decrease in deformation temperature contribute to the accumulation of stored energy, which show similar variation trends to those of GNDs density. The parameters in the stress–strain relationship model are solved according to the stored energy under different deformation conditions. The consistency between the predicted results from the proposed stress–strain relationship and the experimental results shows that the evolution of stored energy can accurately predict the stress–strain relationship of CGI.
In recent years, the robot industry has developed rapidly, and researchers and enterprises have begun to pay more attention to this industry. People are barely familiar with climbing robots, a kind of special robot. However, from their practical value and scientific research value, climbing robots should studied further. This paper analyzes and summarizes the key technologies of climbing robots, introduces various kinds of climbing robots, and examines their advantages and disadvantages to provide a reference for future researchers. Many countries have studied climbing robots and made some achievements. However, due to the complexity of climbing robots, their climbing efficiency and accuracy need to be further improved. The new structure can improve the climbing efficiency. This paper analyzes climbing robots such as mechanical arms, magnetic attraction, and claws. Optimization methods and path planning can improve the accuracy of work. This paper involves some control methods, including complex intelligent control methods such as behavior-based robot control. This paper also investigates various kinematic planning methods and expounds and summarizes various path planning algorithms, including machine learning and reinforcement learning of artificial intelligence, ant colony algorithm, and other algorithms. Therefore, by analyzing the research status of climbing robots at home and abroad, this paper summarizes three important aspects of climbing robots, namely, structural design, control methods, and climbing strategies, elaborates on the achievements and existing problems of these key technologies, and looks forward to the future development trend and research direction of climbing robots.
Many processes may be used for manufacturing functionally graded materials. Among them, additive manufacturing seems to be predestined due to near-net shape manufacturing of complex geometries combined with the possibility of applying different materials in one component. By adjusting the powder composition of the starting material layer by layer, a macroscopic and step-like gradient can be achieved. To further improve the step-like gradient, an enhancement of the in-situ mixing degree, which is limited according to the state of the art, is necessary. In this paper, a novel technique for an enhancement of the in-situ material mixing degree in the melt pool by applying laser remelting (LR) is described. The effect of layer-wise LR on the formation of the interface was investigated using pure copper and low-alloy steel in a laser powder bed fusion process. Subsequent cross-sectional selective electron microscopic analyses were carried out. By applying LR, the mixing degree was enhanced, and the reaction zone thickness between the materials was increased. Moreover, an additional copper and iron-based phase was formed in the interface, resulting in a smoother gradient of the chemical composition than the case without LR. The Marangoni convection flow and thermal diffusion are the driving forces for the observed effect.
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 internal force antagonism (IFA) problem is one of the most important issues limiting the applications and popularization of redundant parallel robots in industry. Redundant cable-driven parallel robots (RCDPRs) and redundant rigid parallel robots (RRPRs) behave very differently in this problem. To clarify the essence of IFA, this study first analyzes the causes and influencing factors of IFA. Next, an evaluation index for IFA is proposed, and its calculating algorithm is developed. Then, three graphical analysis methods based on this index are proposed. Finally, the performance of RCDPRs and RRPRs in IFA under three configurations are analyzed. Results show that RRPRs produce IFA in nearly all the areas of the workspace, whereas RCDPRs produce IFA in only some areas of the workspace, and the IFA in RCDPRs is milder than that RRPRs. Thus, RCDPRs more fault-tolerant and easier to control and thus more conducive for industrial application and popularization than RRPRs. Furthermore, the proposed analysis methods can be used for the configuration optimization design of RCDPRs.
This study proposes a B-spline-based multiresolution and multimaterial topology optimization (TO) design method for fail-safe structures (FSSs), aiming to achieve efficient and lightweight structural design while ensuring safety and facilitating the postprocessing of topological structures. The approach involves constructing a multimaterial interpolation model based on an ordered solid isotropic material with penalization (ordered-SIMP) that incorporates fail-safe considerations. To reduce the computational burden of finite element analysis, we adopt a much coarser analysis mesh and finer density mesh to discretize the design domain, in which the density field is described by the B-spline function. The B-spline can efficiently and accurately convert optimized FSSs into computer-aided design models. The 2D and 3D numerical examples demonstrate the significantly enhanced computational efficiency of the proposed method compared with the traditional SIMP approach, and the multimaterial TO provides a superior structural design scheme for FSSs. Furthermore, the postprocessing procedures are significantly streamlined.
Nanoparticle-enhanced coolants (NPECs) are increasingly used in minimum quantity lubrication (MQL) machining as a green lubricant to replace conventional cutting fluids to meet the urgent need for carbon emissions and achieve sustainable manufacturing. However, the thermophysical properties of NPEC during processing remain unclear, making it difficult to provide precise guidance and selection principles for industrial applications. Therefore, this paper reviews the action mechanism, processing properties, and future development directions of NPEC. First, the laws of influence of nano-enhanced phases and base fluids on the processing performance are revealed, and the dispersion stabilization mechanism of NPEC in the preparation process is elaborated. Then, the unique molecular structure and physical properties of NPECs are combined to elucidate their unique mechanisms of heat transfer, penetration, and anti-friction effects. Furthermore, the effect of NPECs is investigated on the basis of their excellent lubricating and cooling properties by comprehensively and quantitatively evaluating the material removal characteristics during machining in turning, milling, and grinding applications. Results showed that turning of Ti‒6Al‒4V with multi-walled carbon nanotube NPECs with a volume fraction of 0.2% resulted in a 34% reduction in tool wear, an average decrease in cutting force of 28%, and a 7% decrease in surface roughness Ra, compared with the conventional flood process. Finally, research gaps and future directions for further applications of NPECs in the industry are presented.
Soft arms have shown great application potential because of their flexibility and compliance in unstructured environments. However, soft arms made from soft materials exhibit limited cargo-loading capacity, which restricts their ability to manipulate objects. In this research, a novel soft arm was developed by coupling a rigid origami exoskeleton with soft airbags. The joint module of the soft arm was composed of a deployable origami exoskeleton and three soft airbags. The motion and load performance of the soft arm of the eight-joint module was tested. The developed soft arm withstood at least 5 kg of load during extension, contraction, and bending motions; exhibited bistable characteristics in both fully contracted and fully extended states; and achieved a bending angle of more than 240° and a contraction ratio of more than 300%. In addition, the high extension, contraction, bending, and torsional stiffnesses of the soft arm were experimentally demonstrated. A kinematic-based trajectory planning of the soft arm was performed to evaluate its error in repetitive motion. This work will provide new design ideas and methods for flexible manipulation applications of soft arms.
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.
