A deep learning-enabled human-cyber-physical fusion method towards human-robot collaborative assembly

Human-robot collaborative (HRC) assembly has become popular in recent years. It takes full advantage of the strength, repeatability and accuracy of robots and the high-level cognition, flexibility and adaptability of humans to achieve an ergonomic working environment with better overall productivity. However, HRC assembly is still in its infancy nowadays. How to ensure the safety and efficiency of HRC assembly while reducing assembly failures caused by human errors is challenging. To address the current challenges, this paper proposes a novel human-cyber-physical assembly system (HCPaS) framework, which combines the powerful perception and control capacity of digital twin with the virtual-reality interaction capacity of augmented reality (AR) to achieve a safe and efficient HRC environment. Based on the framework, a deep learning-enabled fusion method of HCPaS is proposed from the perspective of robot-level fusion and part-level fusion. Robot-level fusion perceives the pose of robots with the combination of PointNet and iterative closest point (ICP) algorithm, where the status of robots together with their surroundings could be registered into AR environment to improve the human's cognitive ability of complex assembly environment, thus ensuring the safe HRC assembly. Part-level fusion recognizes the type and pose of parts being assembled with a parallel network that takes an extended Pixel-wise Voting Network (PVNet) as the base architecture, on which assembly sequence/process information of the part could be registered into AR environment to provide smart guidance for manual work to avoid human errors. Eventually, experimental results demonstrate the effectiveness and efficiency of the approach.     

Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem

Integrated process planning and scheduling (IPPS) is of great significance for modern manufacturing enterprises to achieve high efficiency in manufacturing and maximize resource utilization. In this paper, the integration strategy and solution method of IPPS problem are deeply studied, and an improved genetic algorithm based on multi-layer encoding (IGA-ML) is proposed to solve the IPPS problem. Firstly, considering the interaction ability between the two subsystems and the multi-flexibility characteristics of the IPPS problem, a new multi-layer integrated encoding method is designed. The encoding method includes feature layer, operation layer, machine layer and scheduling layer, which respectively correspond to the four sub-problems of IPPS problem, which provides a premise for a more flexible and deeper exploration in the solution space. Then, based on the coupling characteristics of process planning and shop scheduling, six evolutionary operators are designed to change the four-layer coding interdependently and independently. Two crossover operators change the population coding in the unit of jobs, and search the solution space globally. The four mutation operators change the population coding in the unit of gene and search the solution space locally. The six operators are used in series and iteratively optimized to ensure a fine balance between the global exploration ability and the local exploitation ability of the algorithm. Finally, performance of IGA-ML is verified by testing on 44 examples of 14 benchmarks. The experimental results show that the proposed algorithm can find better solutions (better than the optimal solutions found so far) on some problems, and it is an effective method to solve the IPPS problem with the maximum completion time as the optimization goal.     

Efficient skeleton curve-guided five-axis sweep scanning based on optimal traversing of multiple connected skeleton curves

The five-axis sweep scanning approach is an emerging surface inspection technology which could tremendously boost the inspection efficiency through working in the way of continuous scanning. While inspecting the surfaces with multiple connected skeleton curves, the topological complexity brings conflict between achieving efficient inspection and working in continuous manner. Recently, a skeleton curve-guided five-axis sweep scanning method was proposed to tackle this problem but the resulting inspection path has to inspect the entire surface in a round-trip way. The manner of round-trip inspection pulls down the entire inspection efficiency and should be avoided as much as possible. In this paper, we present an improved skeleton curve-guided five-axis sweep scanning method to generate a more efficient five-axis scanning path for the surface with multiple connected skeleton curves. The proposed method starts from the framework of existing skeleton curve-guided five-axis sweep scanning method. Under the unique kinematic requirements of efficient five-axis sweep scanning, an integer linear programming optimization approach is utilized to optimally connect the inspection paths on independent surface patches and form a shorter skeleton curve-based sweep scanning path as compared with the existing skeleton curve-guided five-axis sweep scanning method. The resulting inspection path is composed of the single-pass inspection for most of the surface and the round-trip inspection for a small part of the surface. The comparison experiments are conducted on two surfaces with multiple connected skeleton curves. Experiment results show that the proposed method significantly outperforms the method provided by the leading commercial software Apexblade and the original skeleton curve-guided five-axis sweep scanning method.     

Sparse identification for ball-screw drives considering position-dependent dynamics and nonlinear friction

Establishing accurate dynamic models in a form that is suitable for integration with model-based control methods, is of great significance for further improving the dynamic motion control precision of ball-screw drives. However, due to the nonlinear time-varying factors such as position-dependent dynamics and nonlinear friction disturbance, it is difficult to model the dynamic characteristics of ball-screw drives accurately, concisely and efficiently. To overcome this challenge, a sparse identification method for ball-screw drives is proposed. Ball-screw drives are modeled as discrete-time linear parameter-varying systems under nonlinear friction disturbance, and two types of dictionary function libraries are designed to represent the position-dependent dynamics and nonlinear friction respectively. After constructing the regression form of the system model, a stepwise sparse regression policy is proposed to solve all the coefficients of dictionary functions. The proposed method is verified in both simulation and real environments. The results both show that by the proposed method, an accurate and linearizable dynamic model of ball-screw drives can be identified only using the data from only one global random excitation experiment covering the working stroke.     

Digital twin-driven manufacturing equipment development

Currently, expectations of shorter time-to-market and improved product performance are placing greater demands on manufacturing companies. However, the optimization and redesign work between the design stage and the prototype design and manufacturing stage in the traditional product development process lengthens the required product development cycle time (which lasts up to several years in extreme cases). The manufacturing phase for the physical prototype of the product is especially time-consuming and costly. The above reasons make the common product development process increasingly unable to meet the demands of market needs. Motivated by this need, the digital twin (DT)-driven manufacturing equipment (ME) development method is studied in this paper. This method contains three main core elements of the design method based on axiomatic design (AD) theory, the construction of DT models related to ME development, and DT-based validation analysis. The advantage of this method is that it can incorporate the physical prototype manufacturing stage into the digital space with the high-fidelity model provided by the DT technology, which ensures the confidentiality of the design scheme validation while freeing it from the physical prototype stage. This avoids the cost of physical prototyping, shortens the product development cycle, and improves the efficiency of new ME development. At the end of this paper, a case study of the development of a virtual machining dynamic performance test bench (VM-TB) is carried out to show the implementation flow of this proposed method, and its operability and effectiveness are verified.     

Thermally Responsive Fibers for Versatile Thermoactivated Protective Fabrics

Smart textiles with good mechanical adaptability play an important role in personal protection, health monitoring, and aerospace applications. However, most of the reported thermally responsive polymers has long response time and poor processability, comfort, and wearability. Skin-core structures of thermally responsive fibers with multiple commercial fiber cores and temperature-responsive hydrogel skins are designed and fabricated, which exhibit rapid mechanical adaptability, good thermohardening, and thermal insulation. This universal method enables tight bonding between various commercial fiber cores and hydrogel skins via specific covalently anchored networks. At room temperature, prepared fibers show softness, flexibility, and skin compatibility similar to those of ordinary fibers. As temperature rises, smart fibers become hard, rigid, and self-supporting. The modulus of hydrogel skin increases from 304% to 30883%, showing good mechanoadaptability and impact resistance owing to the synergy between hydrophobic interactions and ionic bonding. Moreover, this synergistic effect leads to an increase in heat absorption, and fibers exhibit good thermal insulation, which reduces the contact temperature of the body surface by ≈25 °C under the external temperature of 95 °C, effectively preventing thermal burns. Notably, the active mechanoadaptability of these smart fibers using conductive fibers as cores is demonstrated. This study provides feasibility for fabricating environmentally adaptive intelligent textiles.     

A rear-end collision risk assessment model based on drivers’ collision avoidance process under influences of cell phone use and gender—A driving simulator based study

Driver’s collision avoidance performance has a direct link to the collision risk and crash severity. Previous studies demonstrated that the distracted driving, such as using a cell phone while driving, disrupted the driver’s performance on road. This study aimed to investigate the manner and extent to which cell phone use and driver’s gender affected driving performance and collision risk in a rear-end collision avoidance process. Forty-two licensed drivers completed the driving simulation experiment in three phone use conditions: no phone use, hands-free, and hand-held, in which the drivers drove in a car-following situation with potential rear-end collision risks caused by the leading vehicle’s sudden deceleration. Based on the experiment data, a rear-end collision risk assessment model was developed to assess the influence of cell phone use and driver’s gender. The cell phone use and driver’s gender were found to be significant factors that affected the braking performances in the rear-end collision avoidance process, including the brake reaction time, the deceleration adjusting time and the maximum deceleration rate. The minimum headway distance between the leading vehicle and the simulator during the rear-end collision avoidance process was the final output variable, which could be used to measure the rear-end collision risk and judge whether a collision occurred. The results showed that although cell phone use drivers took some compensatory behaviors in the collision avoidance process to reduce the mental workload, the collision risk in cell phone use conditions was still higher than that without the phone use. More importantly, the results proved that the hands-free condition did not eliminate the safety problem associated with distracted driving because it impaired the driving performance in the same way as much as the use of hand-held phones. In addition, the gender effect indicated that although female drivers had longer reaction time than male drivers in critical situation, they were more quickly in braking with larger maximum deceleration rate, and they tended to keep a larger safety margin with the leading vehicle compared to male drivers. The findings shed some light on the further development of advanced collision avoidance technologies and the targeted intervention strategies about cell phone use while driving.     

Low-valence ion addition induced more compact passive films on nickel-copper nano-coatings

Ni-Cu nano-coatings were prepared by pulsed electroplating technique in the baths containing various amount of boric acid. Their microstructure, morphologies and corrosion resistance were characterized in detail. The addition of boric acid strongly influences on the microstructure of the Ni-Cu coatings. The coating with a grain size of 130 nm, obtained from the bath containing 35 g L⁻¹ boric acid, shows the highest corrosion resistance. This is attributed to the low-valence Cu ion (Cu⁺) additions in nickel oxide, which could significantly decrease the oxygen ion vacancy density in the passive film to form a more compact passive film. The higher Cu⁺ additions and the lower diffusivity of point defects (D₀) are responsible for the formation of more compact passive film on the coating obtained from the bath with 35 g L⁻¹ boric acid.     

Facile synthesis of rutile TiO2/carbon nanosheet composite from MAX phase for lithium storage

Titanium oxide (TiO₂), with excellent cycling stability and low volume expansion, is a promising anode material for lithium-ion battery (LIB), which suffers from low electrical conductivity and poor rate capability. Combining nano-sized TiO₂ with conductive materials is proved an efficient method to improve its electrochemical properties. Here, rutile TiO₂/carbon nanosheet was obtained by calcinating MAX (Ti₃AlC₂) and Na₂CO₃ together and water-bathing with HCl. The lamellar carbon atoms in MAX are converted to 2D carbon nanosheets with urchin-like rutile TiO₂ anchored on. The unique architecture can offer plentiful active sites, shorten the ion diffusion distance and improve the conductivity. The composite exhibits a high reversible capacity of 247 mA h g⁻¹, excellent rate performance (38 mA h g⁻¹ at 50 C) and stable cycling performance (0.014% decay per cycle during 2000 cycles) for lithium storage.     

Prediction of stable high-pressure structures of tantalum nitride TaN2

Structure searches based on a combination of first-principles calculations and a particle swarm optimization technique unravel two new stable high-pressure structures (C2/m and Cmce) for TaN₂. The structural features, mechanical properties, formation enthalpies, electronic structure, and phase diagram of TaN₂ are fully investigated. Being mechanically and dynamically stable, the two phases could be made metastable experimentally at ambient conditions.