Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the world.However,there is no consensus on why and what is Industry 5.0 yet.In this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development.We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies.Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

Parallel Manufacturing for Industrial Metaverses:A New Paradigm in Smart Manufacturing

To tackle the complexity of human and social factors in manufacturing systems, parallel manufacturing for industrial metaverses is proposed as a new paradigm in smart manufacturing for effective and efficient operations of those systems, where Cyber-Physical-Social Systems(CPSSs) and the Internet of Minds(Io M) are regarded as its infrastructures and the "Artificial systems", "Computational experiments"and "Parallel execution"(ACP) method is its methodological foundation for parallel evolution, closed-loop feedback, and collaborative optimization. In parallel manufacturing, social demands are analyzed and extracted from social intelligence for product R&D and production planning, and digital workers and robotic workers perform the majority of the physical and mental work instead of human workers, contributing to the realization of low-cost, high-efficiency and zero-inventory manufacturing. A variety of advanced technologies such as Knowledge Automation(KA), blockchain, crowdsourcing and Decentralized Autonomous Organizations(DAOs) provide powerful support for the construction of parallel manufacturing, which holds the promise of breaking the constraints of resource and capacity, and the limitations of time and space. Finally, the effectiveness of parallel manufacturing is verified by taking the workflow of customized shoes as a case,especially the unmanned production line named Flex Vega.

Integrated Inspection of QoM, QoP, and QoS for AOI Industries in Metaverses

With the rapid development of information technologies such as digital twin, extended reality, and blockchain,the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection(Auto QI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing(Qo M), Quality of Products, Quality of Processes(Qo P), Quality of Systems, and Quality of Services(Qo S). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottomup inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection,we propose Quality Transformers to complete detection task,while federated learning is integrated to regulate data sharing.In summary, we point out the development directions of quality inspection under metaverse tide.

3D Characteristic Diagram of Acoustically Induced Surface Vibration with Different Landmines Buried

The 3D characteristic diagram of acoustically induced surface vibration was employed to study the influence of different buried landmines on the acoustic detection signal. By using the vehicular experimental system for acoustic landmine detection and the method of scanning detection, the 3D characteristic diagrams of surface vibration were measured when different objects were buried underground, including big plastic landmine, small plastic landmine, big metal landmine and bricks. The results show that, under the given conditions, the surface vibration amplitudes of big plastic landmine, big metal landmine, small plastic landmine and bricks decrease in turn. The 3D characteristic diagrams of surface vibration can be used to further identify the locations of buried landmines.

Research on monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration

In this paper,we proposed a monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration for missile-borne detector.Through iteration,the proposed algorithm automatically selects the echo signal of isolated strong-scattering points from the receiving echo signal data to accurately estimate the actual optimal monopulse response curve(MRC) of the same distance range,and we applied optimal MRC to realize the azimuth self-focusing in the process of imaging.We use real-time echo data to perform error correction for obtaining the optimal MRC,and the azimuth angulation accuracy may reach the optimum at a certain distance dimension.We experimentally demonstrate the validity,reliability and high performance of the proposed algorithm.The azimuth angulation accuracy may reach up to ten times of the detection beam-width.The simulation experiments have verified the feasibility of this strategy,with the average height measurement error being 7.8%.In the out-field unmanned aerial vehicle(UAV) tests,the height measurement error is less than 25 m,and the whole response time can satisfy the requirements of a missile-borne detector.

Realization of proportional navigation and lateral acceleration autopilot for endoatmospheric kinetic interceptor with pulse thrusters

The guidance and control strategy for endoatmospheric kinetic interceptor controlled by lateral pulse thrusters was detailed.The pulse thruster control system was firstly proposed.The sample-and-hold approach was introduced to design a novel lateral acceleration autopilot on the basis of traditional two-loop topology.Combined with proportional navigation guidance law and the novel autopilot,the overall ballistic trajectory was presented and examined.Simulation results show that the pulse thruster control strategy can greatly improve the control system response speed and the maximal acceleration capability for realizing kinetic kill interception.

Novel assisted cough system based on simulating cough airflow dynamics

Cough is a defensive behavior that protects the respiratory system from infection and clears airway secretions.Cough airflow dynamics have been analyzed by a variety of mathematical and experimental tools.In this paper,the cough airflow dynamics of 42 subjects were obtained and analyzed.An identification model based on piecewise Gauss function for cough airflow dynamics is proposed through the dimensionless method,which could achieve over 90%identification accuracy.Meanwhile,an assisted cough system based on pneumatic flow servo system is presented.The vacuum situation and feedback control are used to increase the simulated peak cough flow rate,which are important for airway secretion clearance and to avoid airway collapse,respectively.The simulated cough peak flow could reach 5 L/s without the external assistance such as manual pressing,patient cooperation and other means.Finally,the backstepping control is developed to generate a simulated cough airflow that closely mimics the natural cough airflow of humans.The assisted cough system opens up wide opportunities of practical application in airway secretion clearance for critically ill patients with COVID 2019 and other pulmonary diseases.

Design and application of wireless signal strength measurement system on the near-ground

The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.

Design for Distributed Integrated Electronic System BIT

The design model of system-level and module-level BIT system are established based on hierarchical BIT design. The unifi ed data structure of PBIT, CBIT, IBIT test item including detection function and recovery function are designed. Fault tree theory is introduced to BIT system and the PBIT, CBIT, IBIT universal automatic traversal fault tree test algorithm is designed, which can realize the bottom-up node test of integrated electronic system and top-down fault diagnosis. In a integrated electronic system application show that the scheme of online health monitoring and fault diagnosis based on BIT is reasonable and feasible, easy to maintain, and can improve integrated electronic system testability and reliability.

All laser direct writing process for temperature sensor based on graphene and silver

A highly sensitive temperature sensing array is prepared by all laser direct writing(LDW)method,using laser induced silver(LIS)as electrodes and laser induced graphene(LIG)as temperature sensing layer.A finite element analysis(FEA)photothermal model incorporating a phase transition mechanism is developed to investigate the relationship between laser parameters and LIG properties,providing guidance for laser processing parameters selection with laser power of 1–5 W and laser scanning speed(greater than 50 mm/s).The deviation of simulation and experimental data for widths and thickness of LIG are less than 5%and 9%,respectively.The electrical properties and temperature responsiveness of LIG are also studied.By changing the laser process parameters,the thickness of the LIG ablation grooves can be in the range of 30–120μm and the resistivity of LIG can be regulated within the range of 0.031–67.2Ω・m.The percentage temperature coefficient of resistance(TCR)is calculated as−0.58%/°C.Furthermore,the FEA photothermal model is studied through experiments and simulations data regarding LIS,and the average deviation between experiment and simulation is less than 5%.The LIS sensing samples have a thickness of about 14μm,an electrical resistivity of 0.0001–100Ω・m is insensitive to temperature and pressure stimuli.Moreover,for a LIS-LIG based temperature sensing array,a correction factor is introduced to compensate for the LIG temperature sensing being disturbed by pressure stimuli,the temperature measurement difference is decreased from 11.2 to 2.6°C,indicating good accuracy for temperature measurement.

Low-Carbon Economic Operation Optimization of Mine Integrated Energy System Considering Energy Supply Uncertainty

Mine integrated energy system(MIES)can promote the uilliation of derived energy and achieve multi-energy complementation and ecological protection.Now it gradually becomes an important focus for scientific carbon reduction and carbon neutrality.To reduce the impact of uncertain prediction differences on the system during the process of using mine derived energy,a low-carbon economic operation strategy of MIES considering energy supply uncertainty is developed in this paper.Firstly,based on the basic structure of energy flow in MIES,the energy-carbon flow framework of MIES is established for the low-carbon operation requirements.Secondly,considering carbon emission constraints,the low-carbon economic operation optimization model(LEOOM)is bullt for MIES to minimize operation cost and carbon emission.Finally,multiple uncertainties of the system are modeled and analyzed by using the robust model under the risk aversion strategy of information gap decision theory(IGDT),and a model conversion method is designed to optimize the low-carbon economic operation model.The simulation results under three scenarios demonstrate that compared to the existed economic dispatching models,the proposed model achieves a 30%reduction in carbon emission while the operational cost of MIES only is increased by 2.1%.The model ffiently mitigates the carbon emission of the system,and the proposed uncertain treatment strategy can significantly improve the robustness of obtained operation plans.

A data-driven approach for modeling and predicting the thrust force of a tunnel boring machine

Thrust prediction of a tunnel boring machine(TBM)is crucial for the life span of disc cutters,cost forecasting,and its design optimization.Many factors affect the thrust of a TBM.The rock pressure on the shield,advance speed,and cutter water pressure will all have a certain impact.In addition,geological conditions and other random factors will also influence the thrust and greatly increase the difficulty of modeling it,seriously affecting the efficiency of tunnel excavation.To overcome these challenges,this paper establishes a thrust prediction model for the TBM based on the combination of on-site quality record data and surrogate model technology.Firstly,the thrust composition and influencing factors are analyzed and the thrust is modeled using a surrogate model based on field data.After main factor screening based on the Morris method,the accuracy of the surrogate model is greatly improved.The Kriging model with the highest accuracy is selected to model the thrust and predict the thrust of the unexcavated section.The results show that the thrust model has better thrust prediction by selecting similar conditions for modeling and reasonably increasing modeling samples.The thrust prediction method of TBM based on the combination of field data and surrogate model can accurately predict the dynamic thrust of the load and can also accurately estimate its statistical characteristics and effectively improve the excavation plan.

Uncertainty evaluation for bearing fatigue property of CFRP double-lap,single-bolt joints

The considerable uncertainty in mechanical properties of composite bolted joints not only prevents advanced composite materials from efficient applications,but also threatens the safety and reliability of the aircraft structures.In this paper,the uncertainty in bearing fatigue properties of a CFRP double-lap,single-bolt joint was evaluated by combing a Progressive Fatigue Damage Model(PFDM)with the interval analysis method.In the PFDM,a residualstrain-based gradual material degradation model and a strain-based fatigue failure criterion were combined with a micromechanics-based sudden material degradation model to predict fatigue properties of the joint.Based on the interval analysis,the key uncertain parameters,which were firstly picked out from eighteen structural parameters of the joint,were described by estimated intervals,and the envelope cases were determined to estimate the lower and upper bounds of fatigue properties of the joint.The predicted results have the same tendency with the experimental results in literatures,which indicates that the PFDM combined with the interval analysis shows potential in efficiently evaluating the fatigue reliability of the complex bolted joints with an adequate accuracy.

Experimental measurement of acoustically induced surface vibration with different soil conditions

Experimental measurement is performed to investigate the acoustically induced surface vibration with different soil conditions. Using the method of scanning detection and analyzing the three-dimensional （3D） char- acteristic diagram of surface vibration, the influence of soil properties, such as porosity and humidity, upon the signal of acoustically induced surface vibration is measured. The experimental results show that the surface vibration redu- ces with the decrease of soil porosity and reduces a little with the increase of soil humidity; and with a big plastic landmine buried, the surface vibration enhances signifi- cantly. It indicates that the signal of acoustically induced surface vibration mainly depends on soil porosity and mechanical effect of buried objects.

Effects of microcracks on the poroelastic behaviors of a single osteon

It has been suggested that microcracks do play a key role in the triggering of the bone remodeling process.In order to evaluate the influence of microcracks on the poroelastic behaviors of an osteon,a finite element model is established and investigated by using the Comsol Multiphysics software.The findings show that the presence of a microcrack in the osteon wall strongly modifies(enlarges)its local fluid pressure and velocity.Especially,the pressure and velocity amplitudes produced in the microcracked region are larger than those of the non-cracked region.Thus,this study can also be used for proposing a likely mechanism that bone can sense the changes of surrounding mechanical environments.

A novel architecture for ahead branch prediction

In theory, branch predictors with more compli- cated algorithms and larger data structures provide more accurate predictions. Unfortunately, overly large structures and excessively complicated algorithms cannot be implemented because of their long access delay. To date, many strategies have been proposed to balance delay with accuracy, but none has completely solved the issue. The architecture for ahead branch prediction （A2BP） separates traditional pre- dictors into two parts. First is a small table located at the front-end of the pipeline, which makes the prediction brief enough even for some aggressive processors. Second, oper- ations on complicated algorithms and large data structures for accurate predictions are all moved to the back-end of the pipeline. An effective mechanism is introduced for ahead branch prediction in the back-end and small table update in the front. To substantially improve prediction accuracy, an indirect branch prediction algorithm based on branch history and target path （BHTP） is implemented in AZBE Experiments with the standard performance evaluation corpora- tion （SPEC） benchmarks on gem5/SimpleScalar simulators demonstrate that AzBP improves average performance by 2.92% compared with a commonly used branch target bufferbased predictor. In addition, indirect branch misses with the BHTP algorithm are reduced by an average of 28.98% com- pared with the traditional algorithm.