Micro/nano flow chemistry by Beyond Limits Manufacturing

In September 2018,we proposed the cutting-edge concept of“Beyond Limits Manufacturing”(BLM).BLM technology is based on the three-dimensional inner engraving or precise outer engraving of ultra-fast laser,to invent micro/nano scale flow chips or devices,which makes it possible for the microform,integration,economy,safety,high efficiency,green and intelligence of research,development and manufacturing process,so as to realize transformational manufacturing in the era of Industry 4.0.In this paper,we reviewed the representative results we made in the field of micro/nano flow chemistry during the implementation of the BLM major project(December 2019 to August 2023),and discussed its application prospects in micro/nano flow chemistry.

A review on stress determination and control in metal-based additive manufacturing

Metal additive manufacturing(MAM)is an emerging and disruptive technology that builds three-dimensional(3D)components by adding layer-upon-layer of metallic materials.The complex cyclic thermal history and highly localized energy can produce large temperature gradients,which will,in turn,lead to compressive and tensile stress during the MAM process and eventually result in residual stress.Being an issue of great concern,residual stress,which can cause distortion,delamination,cracking,etc.,is considered a key mechanical quantity that affects the manufacturing quality and service performance of MAM parts.In this review paper,the ongoing work in the field of residual stress determination and control for MAM is described with a particular emphasis on the experimental measurement/control methods and numerical models.We also provide insight on what still requires to be achieved and the research opportunities and challenges.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

A facile strategy for tuning the density of surface-grafted biomolecules for melt extrusion-based additive manufacturing applications

Melt extrusion-based additive manufacturing(ME-AM)is a promising technique to fabricate porous scaffolds for tissue engi-neering applications.However,most synthetic semicrystalline polymers do not possess the intrinsic biological activity required to control cell fate.Grafting of biomolecules on polymeric surfaces of AM scaffolds enhances the bioactivity of a construct;however,there are limited strategies available to control the surface density.Here,we report a strategy to tune the surface density of bioactive groups by blending a low molecular weight poly(ε-caprolactone)5k(PCL5k)containing orthogonally reactive azide groups with an unfunctionalized high molecular weight PCL75k at different ratios.Stable porous three-dimensional(3D)scaf-folds were then fabricated using a high weight percentage(75 wt.%)of the low molecular weight PCL 5k.As a proof-of-concept test,we prepared films of three different mass ratios of low and high molecular weight polymers with a thermopress and reacted with an alkynated fluorescent model compound on the surface,yielding a density of 201-561 pmol/cm^(2).Subsequently,a bone morphogenetic protein 2(BMP-2)-derived peptide was grafted onto the films comprising different blend compositions,and the effect of peptide surface density on the osteogenic differentiation of human mesenchymal stromal cells(hMSCs)was assessed.After two weeks of culturing in a basic medium,cells expressed higher levels of BMP receptor II(BMPRII)on films with the conjugated peptide.In addition,we found that alkaline phosphatase activity was only significantly enhanced on films contain-ing the highest peptide density(i.e.,561 pmol/cm^(2)),indicating the importance of the surface density.Taken together,these results emphasize that the density of surface peptides on cell differentiation must be considered at the cell-material interface.Moreover,we have presented a viable strategy for ME-AM community that desires to tune the bulk and surface functionality via blending of(modified)polymers.Furthermore,the use of alkyne-azide“click”chemistry enables spatial control over bioconjugation of many tissue-specific moieties,making this approach a versatile strategy for tissue engineering applications.

Advances in Compact Manufacturing for Shape and Performance Controllability of Large-scale Components-A Review

Research on compact manufacturing technology for shape and performance controllability of metallic components can reanze the simplification and high-reliability of manufacturing process on the premise of satisfying the requirement of macro/micro-structure. It is not only the key paths in improving performance, saving material and energy, and green manufacturing of components used in major equipments, but also the challenging subjects in frontiers of advanced plastic forming. To provide a novel horizon for the manufacturing in the critical components is significant. Focused on the high-performance large-scale components such as bearing rings, flanges, railway wheels, thick-walled pipes, etc, the conventional processes and their developing situations are summarized. The existing problems including multi-pass heating, wasting material and energy, high cost and high-emission are discussed, and the present study unable to meet the manufacturing in high-quality components is also pointed out. Thus, the new techniques related to casting-rolling compound precise forming of rings, compact manufacturing for duplex-metal composite rings, compact manufacturing for railway wheels, and casting-extruding continuous forming of thick-walled pipes are introduced in detail, respectively. The corresponding research contents, such as casting ring blank, hot ring rolling, near solid-state pressure forming, hot extruding, are elaborated. Some findings in through-thickness microstructure evolution and mechanical properties are also presented. The components produced by the new techniques are mainly characterized by fine and homogeneous grains. Moreover, the possible directions for fin＇ther development of those techniques are suggested. Finally, the key scientific problems are first proposed. All of these results and conclusions have reference value and guiding significance for the integrated control of shape and performance in advanced compact manufacturing.

Fluid-driven artificialmuscles: bio-design, manufacturing, sensing, control, and applications

Developing artificial muscles that can replace biological muscles to accomplish various tasks iswhat we have long been aiming for.Recent advances in flexible materials and 3D printing technology greatly promote the development of artificial muscle technology.A variety of flexible material-based artificial muscles that are driven by different external stimuli,including pressure,voltage,light,magnetism,temperature,etc.,have been developed.Among these,fluid-driven artificial muscles(FAMs),which can convert the power of fluid(gas or liquid)into the force output and displacement of flexible materials,are the most widely used actuation methods for industrial robots,medical instruments,and human-assisted devices due to their simplicity,excellent safety,large actuation force,high energy efficiency,and low cost.Herein,the bio-design,manufacturing,sensing,control,and applications of FAMs are introduced,including conventional pneumatic/hydraulic artificial muscles and several innovative artificial muscles driven by functional fluids.What’s more,the challenges and future directions of FAMs are discussed.

Deadlock-free Supervisor Design for Robotic Manufacturing Cells With Uncontrollable and Unobservable Events

In this paper,a deadlock prevention policy for robotic manufacturing cells with uncontrollable and unobservable events is proposed based on a Petri net formalism.First,a Petri net for the deadlock control of such systems is defined.Its admissible markings and first-met inadmissible markings(FIMs)are introduced.Next,place invariants are designed via an integer linear program(ILP)to survive all admissible markings and prohibit all FIMs,keeping the underlying system from reaching deadlocks,livelocks,bad markings,and the markings that may evolve into them by firing uncontrollable transitions.ILP also ensures that the obtained deadlock-free supervisor does not observe any unobservable transition.In addition,the supervisor is guaranteed to be admissible and structurally minimal in terms of both control places and added arcs.The condition under which the supervisor is maximally permissive in behavior is given.Finally,experimental results with the proposed method and existing ones are given to show its effectiveness.

Role of laser scan strategies in defect control,microstructural evolution and mechanical properties of steel matrix composites prepared by laser additive manufacturing

Steel matrix composites(SMCs)reinforced with WC particles were fabricated via selective laser melting(SLM)by employing various laser scan strategies.A detailed relationship between the SLM strategies,defect formation,microstructural evolution,and mechanical properties of SMCs was established.The laser scan strategies can be manipulated to deliberately alter the thermal history of SMC during SLM processing.Particularly,the involved thermal cycling,which encompassed multiple layers,strongly affected the processing quality of SMCs.Sshaped scan sequence combined with interlayer offset and orthogonal stagger mode can effectively eliminate the metallurgical defects and retained austenite within the produced SMCs.However,due to large thermal stress,microcracks that were perpendicular to the building direction formed within the SMCs.By employing the checkerboard filling(CBF)hatching mode,the thermal stress arising during SLM can be significantly reduced,thus preventing the evolution of interlayer microcracks.The compressive properties of fabricated SMCs can be tailored at a high compressive strength(~3031.5 MPa)and fracture strain(~24.8%)by adopting the CBF hatching mode combined with the optimized scan sequence and stagger mode.This study demonstrates great feasibility in tuning the mechanical properties of SLM-fabricated SMCs without varying the set energy input,e.g.,laser power and scanning speed.

Integrated Product and Process Control for Sustainable Semiconductor Manufacturing

Semiconductor fabrication is a manufacturing sequence with hundreds of sophisticated unit operations and it is always challenged by strategy development for ensuring the yield of defect-free products.In this paper,an advanced control strategy through integrating product and process control is established.The proposed multiscale scheme contains three layers for coordinated equipment control,process control and product quality control.In the upper layer,online control performance assessment is applied to reduce the quality variation and maximize the overall product performance （OPP）.It serves as supervisory control to update the recipe of the process controller in the middle layer.The process controller is designed as an exponentially weighted moving average （EWMA） run-to-run controller to reject disturbances,such as process shift,drift and tool worn out,that are exerted to the op-eration.The equipment in the process is individually controlled to maintain its optimal operational status and maximize the overall equipment effectiveness （OEE）,based on the set point given by the process controller.The ef-ficacy of the proposed integrated control scheme is demonstrated through case studies,where both the OPP （for product） and the OEE （for equipment） are enhanced.

Regulations for the Manufacture and Control of Live Poliovirus Vaccine: International Experience and China’s Path

The eradication of poliomyelitis is a landmark achievement in the history of public health, providing strong protection for children’s health. The introduction of the Chinese Regulations for the Manufacture and Control of Live Poliovirus Vaccine is a prerequisite and safeguard for the large-scale production and use of domestically produced live poliovirus vaccines, serving as an indispensable component of vaccine safety. This article, based on archival documents, letters, collections of essays, and oral interviews, examines the historical experience of the development of Chinese Regulations for the Manufacture and Control of Live Poliovirus Vaccine. It contends that the emphasis on localization and the active engagement in international cooperation are critical factors in the swift introduction of Chinese Regulations for the Manufacture and Control of Live Poliovirus Vaccine.

Optimal Production Control of Hybrid Manufacturing/Remanufacturing Failure-Prone Systems under Diffusion-Type Demand

The problem of production control for a hybrid manufacturing/remanufacturing system under uncertainty is analyzed. Two sources of uncertainty are considered: machines are subject to random breakdowns and repairs, and demand level is modeled as a diffusion type stochastic process. Contrary to most of studies where the demand level is considered constant and fewer results where the demand is modeled as a Poisson process with few discrete levels and exponentially distributed switching time, the demand is modeled here as a diffusion type process. In particular Wiener and Ornstein-Uhlenbeck processes for cumulative demands are analyzed. We formulate the stochastic control problem and develop optimality conditions for it in the form of Hamilton-Jacobi-Bellman (HJB) partial differential equations (PDEs). We demonstrate that HJB equations are of the second order contrary to the case of constant demand rate (corresponding to the average demand in our case), where HJB equations are linear PDEs. We apply the Kushner-type finite difference scheme and the policy improvement procedure to solve HJB equations numerically and show that the optimal production policy is of hedging-point type for both demand models we have introduced, similarly to the known case of a constant demand. Obtained results allow to compute numerically the optimal production policy in hybrid manufacturing/ remanufacturing systems taking into account the demand variability, and also show that Kushner-type discrete scheme can be successfully applied for solving underlying second order HJB equations.

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart

Tool condition monitoring(TCM)is a key technology for intelligent manufacturing.The objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing costs.Recently,an innovative TCM approach based on sensor data modelling and model frequency analysis has been proposed.Different from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process model.Then,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health conditions.In order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency analysis.The feature dimension is reduced by principal component analysis first.Then the moving average strategy is exploited to generate monitoring variables and overcome the effects of noises.The milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end mills.The results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

Carbon Emission Modeling and Analysis in Manufacturing Process for Numerical Control Machine Tools

Reducing carbon emissions( CEs) is the urgent demand all over the world. In order to realize the low-carbon numerical control( NC) machining, the evaluation model of a part's manufacturing carbon emission with NC machine tools was built by considering the influences of the cutting tool geometrical parameters.The manufacturing CEs were produced by electric power,cutting tools,and cutting fluid consumed in manufacturing process. The parameters of cutting tools affected not only the CEs,but also the machining quality. Then the actual constraint models of the machine performance,machining quality were given in order to optimize the cutting parameters and achieve the low-CEs. Finally,a case was given to analyze the influences of the cutting tool angles on the manufacturing CEs. The results show that the CEs decrease as the rake angle and edge angle increase under the constraints of the machine specifications and machining quality.

Design and Implementation of Distributed Numerical Control in Flexible Manufacturing System

To monitor, control and manage the work process of computer numerical control machine tools in a flexible manufacturing system (FMS) effectively, the distributed numerical coutrol (distributed-NC) software should be innovated with the characteristics of modularization and reconfiguration. In this paper, firstly, distributed-NC functions in the FMS environment are described. Then, we present a design and development method of the real time distributed-NC that is on the basis of the re-coufigurable software and hardware platform and with an object-oriented model concept. Finally, to verify the proposed method, the distributed-NC software has been implemented in VC++ 6.0 and has been tested in connection with the different physical flexible manufacturing shops.

Interaction of Units and Research Methods of Control Performances of Distributed Manufacturing Information System

In order to analyze the function demand of the distributed manufacturing information system as well as its control demand, and eliminate information ambiguity among system units to integrate semantics, the abstract Agent model and computational structure of each unit was presented based on flexible coupling automata. The autonomy of each unit was investigated in this foundation. The system unit was described using the Web Ontology Language (OWL) ontology. And the system semantics was also integrated. On these basics the communication among the system units was analyzed with an example of interaction between a machine and a warehouse. The control performances of information system units were investigated using Boolean matrix as a substitute for traditional process in RW theory, which reduced the computational complexity. This work established the foundation for the demand analysis, design and development of the distributed manufacture information system.

Management and Control of Outsourcing in Dispersed Network Manufacturing Systems

The trend of economic globalisation and advances in i nformation technology has led to the emergence of dispersed manufacturing system s as a form of the virtual organisation. New manufacturing strategy pays more at tention to the management of the total value chain and therefore puts emphasis o n outsourcing. In fact, outsourcing is an efficient way of utilizing available r esources and has become one key aspect of the manufacturing strategy. Improved d ecision and organization on outsourcing will result in cost production and short er lead-times. However, most concepts and practice of traditional outsourcing do not adapt to t he changing environment and meet increasing performance requirements. On the oth er hand, virtual organisations might display instability between pure outsourcin g and establishing alliance. Balance and trade-off between independent agents a nd creating alliance are thus required. Therefore, the purpose of this paper is to develop a model to support decision-making, management and control on outsou rcing in a dispersed network manufacturing system and to discuss several key iss ues that are relevant to the relationship between the agents of the network. Dev elopment of the model will deploy Applied System Theory and will be built on fou ndations of earlier research on industrial management such the theories of Outso urcing, Order Entry Points, Design of Organisations and Logistic Control. The is sues that will be addressed in this paper are: · The selection of suppliers and co-makers; · Communication between suppliers and clients; · The mechanisms for profit-sharing between agents; · The product data management to integrate the knowledge of the different agent s into product design. Industrial companies will benefit from this research by the practical methods an d implementation extending their business models beyond concepts for outsourcing and alliances. Additionally, the exploration will lead to proactive contributio n of manufacturing during engineering, which would improve management and contro l of dispersed manufacturing systems.

Implementation of Hierarchical and Distributed Control for Discrete Event Robotic Manufacturing Systems

The large scale and complex manufacturing systems have a hierarchical structure where a system is composed several lines with some stations and each station also have several machines and so on. In such a hierarchical structure, the controllers are geographically distributed according to their physical structure. So it is desirable to realize the hierarchical and distributed control. In this paper, a methodology is presented using Petri nets for hierarchical and distributed control. The Petri net representation of discrete event manufacturing processes is decomposed and distributed into the machine controllers, which are coordinated through communication between the coordinator and machine controllers so that the decomposed transitions fire at the same time. Implementation of a hierarchical and distributed control system is described for an example robotic manufacturing system. The demonstrations show that the proposed system can be used as an effective tool for consistent modeling and control of large and complex manufacturing systems.

Investigation on Occupational Disease Inductive Factors ofA utomobile Manufacturing Enterprises and Evaluation of Control Effect

The automobile manufacturing industry has a complex process and various processes,so it has buried many occupational-disease-related safety hazards and is the industry of high incidence of occupational diseases.Accurate identification and effective control of hazards in automobile manufacturing are an important guarantee for effective prevention of occupational disease.In order to investigate the types and status of occupational-disease-inductive factors of a car manufacturer,and evaluate the control effect,the general situation of the enterprise is collected from August 2016 to October 2017.And then the targeted treatment,including management measures,engineering protection and personal protection.The occupational-disease-inductive factors that are mainly produced in automobile manufacturing are productive dust,chemical substance and physical factors.

Bifurcation Control, Manufacturing Planning and Formation Control

The paper consists of three topics on control theory and engineering applications, namely bifurcation control, manufacturing planning, and formation control. For each topic, we summarize the control problem to be addressed and some key ideas used in our recent research. Interested readers are referred to related publications for more details. Each of the three topics in this paper is technically independent from the other ones. However, all three parts together reflect the recent research activities of the first author, jointly with other researchers in different fields.

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.