To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and l...To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.展开更多
A reasonable process plan is an important basis for implementing wire arc additive and subtractive hybrid manufacturing(ASHM),and a new optimization method is proposed.Firstly,the target parts and machining tools are ...A reasonable process plan is an important basis for implementing wire arc additive and subtractive hybrid manufacturing(ASHM),and a new optimization method is proposed.Firstly,the target parts and machining tools are modeled by level set functions.Secondly,the mathematical model of the additive direction optimization problem is established,and an improved particle swarm optimization algorithm is designed to decide the best additive direction.Then,the two-step strategy is used to plan the hybrid manufacturing alternating sequence.The target parts are directly divided into various processing regions;each processing region is optimized based on manufacturability and manufacturing efficiency,and the optimal hybrid manufacturing alternating sequence is obtained by merging some processing regions.Finally,the method is used to outline the process plan of the designed example model and applied to the actual hybrid manufacturing process of the model.The manufacturing result shows that the method can meet the main considerations in hybrid manufacturing.In addition,the degree of automation of process planning is high,and the dependence on manual intervention is low.展开更多
1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex...1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex and interactive process designs,modern plantwide systems are becoming increasingly sophisticated.The operation of these processes is typically characterized by the complexity of individual units(subsystems)and the intricate interactions between geographically distributed units through networks of material and energy flows,as well as control loops[1].展开更多
Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual ...Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual inspection depends strongly on operator experience,while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines.These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes.This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process.The diagnostic approach leverages Mel-frequency cepstral coefficients(MFCC),and shorttime Fourier transform(STFT)parameters to capture the rotational frequency and harmonic characteristics of the scroll compressor.These parameters enable the extraction of defect-related features even in the presence of background noise.A convolutional neural network(CNN)model was constructed using MFCCs and spectrograms as image inputs.The proposed method was validated using acoustic data collected from compressors operated at a fixed rotational speed under real manufacturing process.The method identified normal operation and three defect types.These results demonstrate the applicability of this method in noise-prone manufacturing environments and suggest its potential for improving product quality,manufacturing reliability and productivity.展开更多
To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much at...To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing.However,the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed.In this perspective paper,we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing,with a focus on chemical product design,process synthesis and design,process control,and process safety and hazards.展开更多
Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nic...Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nickel-based superalloys,pivotal materials for high-temperature bearing components in aeroengines,present significant challenges in the fabrication of complex parts due to their great hardness.Huge attention and rapid progress have been garnered in AM processing of nicklebased superalloys,largely owing to its distinct benefits in the freedom of fabrication and reduced manufacturing lifecycle.Despite extensive research into AM in nickel-based superalloys,the corresponding results and conclusions are scattered attributed to the variety of nickel-based superalloys and complex AM processing parameters.Therefore,there is still a pressing need for a comprehensive and deep understanding of the relationship between the AM processing and microstructures and mechanical performance of nickel-based superalloys.This review introduces the processing characteristics of four primary AM technologies utilized for superalloys and summarizes the microstructures and mechanical properties prior to and post-heat treatments.Additionally,this review presents innovative superalloys specifically accommodated to AM processing and offers insights into the material development and performance improvement,aiming to provide a valuable assessment on AM processing of nickel-based superalloys and an effective guidance for the future research.展开更多
With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking f...With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.展开更多
Electronic 3D printing possesses a remarkable molding ability and convenience in integrated circuits,flexible wearables,and individual automobile requirements.However,traditional 3D printing technology still struggles...Electronic 3D printing possesses a remarkable molding ability and convenience in integrated circuits,flexible wearables,and individual automobile requirements.However,traditional 3D printing technology still struggles to meet the demands of high precision and high efficiency in the process of fabricating a curved surface circuit,particularly achieving precise silver circuit molding on irregular substrates.Here,a high-precision and muti-scaled conformal manufacturing method for silver circuits is presented through the digital light processing(DLP)of ultraviolet-curable silver paste(UV-SP)with adjustable photocuring properties,enabling the successful preparation of micro-scaled conductive structure on the sharply skewed hook face.The minimum modeling depth and width of the cured silver paste can be well controlled to 10 and 88µm,respectively.Compared with traditional printing technology,the printing efficiency of complex patterns has increased by over 70%.The printed silver circuit demonstrates an exceptionally high electrical conductivity,reaching as high as 1.16×10^(7) S/m.Additionally,the UV-SP exhibits significant manufacturing efficiency and superior molding resolution compared to conventional direct ink writing and inkjet printing techniques,thereby contributing to the attainment of high precision and efficiency of conformal and micro-molding manufacturing in sensors,communication antennas,and other electronic devices based on curved substrates.展开更多
Process manufacturing is going through a critical transformation in response to escalating demands for efficiency,sustainability,and intelligent innovation.With process manufacturing being characterized by complex wor...Process manufacturing is going through a critical transformation in response to escalating demands for efficiency,sustainability,and intelligent innovation.With process manufacturing being characterized by complex workflows and high resource consumption,the process manufacturing industry is under mounting pressure to optimize resource utilization,enhance intelligent design,reduce carbon emissions,and address emerging challenges in quality assurance,safety,and information integration.展开更多
VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)has been developed and applied for over twenty-five years,gaining recognition as a prominent multi-criteria decision-making(MCDM)method.Over this period,numer...VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)has been developed and applied for over twenty-five years,gaining recognition as a prominent multi-criteria decision-making(MCDM)method.Over this period,numerous studies have explored its applications,conducted comparative analyses,integrated it with other methods,and proposed various modifications to enhance its performance.This paper aims to delve into the fundamental principles and objectives of VIKOR,which aim to maximize group utility and minimize individual regret simultaneously.However,this study identifies a significant limitation in the VIKOR methodology:its process amplifies the weight of individual regret,and the calculated index values further magnify this effect.This phenomenon not only affects the decision-making balance but also leads to the critical issue of ranking reversal,which undermines the reliability of the results.To address these shortcomings,this paper introduces an enhanced version of VIKOR that mitigates the impact of individual regret while preserving the method’s original objectives.This paper validates the effectiveness of the proposed enhanced VIKOR method using various MCDM approaches,including(1)ten different versions of VIKOR and(2)eleven commonly used MCDM methods.Furthermore,this study confirms that the enhanced VIKOR can be effectively applied across various existing VIKOR versions,broadening its adaptability.A sensitivity analysis is additionally performed by adjusting the criteria weights using the ordered weighted averaging method.An illustrative case study involving the selection of a manufacturing process validates the proposed model.The results show that the proposed model is robust and capable of producing more reliable outcomes.It also demonstrates its practicality and effectiveness in real-world decision-making scenarios.展开更多
Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods ...Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods becomes challenging due to the continuous development and numerous processing parameters involved in steel production.To address this challenge,the application of machine learning methods becomes crucial in establishing complex relationships between manufacturing processes and steel performance.This review begins with a general overview of machine learning methods and subsequently introduces various performance predictions in steel materials.The classification of performance pre-diction was used to assess the current application of machine learning model-assisted design.Several important issues,such as data source and characteristics,intermediate features,algorithm optimization,key feature analysis,and the role of environmental factors,were summarized and analyzed.These insights will be beneficial and enlightening to future research endeavors in this field.展开更多
The pharmaceutical industry is now paying increased attention to continuous manufacturing.While the revolution to continuous and automated manufacturing is deepening in most of the top pharma companies in the world,th...The pharmaceutical industry is now paying increased attention to continuous manufacturing.While the revolution to continuous and automated manufacturing is deepening in most of the top pharma companies in the world,the advancement of automated pharmaceutical continuous manufacturing in China is relatively slow due to some key challenges including the lack of knowledge on the related technologies and shortage of qualified personnels.In this review,emphasis is given to two of the crucial technologies in automated pharmaceutical continuous manufacturing,i.e.,process analytical technology(PAT)and self-optimizing algorithm.Research work published in recent 5 years employing advanced PAT tools and self-optimization algorithms is introduced,which represents the great progress that has been made in automated pharmaceutical continuous manufacturing.展开更多
Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is co...Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.展开更多
Submerged friction stir processing(SFSP)with flowing water was employed to alleviate the porosities and coarse-grained structure introduced by wire-arc manufacturing.As a result,uniform and ultrafine grained(UFG)struc...Submerged friction stir processing(SFSP)with flowing water was employed to alleviate the porosities and coarse-grained structure introduced by wire-arc manufacturing.As a result,uniform and ultrafine grained(UFG)structure with average grain size of 0.83μm was achieved with the help of sharply reduced heat input and holding time at elevated temperature.The optimized UFG structure enabled a superior combination of strength and ductility with high ultimate tensile strength and elongation of 273.17 MPa and 15.39%.Specifically,grain refinement strengthening and decentralized θ(Al_(2)Cu)phase in the sample subjected to SFSP made great contributions to the enhanced strength.In addition,the decrease in residual stresses and removal of pores substantially enhance the ductility.High rates of cooling and low temperature cycling,which are facilitated by the water-cooling environment throughout the machining process,are vital in obtaining superior microstructures.This work provides a new method for developing a uniform and UFG structure with excellent mechanical properties.展开更多
Laser additive manufacturing(LAM)of titanium(Ti)alloys has emerged as a transformative technology with vast potential across multiple industries.To recap the state of the art,Ti alloys processed by two essential LAM t...Laser additive manufacturing(LAM)of titanium(Ti)alloys has emerged as a transformative technology with vast potential across multiple industries.To recap the state of the art,Ti alloys processed by two essential LAM techniques(i.e.,laser powder bed fusion and laser-directed energy deposition)will be reviewed,covering the aspects of processes,materials and post-processing.The impacts of process parameters and strategies for optimizing parameters will be elucidated.Various types of Ti alloys processed by LAM,includingα-Ti,(α+β)-Ti,andβ-Ti alloys,will be overviewed in terms of micro structures and benchmarking properties.Furthermore,the post-processing methods for improving the performance of L AM-processed Ti alloys,including conventional and novel heat treatment,hot isostatic pressing,and surface processing(e.g.,ultrasonic and laser shot peening),will be systematically reviewed and discussed.The review summarizes the process windows,properties,and performance envelopes and benchmarks the research achievements in LAM of Ti alloys.The outlooks of further trends in LAM of Ti alloys are also highlighted at the end of the review.This comprehensive review could serve as a valuable resource for researchers and practitioners,promoting further advancements in LAM-built Ti alloys and their applications.展开更多
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 ...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.展开更多
With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process...With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.展开更多
Acknowledged as a highly versatile manufacturing technology,additive manufacturing holds the potential to transform traditional manufacturing practices in the future.This paper provides a comprehensive review of the l...Acknowledged as a highly versatile manufacturing technology,additive manufacturing holds the potential to transform traditional manufacturing practices in the future.This paper provides a comprehensive review of the latest processes for manufacturing multi-material structural components using additive manufacturing technologies.It discusses the most recent applications of these processes in the fields of automotive,aerospace,biomedical,and dental,and presents a systematic overview of commonly used methods in multi-material additive manufacturing.展开更多
Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
The functionally graded materials(FGMs)are obtained by various processes.Although a few FGMs are obtained naturally,such as oyster,pearl,and bamboo,additive manufacturing(AM),known as 3D printing,is a net-shaped manuf...The functionally graded materials(FGMs)are obtained by various processes.Although a few FGMs are obtained naturally,such as oyster,pearl,and bamboo,additive manufacturing(AM),known as 3D printing,is a net-shaped manufacturing process employed to manufacture complex 3D objects without tools,molds,assembly,and joining.Currently,commercial AM techniques mostly use homogeneous composition with simplified geometric descriptions,employing a single material across the entire component to achieve functional graded additive manufacturing(FGAM),in contrast to multi-material FGAM with heterogeneous structures.FGMs are widely used in various fields due to their mechanical property advantages.Because FGM plays a significant role in the industrial production,the characteristics and mechanical behaviour of FGMs prepared by AM were reviewed.In this review,the research on FGMs and AM over the past 30 years was reviewed,suggesting that future researchers should focus on the application of artificial intelligence and machine learning technologies in industry to optimize the process parameters of different gradient systems.展开更多
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+1 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)。
文摘To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.
基金The National Natural Science Foundation of China(No.52305381)the Natural Science Foundation of Jiangsu Province(No.BK20210351)the Fundamental Research Funds for the Central Universities(No.30923011008).
文摘A reasonable process plan is an important basis for implementing wire arc additive and subtractive hybrid manufacturing(ASHM),and a new optimization method is proposed.Firstly,the target parts and machining tools are modeled by level set functions.Secondly,the mathematical model of the additive direction optimization problem is established,and an improved particle swarm optimization algorithm is designed to decide the best additive direction.Then,the two-step strategy is used to plan the hybrid manufacturing alternating sequence.The target parts are directly divided into various processing regions;each processing region is optimized based on manufacturability and manufacturing efficiency,and the optimal hybrid manufacturing alternating sequence is obtained by merging some processing regions.Finally,the method is used to outline the process plan of the designed example model and applied to the actual hybrid manufacturing process of the model.The manufacturing result shows that the method can meet the main considerations in hybrid manufacturing.In addition,the degree of automation of process planning is high,and the dependence on manual intervention is low.
基金the National Natural Science Foundation of China(NSFC)(62103283)the Australia Research Council’s Discovery Pro-jects Scheme(DP220100355).
文摘1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex and interactive process designs,modern plantwide systems are becoming increasingly sophisticated.The operation of these processes is typically characterized by the complexity of individual units(subsystems)and the intricate interactions between geographically distributed units through networks of material and energy flows,as well as control loops[1].
基金supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00239657)in part by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.RS-2024-00423772)。
文摘Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual inspection depends strongly on operator experience,while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines.These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes.This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process.The diagnostic approach leverages Mel-frequency cepstral coefficients(MFCC),and shorttime Fourier transform(STFT)parameters to capture the rotational frequency and harmonic characteristics of the scroll compressor.These parameters enable the extraction of defect-related features even in the presence of background noise.A convolutional neural network(CNN)model was constructed using MFCCs and spectrograms as image inputs.The proposed method was validated using acoustic data collected from compressors operated at a fixed rotational speed under real manufacturing process.The method identified normal operation and three defect types.These results demonstrate the applicability of this method in noise-prone manufacturing environments and suggest its potential for improving product quality,manufacturing reliability and productivity.
基金funding support from the National Natural Science Foundation of China(62394343)the Program of Introducing Talents of Discipline to Universities(the 111 Project)(B17017).
文摘To achieve sustainable development goals and the requirements of a circular economy,a new class of intelligent computer-aided methods and tools is needed.Artificial intelligence(AI)techniques have been gaining much attention due to their ability to provide options to tackle the challenges we are currently facing.However,the successful application of AI to solve problems of current interest requires AI to be integrated with associated process systems engineering methods and tools that are already available or being developed.In this perspective paper,we highlight the use of a collection of process systems engineering methods and tools augmented by AI techniques to solve problems related to process manufacturing,with a focus on chemical product design,process synthesis and design,process control,and process safety and hazards.
基金financially supported by the National Key R&D Program of China(No.2021YFB3702301)the National Natural Science Foundation of China(No.52101068]+2 种基金the China Postdoctoral Science Foundation[No.2022T150342]the Postdoctoral International Exchange Program[No.YJ20210129]the Shuimu Tsinghua Scholar Program(No.2020SM100)
文摘Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nickel-based superalloys,pivotal materials for high-temperature bearing components in aeroengines,present significant challenges in the fabrication of complex parts due to their great hardness.Huge attention and rapid progress have been garnered in AM processing of nicklebased superalloys,largely owing to its distinct benefits in the freedom of fabrication and reduced manufacturing lifecycle.Despite extensive research into AM in nickel-based superalloys,the corresponding results and conclusions are scattered attributed to the variety of nickel-based superalloys and complex AM processing parameters.Therefore,there is still a pressing need for a comprehensive and deep understanding of the relationship between the AM processing and microstructures and mechanical performance of nickel-based superalloys.This review introduces the processing characteristics of four primary AM technologies utilized for superalloys and summarizes the microstructures and mechanical properties prior to and post-heat treatments.Additionally,this review presents innovative superalloys specifically accommodated to AM processing and offers insights into the material development and performance improvement,aiming to provide a valuable assessment on AM processing of nickel-based superalloys and an effective guidance for the future research.
基金the financial support of the National Key Research and Development Program of China(2021YFE0112800)EU RISE project OPTIMAL(101007963).
文摘With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.
基金supported by the National Natural Science Foundation of China(Nos.51972079 and 52302062)the National Key Research and Development Program of China(Nos.2022YFB370630202 and 2022YFB3706305).
文摘Electronic 3D printing possesses a remarkable molding ability and convenience in integrated circuits,flexible wearables,and individual automobile requirements.However,traditional 3D printing technology still struggles to meet the demands of high precision and high efficiency in the process of fabricating a curved surface circuit,particularly achieving precise silver circuit molding on irregular substrates.Here,a high-precision and muti-scaled conformal manufacturing method for silver circuits is presented through the digital light processing(DLP)of ultraviolet-curable silver paste(UV-SP)with adjustable photocuring properties,enabling the successful preparation of micro-scaled conductive structure on the sharply skewed hook face.The minimum modeling depth and width of the cured silver paste can be well controlled to 10 and 88µm,respectively.Compared with traditional printing technology,the printing efficiency of complex patterns has increased by over 70%.The printed silver circuit demonstrates an exceptionally high electrical conductivity,reaching as high as 1.16×10^(7) S/m.Additionally,the UV-SP exhibits significant manufacturing efficiency and superior molding resolution compared to conventional direct ink writing and inkjet printing techniques,thereby contributing to the attainment of high precision and efficiency of conformal and micro-molding manufacturing in sensors,communication antennas,and other electronic devices based on curved substrates.
文摘Process manufacturing is going through a critical transformation in response to escalating demands for efficiency,sustainability,and intelligent innovation.With process manufacturing being characterized by complex workflows and high resource consumption,the process manufacturing industry is under mounting pressure to optimize resource utilization,enhance intelligent design,reduce carbon emissions,and address emerging challenges in quality assurance,safety,and information integration.
基金supported by the National Science and Technology Council(NSTC)Taiwan(Grant No.NSTC 113-2222-E-029-005),with additional computational resources provided by the projectThe work of Josef Jablonsky was supprted by the Faculty of Informatics and Statistics,Prague University of Economics and Business。
文摘VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)has been developed and applied for over twenty-five years,gaining recognition as a prominent multi-criteria decision-making(MCDM)method.Over this period,numerous studies have explored its applications,conducted comparative analyses,integrated it with other methods,and proposed various modifications to enhance its performance.This paper aims to delve into the fundamental principles and objectives of VIKOR,which aim to maximize group utility and minimize individual regret simultaneously.However,this study identifies a significant limitation in the VIKOR methodology:its process amplifies the weight of individual regret,and the calculated index values further magnify this effect.This phenomenon not only affects the decision-making balance but also leads to the critical issue of ranking reversal,which undermines the reliability of the results.To address these shortcomings,this paper introduces an enhanced version of VIKOR that mitigates the impact of individual regret while preserving the method’s original objectives.This paper validates the effectiveness of the proposed enhanced VIKOR method using various MCDM approaches,including(1)ten different versions of VIKOR and(2)eleven commonly used MCDM methods.Furthermore,this study confirms that the enhanced VIKOR can be effectively applied across various existing VIKOR versions,broadening its adaptability.A sensitivity analysis is additionally performed by adjusting the criteria weights using the ordered weighted averaging method.An illustrative case study involving the selection of a manufacturing process validates the proposed model.The results show that the proposed model is robust and capable of producing more reliable outcomes.It also demonstrates its practicality and effectiveness in real-world decision-making scenarios.
基金supported by the National Natural Science Foundation of China (No.51701061)the Natural Science Foundation of Hebei Province (Nos.E2023202047 and E2021202075)+1 种基金the Key-Area R&D Program of Guangdong Province (No.2020B0101340004)Guangdong Academy of Science (2021GDASYL-20210102002).
文摘Steels are widely used as structural materials,making them essential for supporting our lives and industries.However,further improving the comprehensive properties of steel through traditional trial-and-error methods becomes challenging due to the continuous development and numerous processing parameters involved in steel production.To address this challenge,the application of machine learning methods becomes crucial in establishing complex relationships between manufacturing processes and steel performance.This review begins with a general overview of machine learning methods and subsequently introduces various performance predictions in steel materials.The classification of performance pre-diction was used to assess the current application of machine learning model-assisted design.Several important issues,such as data source and characteristics,intermediate features,algorithm optimization,key feature analysis,and the role of environmental factors,were summarized and analyzed.These insights will be beneficial and enlightening to future research endeavors in this field.
基金supported by the National Natural Science Foundation of China(Nos.21808059,21878088,and 21476077)Key Project of the Shanghai Science and Technology Committee(No.18DZ1112703)。
文摘The pharmaceutical industry is now paying increased attention to continuous manufacturing.While the revolution to continuous and automated manufacturing is deepening in most of the top pharma companies in the world,the advancement of automated pharmaceutical continuous manufacturing in China is relatively slow due to some key challenges including the lack of knowledge on the related technologies and shortage of qualified personnels.In this review,emphasis is given to two of the crucial technologies in automated pharmaceutical continuous manufacturing,i.e.,process analytical technology(PAT)and self-optimizing algorithm.Research work published in recent 5 years employing advanced PAT tools and self-optimization algorithms is introduced,which represents the great progress that has been made in automated pharmaceutical continuous manufacturing.
基金financially supported by the Technology Development Fund of China Academy of Machinery Science and Technology(No.170221ZY01)。
文摘Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.
文摘Submerged friction stir processing(SFSP)with flowing water was employed to alleviate the porosities and coarse-grained structure introduced by wire-arc manufacturing.As a result,uniform and ultrafine grained(UFG)structure with average grain size of 0.83μm was achieved with the help of sharply reduced heat input and holding time at elevated temperature.The optimized UFG structure enabled a superior combination of strength and ductility with high ultimate tensile strength and elongation of 273.17 MPa and 15.39%.Specifically,grain refinement strengthening and decentralized θ(Al_(2)Cu)phase in the sample subjected to SFSP made great contributions to the enhanced strength.In addition,the decrease in residual stresses and removal of pores substantially enhance the ductility.High rates of cooling and low temperature cycling,which are facilitated by the water-cooling environment throughout the machining process,are vital in obtaining superior microstructures.This work provides a new method for developing a uniform and UFG structure with excellent mechanical properties.
基金financially supported by the 2022 MTC Young Individual Research Grants under Singapore Research,Innovation and Enterprise(RIE)2025 Plan(No.M22K3c0097)the Natural Science Foundation of US(No.DMR-2104933)the sponsorship of the China Scholarship Council(No.202106130051)。
文摘Laser additive manufacturing(LAM)of titanium(Ti)alloys has emerged as a transformative technology with vast potential across multiple industries.To recap the state of the art,Ti alloys processed by two essential LAM techniques(i.e.,laser powder bed fusion and laser-directed energy deposition)will be reviewed,covering the aspects of processes,materials and post-processing.The impacts of process parameters and strategies for optimizing parameters will be elucidated.Various types of Ti alloys processed by LAM,includingα-Ti,(α+β)-Ti,andβ-Ti alloys,will be overviewed in terms of micro structures and benchmarking properties.Furthermore,the post-processing methods for improving the performance of L AM-processed Ti alloys,including conventional and novel heat treatment,hot isostatic pressing,and surface processing(e.g.,ultrasonic and laser shot peening),will be systematically reviewed and discussed.The review summarizes the process windows,properties,and performance envelopes and benchmarks the research achievements in LAM of Ti alloys.The outlooks of further trends in LAM of Ti alloys are also highlighted at the end of the review.This comprehensive review could serve as a valuable resource for researchers and practitioners,promoting further advancements in LAM-built Ti alloys and their applications.
基金support from the National Science and Technology Council of Taiwan(Contract Nos.111-2221 E-011081 and 111-2622-E-011019)the support from Intelligent Manufacturing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan,which is a Featured Areas Research Center in Higher Education Sprout Project of Ministry of Education(MOE),Taiwan(since 2023)was appreciatedWe also thank Wang Jhan Yang Charitable Trust Fund(Contract No.WJY 2020-HR-01)for its financial support.
文摘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.
基金supported by the National Natural Science Foundation of China(62225302,623B2014,and 62173023).
文摘With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.
基金supported by the Youth Science Fund Program under National Natural Science Foundation of China(No.52205248).
文摘Acknowledged as a highly versatile manufacturing technology,additive manufacturing holds the potential to transform traditional manufacturing practices in the future.This paper provides a comprehensive review of the latest processes for manufacturing multi-material structural components using additive manufacturing technologies.It discusses the most recent applications of these processes in the fields of automotive,aerospace,biomedical,and dental,and presents a systematic overview of commonly used methods in multi-material additive manufacturing.
基金funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
文摘The functionally graded materials(FGMs)are obtained by various processes.Although a few FGMs are obtained naturally,such as oyster,pearl,and bamboo,additive manufacturing(AM),known as 3D printing,is a net-shaped manufacturing process employed to manufacture complex 3D objects without tools,molds,assembly,and joining.Currently,commercial AM techniques mostly use homogeneous composition with simplified geometric descriptions,employing a single material across the entire component to achieve functional graded additive manufacturing(FGAM),in contrast to multi-material FGAM with heterogeneous structures.FGMs are widely used in various fields due to their mechanical property advantages.Because FGM plays a significant role in the industrial production,the characteristics and mechanical behaviour of FGMs prepared by AM were reviewed.In this review,the research on FGMs and AM over the past 30 years was reviewed,suggesting that future researchers should focus on the application of artificial intelligence and machine learning technologies in industry to optimize the process parameters of different gradient systems.
