Advancements in machine learning for material design and process optimization in the field of additive manufacturing

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.

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.

Exploration and Application Practice of Energy Conservation Theory and Method in Steel Manufacturing Process System

This article briefly discusses the theoretical basis and overall goals of energy conservation in the steel manufacturing process system.It is proposed that in the process of implementing system energy conservation,it is necessary to fully recognize and utilize the characteristics and functional advantages of the steel manufacturing process,pay more attention to energy quality,firmly grasp the overall goal of system optimization,focus on the integrated optimization of gas,steam,and waste heat systems,and propose the idea of constructing a"steel chemi-cal gas electricity heating cooling multi generation system".Based on practice,the main principles,models,and effects of implementing systematic energy conservation in steel enterprises have been proposed.

Challenges and opportunities in the production of magnesium parts by directed energy deposition processes

Mg-alloys have gained considerable attention in recent years for their outstanding properties such as lightweight,high specific strength,and corrosion resistance,making them attractive for applications in medical,aerospace,automotive,and other transport industries.However,their widespread application is hindered by their low formability at room temperature due to limited slip systems.Cast Mg-alloys have low mechanical properties due to the presence of casting defects such as porosity and anisotropy in addition to the high scrap.While casting methods benefit from established process optimization techniques for these problems,additive manufacturing methods are increasingly replacing casting methods in Mg alloys as they provide more precise control over the microstructure and allow specific grain orientations,potentially enabling easier optimization of anisotropy properties in certain applications.Although metal additive manufacturing(MAM)technology also results in some manufacturing defects such as inhomogeneous microstructural evolution and porosity and additively manufactured Mg alloy parts exhibit lower properties than the wrought parts,they in general exhibit superior properties than the cast counterparts.Thus,MAM is a promising technique to produce Mg alloy parts.Directed energy deposition processes,particularly wire arc directed energy deposition(WA-DED),have emerged as an advantageous additive manufacturing(AM)technique for metallic materials including magnesium alloys,offering advantages such as high deposition rates,improved material efficiency,and reduced production costs compared to subtractive processes.However,the inherent challenges associated with magnesium,such as its high reactivity and susceptibility to oxidation,pose unique hurdles in the application of this technology.This review paper delves into the progress made in the application of DED technology to Mg-alloys,its challenges,and prospects.Furthermore,the predominant imperfections,notably inhomogeneous microstructure evolution and porosity,observed in Mg-alloy components manufactured through DED are discussed.Additionally,the preventive measures implemented to counteract the formation of these defects are explored.

Mechanical and corrosion properties of full liquid phase sintered WE43 magnesium alloy specimens fabricated via binder jetting additive manufacturing

This study investigates full liquid phase sintering as a process of fabrication parts from WE43(Mg-4wt.%Y-3wt.%RE-0.7wt.%Zr)alloy using binder jetting additive manufacturing(BJAM).This fabrication process is being developed for use in producing structural or biomedical devices.Specifically,this study focused on achieving a near-dense microstructure with WE43 Mg alloy while substantially reducing the duration of sintering post-processing after BJAM part rendering.The optimal process resulted in microstructure with 2.5%porosity and significantly reduced sintering time.The improved sintering can be explained by the presence of Y_(2)O_(3)and Nd_(2)O_(3)oxide layers,which form spontaneously on the surface of WE43 powder used in BJAM.These layers appear to be crucial in preventing shape distortion of the resulting samples and in enabling the development of sintering necks,particularly under sintering conditions exceeding the liquidus temperature of WE43 alloy.Sintered WE43 specimens rendered by BJAM achieved significant improvement in both corrosion resistance and mechanical properties through reduced porosity levels related to the sintering time.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

An Interpretable Light Attention-Convolution-Gate Recurrent Unit Architecture for the Highly Accurate Modeling of Actual Chemical Dynamic Processes

To equip data-driven dynamic chemical process models with strong interpretability,we develop a light attention–convolution–gate recurrent unit(LACG)architecture with three sub-modules—a basic module,a brand-new light attention module,and a residue module—that are specially designed to learn the general dynamic behavior,transient disturbances,and other input factors of chemical processes,respectively.Combined with a hyperparameter optimization framework,Optuna,the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process.The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models,including the feedforward neural network,convolution neural network,long short-term memory(LSTM),and attention-LSTM.Moreover,compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1,the LACG parameters are demonstrated to be interpretable,and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM.This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling,paving a route to intelligent manufacturing.

Laser-based bionic manufacturing

Over millions of years of natural evolution,organisms have developed nearly perfect structures and functions.The self-fabrication of organisms serves as a valuable source of inspiration for designing the next-generation of structural materials,and is driving the future paradigm shift of modern materials science and engineering.However,the complex structures and multifunctional integrated optimization of organisms far exceed the capability of artificial design and fabrication technology,and new manufacturing methods are urgently needed to achieve efficient reproduction of biological functions.As one of the most valuable advanced manufacturing technologies of the 21st century,laser processing technology provides an efficient solution to the critical challenges of bionic manufacturing.This review outlines the processing principles,manufacturing strategies,potential applications,challenges,and future development outlook of laser processing in bionic manufacturing domains.Three primary manufacturing strategies for laser-based bionic manufacturing are elucidated:subtractive manufacturing,equivalent manufacturing,and additive manufacturing.The progress and trends in bionic subtractive manufacturing applied to micro/nano structural surfaces,bionic equivalent manufacturing for surface strengthening,and bionic additive manufacturing aiming to achieve bionic spatial structures,are reported.Finally,the key problems faced by laser-based bionic manufacturing,its limitations,and the development trends of its existing technologies are discussed.

An Edge-Fog-Cloud Computing-Based Digital Twin Model for Prognostics Health Management of Process Manufacturing Systems

The prognostics health management(PHM)fromthe systematic viewis critical to the healthy continuous operation of processmanufacturing systems(PMS),with different kinds of dynamic interference events.This paper proposes a three leveled digital twinmodel for the systematic PHMof PMSs.The unit-leveled digital twinmodel of each basic device unit of PMSs is constructed based on edge computing,which can provide real-time monitoring and analysis of the device status.The station-leveled digital twin models in the PMSs are designed to optimize and control the process parameters,which are deployed for the manufacturing execution on the fog server.The shop-leveled digital twin maintenancemodel is designed for production planning,which gives production instructions fromthe private industrial cloud server.To cope with the dynamic disturbances of a PMS,a big data-driven framework is proposed to control the three-level digital twin models,which contains indicator prediction,influence evaluation,and decisionmaking.Finally,a case study with a real chemical fiber system is introduced to illustrate the effectiveness of the digital twin model with edge-fog-cloud computing for the systematic PHM of PMSs.The result demonstrates that the three-leveled digital twin model for the systematic PHM in PMSs works well in the system’s respects.

An Industrial Perspective on Magnesium Alloy Wheels: A Process and Material Design

Light weights wheels improve vehicle performance with respect to road handling, cornering as well providing fuel economy and reduced greenhouse gas emissions. Aluminum wheels are currently used in many models and are produced usually by low pressure assisted gravity casting. Important property requirements are fatigue strength, pressure tightness, tensile strength, impact resistance, and corrosion resistance. Many attempts have been made to convert aluminum road wheels to magnesium. Race cars and some of the high end models (Porsche, Ferrari, etc.) have used magnesium wheels. These wheels have been gravity cast or forged. Viable corrosion protection systems have been developed and magnesium wheels have been used with success on these models. To use magnesium on more modest models is a challenge due to cost issues. Higher productivity casting processes or more cost effective coating systems need to be utilized. The project consists of selecting magnesium alloys for road wheels, examining the possible cost effective casting processes and corrosion protection systems, evaluating the cost per one wheel and comparing it to aluminum wheel costs. The wheels will also be compared with respect to fatigue and impact properties, pressure tightness, and corrosion.

Simulation Research on the Effect of Spreading Process Parameters on the Quality of Lunar Regolith Powder Bed in Additive Manufacturing

Lunar surface additive manufacturing with lunar regolith is a key step in in-situ resource utilization.The powder spreading process is the key process,which has a major impact on the quality of the powder bed and the precision of molded parts.In this study,the discrete element method(DEM)was adopted to simulate the powder spreading process with a roller.The three powder bed quality indicators,including the molding layer offset,voidage fraction,and surface roughness,were established.Besides,the influence of the three process parameters,which are roller’s translational speed,rotational speed,and powder spreading layer thickness on the powder bed quality indicators was also analyzed.The results show that with the reduction of the powder spreading layer thickness and the increase of the rotational speed,the offset increased significantly;when the translational speed increased,the offset first increased and then decreased,which resulted in an extreme value;with the increase of the layer thickness and the decrease of the translational speed,the values for voidage fraction and surface roughness significantly reduced.The powder bed quality indicators were adopted as the optimization objective,and the multi-objective parameter optimization was carried out.The predicted optimal powder spreading parameters and powder bed quality indicators were then obtained.Moreover,the optimal values were then verified.This study can provide informative guidance for in-situ manufacturing at the moon in future deep space exploration missions.

Fundamental Theories and Key Technologies for Smart andOptimal Manufacturing in the Process Industry

Given the significant requirements for transforming and promoting the process industry, we present themajor limitations of current petrochemical enterprises, including limitations in decision-making, produc-tion operation, efficiency and security, information integration, and so forth. To promote a vision of theprocess industry with efficient, green, and smart production, modern information technology should beutilized throughout the entire optimization process for production, management, and marketing. To focuson smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of themanufacturing process, operating mode, and supply chain management, we put forward several key scien-tific problems in engineering in a demand-driven and application-oriented manner, namely：intelligentsensing and integration of all process information, including production and management information; collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; cooperative control and optimization of plant-wide production processes via human-cyber-physical in-teraction; and Q life-cycle assessments for safety and environmental footprint monitoring, in addition totracing analysis and risk control. In order to solve these limitations and core scientific problems, we furtherpresent fundamental theories and key technologies for smart and optimal manufacturing in the processindustry. Although this paper discusses the process industry in China, the conclusions in this paper can beextended to the larocess industry around the world.

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process- safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging informa- tion technologies such as arti cial intelligence (AI) are quite promising as a means of overcoming these dif culties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety;② knowledge-based reasoning for process safety;③ accurate fusion of heterogeneous data from various sources;and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

Advanced Data Collection and Analysis in Data‑Driven Manufacturing Process

The rapidly increasing demand and complexity of manufacturing process potentiates the usage of manufacturing data with the highest priority to achieve precise analyze and control,rather than using simplified physical models and human expertise.In the era of data-driven manufacturing,the explosion of data amount revolutionized how data is collected and analyzed.This paper overviews the advance of technologies developed for in-process manufacturing data collection and analysis.It can be concluded that groundbreaking sensoring technology to facilitate direct measurement is one important leading trend for advanced data collection,due to the complexity and uncertainty during indirect measurement.On the other hand,physical model-based data analysis contains inevitable simplifications and sometimes ill-posed solutions due to the limited capacity of describing complex manufacturing process.Machine learning,especially deep learning approach has great potential for making better decisions to automate the process when fed with abundant data,while trending data-driven manufacturing approaches succeeded by using limited data to achieve similar or even better decisions.And these trends can demonstrated be by analyzing some typical applications of manufacturing process.

Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era

Safe, ef cient, and sustainable operations and control are primary objectives in industrial manufacturing processes. State-of-the-art technologies heavily rely on human intervention, thereby showing apparent limitations in practice. The burgeoning era of big data is in uencing the process industries tremendously, providing unprecedented opportunities to achieve smart manufacturing. This kind of manufacturing requires machines to not only be capable of relieving humans from intensive physical work, but also be effective in taking on intellectual labor and even producing innovations on their own. To attain this goal, data analytics and machine learning are indispensable. In this paper, we review recent advances in data analytics and machine learning applied to the monitoring, control, and optimization of industrial processes, paying particular attention to the interpretability and functionality of machine learning mod- els. By analyzing the gap between practical requirements and the current research status, promising future research directions are identi ed.

Numerical and experimental analysis of quenching process for cam manufacturing

In order to obtain satisfactory mechanical properties for the cam used in high-power ship diesel engines, a new quenching technology was proposed by designing a two-stage quenching process with an alkaline bath as the quenching medium. To demonstrate the effectiveness of the proposed new quenching technology, both numerical analysis and experimental study were performed. The new quenching technology was analyzed using finite element method. The combined effects of the temperature, stress and microstructure fields were investigated considering nonlinear material properties. Finally, an experimental study was performed to verify the effectiveness of the proposed new quenching technology. The numerical results show that internal stress is affected by both thermal stress and transformation stress. In addition, the direction of the internal stress is changed several times due to thermal interaction and microstructure evolution during the quenching process. The experimental results show that the proposed new quenching technology significantly improves the mechanical properties and microstructures of the cam. The tensile strength, the impact resistance and the hardness value of the cam by the proposed new quenching technology are improved by 4.3%, 8.9% and 3.5% compared with those by the traditional quenching technology. Moreover, the residual stress and cam shape deformation are reduced by 40.0% and 48.9% respectively for the cam manufactured by the new quenching technology.

Multi-sensor measurement and data fusion technology for manufacturing process monitoring:a literature review

Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

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.

Manufacturing Process Selection of “Green” Oil Palm Natural Fiber Reinforced Polyurethane Composites Using Hybrid TEA Criteria Requirement and AHP Method for Automotive Crash Box

In this study,the best manufacturing process will be selected to build an automotive crash box using green oil palm natural fibre-reinforced polyurethane composite materials.This paper introduces an approach consist of technical aspects(T),the economic point of view(E)and availability(A),and it’s also called as TEA requirement.This approach was developed with the goal of assisting the design engineer in the selection of the best manufacturing process during the design phase at the criteria selection stage.In this study,the TEA requirement will integrate with the analytical hierarchy process(AHP)to assist decision makers or manufacturing engineers in determining the most appropriate manufacturing process to be employed in the manufacture of a composite automotive crash box(ACB)at the early stage of the product development process.It is obvious that a major challenge in the manufacturing selection process is lack of information regarding manufacturing of ACB using natural fibre composite(NFC).There have been no previous studies that examined ranking manufacturability processes in terms of their suitability.Therefore,the TEA-AHP hybrid method was introduced to provide unprejudiced criteria-ranking selection prior to evaluation of pairwise comparisons.At the end of this study,the pulforming process was selected as the best manufacturing process for fabrication of the ACB structural component.

Mechanical and Rheological Properties of Bamboo Pulp Fiber Reinforced High Density Polyethylene Composites:Influence of Nano CaCO_(3)Treatment and Manufacturing Process with Different Pressure Ratings

In order to investigate the effect of the relative motion of nano CaCO_(3)reinforced bamboo pulp fiber(BPF)/HDPE composite components on the mechanical performance,a comparative study was performed.BPF was treated by nano CaCO_(3)blending(BM)and impregnation modification(IM)technology.The composites were produced using hot press(HPMP),extrusion(EMP)and injection molding process(IMP).The physical morphology of BPF was similar at different manufacturing processes.Compared to the samples manufactured by HPMP,a decrease in the(specific)flexural strength of BPF/HDPE composites and an increase in those of composites treated by nano CaCO_(3)manufactured by EMP and IMP were observed.The injection molded composites exhibited the best values in the(specific)impact strength,(specific)tensile properties.IM had a greater effect on the rheological behavior of the composites than BM,and nano CaCO_(3)treatment most effectively affected the performance of the extrusion molded composites.