Research on the Impact of Digital Technology and Servitization on the Performance of Manufacturing Enterprises

This article mainly discusses the impact of digital technology and service-oriented on the performance of manufacturing enterprises.It first elaborates on the background of the trend of service-oriented manufacturing and the significance of its enterprises.Then,based on the impact of digital technology on the performance of manufacturing enterprises,several practical and feasible measures are proposed,mainly focusing on the long-term layout of service-oriented,shortening the low period,enhancing the competitive awareness of state-owned manufacturing enterprises,improving the driving force transformation,achieving simultaneous progress digitally,configuring digital and service-oriented elements,and eliminating the monopoly of digital resources.Followed by lowering the threshold for private enterprise resource acquisition,promoting financial digital development,breaking the financial dilemma of service-oriented manufacturing,continuously improving the performance level of manufacturing enterprises,and promoting the development of manufacturing enterprises.

Research on the Impact of Digital Transformation on the Performance of Manufacturing Enterprises

To address the challenges faced by manufacturing enterprises in digital transformation,this paper analyzes the relationship between digital transformation and enterprise performance.Using panel data from domestic A-share-listed manufacturing enterprises from 2012 to 2022,two hypotheses were proposed.The analysis and verification revealed that digital transformation in manufacturing enterprises can enhance performance and reduce costs.Based on the impact of digital transformation on manufacturing enterprise performance,optimization suggestions are proposed to guide future digital transformation and performance improvement efforts in these enterprises.

Analysis of the Impact of Digital Transformation on Non-Financial Performance of Manufacturing Enterprises

As one of the important pillars of China’s national economy,the development of the manufacturing industry has attracted much attention.The manufacturing industry has become a key development area due to its high technology content,capital-intensive,knowledge-intensive,and many more characteristics.With the rapid development of big data information technology,emerging technologies such as artificial intelligence have brought a lot of convenience to people’s lives,and manufacturing enterprises have also realized the importance of reform and innovation,and have begun to carry out digital transformation.Based on the study of the motivation of digital transformation of manufacturing enterprises,this paper explores the transformation path of manufacturing enterprises to meet the needs of social development,and deeply analyzes the impact of digital transformation of manufacturing enterprises on enterprise performance,with a view to improving the unbalanced industrial structure and lagging technological development at the present stage through digital transformation.

Research on the Digital Economy,Technological Innovation,and Transformation and Upgrading of the Manufacturing Industry in Shandong Province

Based on panel data from 16 cities in Shandong Province from 2013 to 2022,and grounded in theoretical analysis,this paper constructs a benchmark regression model,a mediating effect model,and a heterogeneity test for empirical analysis.The results show that:(1)the digital economy has a significant positive effect on promoting the transformation and upgrading of the manufacturing industry in Shandong Province;(2)the digital economy can drive the transformation and upgrading of the manufacturing industry through technological innovation;and(3)the impact of the digital economy on the transformation and upgrading of the manufacturing industry in Shandong Province is evidently heterogeneous.

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.

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.

Simulation of Fracture Process of Lightweight Aggregate Concrete Based on Digital Image Processing Technology

The mechanical properties and failure mechanism of lightweight aggregate concrete(LWAC)is a hot topic in the engineering field,and the relationship between its microstructure and macroscopic mechanical properties is also a frontier research topic in the academic field.In this study,the image processing technology is used to establish a micro-structure model of lightweight aggregate concrete.Through the information extraction and processing of the section image of actual light aggregate concrete specimens,the mesostructural model of light aggregate concrete with real aggregate characteristics is established.The numerical simulation of uniaxial tensile test,uniaxial compression test and three-point bending test of lightweight aggregate concrete are carried out using a new finite element method-the base force element method respectively.Firstly,the image processing technology is used to produce beam specimens,uniaxial compression specimens and uniaxial tensile specimens of light aggregate concrete,which can better simulate the aggregate shape and random distribution of real light aggregate concrete.Secondly,the three-point bending test is numerically simulated.Thirdly,the uniaxial compression specimen generated by image processing technology is numerically simulated.Fourth,the uniaxial tensile specimen generated by image processing technology is numerically simulated.The mechanical behavior and damage mode of the specimen during loading were analyzed.The results of numerical simulation are compared and analyzed with those of relevant experiments.The feasibility and correctness of the micromodel established in this study for analyzing the micromechanics of lightweight aggregate concrete materials are verified.Image processing technology has a broad application prospect in the field of concrete mesoscopic damage analysis.

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.

Smart Design and Manufacturing the Welded Q350 Steel Frames via Lifecycle Management Strategy of Digital Twin

Artificial intelligent aided design and manufacturing have been recognized as one kind of robust data-driven and data-intensive technologies in the integrated computational material engi-neering(ICME)era.Motivated by the dramatical developments of the services of China Railway High-speed series for more than a decade,it is essential to reveal the foundations of lifecycle man-agement of those trains under environmental conditions.Here,the smart design and manufacturing of welded Q350 steel frames of CR200J series are introduced,presenting the capability and opportu-nity of ICME in weight reduction and lifecycle management at a cost-effective approach.In order to address the required fatigue life time enduring more than 9×10^(6)km,the response of optimized frames to the static and the dynamic loads are comprehensively investigated.It is highlighted that the maximum residual stress of the optimized welded frame is reduced to 69 MPa from 477 MPa of previous existing one.Based on the measured stress and acceleration from the railways,the fatigue life of modified frame under various loading modes could fulfil the requirements of the lifecycle man-agement.Moreover,our recent developed intelligent quality control strategy of welding process mediated by machine learning is also introduced,envisioning its application in the intelligent weld-ing.

On the Digital Transformation of the Automobile Manufacturing Industry in the Chengdu-Chongqing Economic Circle:Mechanism of Action and Feasible Paths

With a long industrial chain and a powerful ability to drive other industries,the automobile manufacturing industry has a prominent strategic position in the national economy.In recent years,many countries have put on their agenda the digitalization of the automobile manufacturing industry,leading to an connected,autonomous,shared,and electric(also known as CASE)①development trend in the industry.As one of the six major automobile industry clusters in China,the Chengdu-Chongqing economic circle has achieved initial results in the digital transformation of the automobile manufacturing industry.However,the region is still faced with some constraints,such as insufficient digital infrastructure,relatively slow development of new automobile products,insufficient innovation ability of the automobile industry,and complex digital transformation of small and medium-sized automobile enterprises(automobile SMEs).This paper intends to construct a framework for the mechanism of action of the digital transformation in the automobile manufacturing industry,analyze the effects of the digital transformation of the automobile manufacturing industry in the Chengdu-Chongqing economic circle,and propose feasible paths for the digital transformation of the automobile manufacturing industry in the region by drawing on domestic and international experience in this regard.The specific paths include:(a)Smoothing the“dual-core”data chain to facilitate the digital transformation of the automobile manufacturing industry;(b)Developing the new energy vehicle(NEV)industry to upgrade the quality of automobile products;(c)Achieving corner overtaking in the digital transformation of the automobile manufacturing industry with digital technology;(d)Jointly building the automobile industrial park to promote the digital transformation of the industry;(e)Addressing problems facing automobile SMEs in digital transformation via targeted policy tools.

Cogitation on the digital transformation of the process manufacturing industry triggered by the practices of the PIDAS

Digital transformation is the process of an industry moving to a digital business of continuous growth and improvement with digital technologies.It is an important path for the manufacturing industry to reconstruct and upgrade the existing operation mode to achieve efficiency, effectiveness, and product quality improvement.In this study, we analyze why the successful digital transformation practice is scarcely found in the core business of process manufacturing industries, such as the steel and cement industries.By introducing the development and application of the Process Intelligent Data Application System(PIDAS),which is the digital platform of heavy plates in Baosteel, the concepts, ideas, and development experience of the digital transformation of the process manufacturing are summarized.Moreover, the core issues worthy of attention are pointed out.Finally, a feasible route for the digital transformation of the process manufacturing is proposed.

Comprehensive Survey of the Landscape of Digital Twin Technologies and Their Diverse Applications

The concept of the digital twin,also known colloquially as the DT,is a fundamental principle within Industry 4.0 framework.In recent years,the concept of digital siblings has generated considerable academic and practical interest.However,academia and industry have used a variety of interpretations,and the scientific literature lacks a unified and consistent definition of this term.The purpose of this study is to systematically examine the definitional landscape of the digital twin concept as outlined in scholarly literature,beginning with its origins in the aerospace domain and extending to its contemporary interpretations in the manufacturing industry.Notably,this investigationwill focus on the research conducted on Industry 4.0 and smartmanufacturing,elucidating the diverse applications of digital twins in fields including aerospace,intelligentmanufacturing,intelligent transportation,and intelligent cities,among others.

Digital Twin Modeling Enabled Machine Tool Intelligence:A Review

Machine tools,often referred to as the“mother machines”of the manufacturing industry,are crucial in developing smart manufacturing and are increasingly becoming more intelligent.Digital twin technology can promote machine tool intelligence and has attracted considerable research interest.However,there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence.Herein,digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin.The review then delves into state-of-the-art digital twin modelingenabled machine tool intelligence,examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling.Additionally,it highlights three bottleneck issues facing the field.Considering these problems,the architecture of a digital twin machine tool(DTMT)is proposed,and three key technologies are expounded in detail:Data perception and fusion technology,mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology,and dynamic optimization and collaborative control technology for multilevel parameters.Finally,future research directions for the DTMT are discussed.This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence,making it significant for developing intelligent machine tools.

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.

Correlations between mineral composition and mechanical properties of granite using digital image processing and discrete element method

This study investigated the correlations between mechanical properties and mineralogy of granite using the digital image processing(DIP) and discrete element method(DEM). The results showed that the X-ray diffraction(XRD)-based DIP method effectively analyzed the mineral composition contents and spatial distributions of granite. During the particle flow code(PFC2D) model calibration phase, the numerical simulation exhibited that the uniaxial compressive strength(UCS) value, elastic modulus(E), and failure pattern of the granite specimen in the UCS test were comparable to the experiment. By establishing 351 sets of numerical models and exploring the impacts of mineral composition on the mechanical properties of granite, it indicated that there was no negative correlation between quartz and feldspar for UCS, tensile strength(σ_(t)), and E. In contrast, mica had a significant negative correlation for UCS, σ_(t), and E. The presence of quartz increased the brittleness of granite, whereas the presence of mica and feldspar increased its ductility in UCS and direct tensile strength(DTS) tests. Varying contents of major mineral compositions in granite showed minor influence on the number of cracks in both UCS and DTS tests.

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.

Digital Twin Modeling and Simulation Optimization of Transmission Front and Middle Case Assembly Line

As the take-off of China’s macro economy,as well as the rapid development of infrastructure construction,real estate industry,and highway logistics transportation industry,the demand for heavy vehicles is increasing rapidly,the competition is becoming increasingly fierce,and the digital transformation of the production line is imminent.As one of themost important components of heavy vehicles,the transmission front andmiddle case assembly lines have a high degree of automation,which can be used as a pilot for the digital transformation of production.To ensure the visualization of digital twins(DT),consistent control logic,and real-time data interaction,this paper proposes an experimental digital twin modeling method for the transmission front and middle case assembly line.Firstly,theDT-based systemarchitecture is designed,and theDT model is created by constructing the visualization model,logic model,and data model of the assembly line.Then,a simulation experiment is carried out in a virtual space to analyze the existing problems in the current assembly line.Eventually,some improvement strategies are proposed and the effectiveness is verified by a new simulation experiment.

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.

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.