A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world.However,different terminologies,namely smart manufacturing(SM)and intelligent manufacturing(IM),have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners.While SM and IM are similar,they are not identical.From an evolutionary perspective,there has been little consideration on whether the definition,thought,connotation,and technical development of the concepts of SM or IM are consistent in the literature.To address this gap,the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM.A bibliometric analysis of publication sources,annual publication numbers,keyword frequency,and top regions of research and development establishes the scope and trends of the currently presented research.Critical topics discussed include origin,definitions,evolutionary path,and key technologies of SM and IM.The implementation architecture,standards,and national focus are also discussed.In this work,a basis to understand SM and IM is provided,which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

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

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

AI-Based Modeling and Data-Driven Evaluation for Smart Manufacturing Processes

Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things(IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management.Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart.We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

Chengdu’s Smart Manufacturing in the New Era:Achievements and Optimization

The promotion of smart manufacturing is a crucial move that will enable Chengdu to build a major growth pole for high-quality national development and shape a new growth powerhouse.In recent years,Chengdu’s capability has made marked progress in smart manufacturing,significantly enhancing the capability for independent innovation,gradually optimizing development factor allocations,relentlessly strengthening pilot and demonstration effects,and constantly emerging collaborative service platforms.The development of Chengdu’s smart manufacturing can be summarized in four points:taking a holistic approach to top-down design,raising policy incentives for innovative talents,creating a favorable environment for industrial innovation,and building service platforms to promote innovation.In the new era,Chengdu should continue to explore the best possible path to smart manufacturing and target high-quality development of local manufacturing industries by making the following efforts:a)optimizing top-down design for better policy guidance;b)focusing on technological innovation to improve corresponding innovation capability;c)forging service platforms to break barriers to development;d)strengthening lateral communications to promote collaborative transformation;and e)building leading enterprises and brands native to Chengdu.

The Material Provisioning Dynamics Analysis and Simulation in the Smart Manufacturing Theme

The nature of production is time dominated based, it requires to manage the material supply, the product delivery, and the time of the process in an effective and efficient way. This paper posits the production behaves as a dynamic system that requires a model to optimize the production scheduling with the flexibility of predictive settings and to give a holistic overview about the dynamic properties of the material and the product across the cycle time to the factory planner.

Smart manufacturing of nonferrous metallurgical processes:Review and perspectives

The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry.As a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and control.Then,it illustrates the perspectives of smart and optimal manufacturing of the NFM industry.Finally,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry.This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.

Exploring self-organization and self-adaption for smart manufacturing complex networks

Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch,short-cycle,and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments,which poses great challenges to manufacturing enterprises.Fortunately,recent advances in the Industrial Internet of Things(IIoT)and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart,flexible,and resilient manufacturing systems.In this context,this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes.Specifically,a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels.Moreover,the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology,which can be added to or removed from the networks in a plug-and-play manner.Materials,information,and financial assets are passed through interactive links across the networks.Subsequently,analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices.Consequently,an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions.The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method,reducing manufacturing cost,manufacturing time,waiting time,and energy consumption,with reasonable computational time.This work potentially enables managers and practitioners to implement active perception,active response,self-organization,and self-adaption solutions in discrete manufacturing enterprises.

Enhancing Operational Efficiency: Exploring the Integration of SOPs Using Virtual Reality and Smart Glasses Technology in Food Manufacturing

This paper explores the integration of Standard Operating Procedures (SOPs) using virtual reality and smart glasses technology in food manufacturing. The study employs a thorough methodology, combining observational insights to develop a comprehensive SOP. Implementation at different firms resulted in significant improvements, reducing product waste and enhancing overall efficiency. The use of virtual reality further augments SOP adoption. The findings underscore SOPs’ transformative influence, offering a tangible solution to challenges in the food production sector. Recommendations include regular SOP reviews and ongoing training for sustained success. Different firms exemplify SOPs as indispensable tools for operational excellence.

The Industrial Internet of Things:A Competitive Advantage in the Era of Smart Manufacturing

At present,the Chinese manufacturing industry’s competitive advantage is facing multiple challenges.China’s“hexagon diagram”industrial policy,which has promoted the competitive advantage of Chinese companies in the era of globalization,encompasses these six strategies:enhancing factor supply,building infrastructure,improving institutional environments,enlarging market size,promoting industrial clustering,and encouraging competition.The hexagon model of industrial policy has gained China entry into many industries and increased their competitive advantage by lowering production cost and creating full-scope value chains.Lately,this competitive advantage is facing significant challenges from globalization reversal,trade wars,and the technological revolution.Relative to anti-globalization and trade wars,however,the most profound challenge facing China’s manufacturing industry is the rise of the Industrial Internet of Things and smart manufacturing.China needs to upgrade its hexagon diagram industrial policy to keep up with new developments in the Industrial Internet of Things in today’s era of smart 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.

Smart manufacturing systems for Industry 4.0： Conceptual framework, scenarios, and future perspectives

Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems （CPS）, which mark the advent of the fourth stage of industrial production （i.e., Industry 4.0）. The widespread application of CPS in manufacturing environments renders manufacturing sys- tems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

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.

Training for smart manufacturing using a mobile robot-based production line

Practice experimentation that integrates the manufacturing processes and cutting-edge technologies of smart manufacturing (SM) is essential for future academic and applied engineering personnel. The broadening efficacy of hands-on experience in SM engineering education has been manifested. In this regard, a reference practical system is proposed in this study for hands-on training in SM crucial advancements. The system constructs a mobile robot-based production line (MRPL) to increase participants’ interest in theoretical learning and professional skills. The MRPL-based reference system includes the comprehensive principles and processes involved in modern SM factories from warehousing to logistics, processing, and testing. With key features of modularity, integrability, customizability, and open architecture, this system has a threefold objective. First, it is an interdisciplinary subject that enables students to translate classroom learning into authentic practices, thus facilitating knowledge synthesis and training involvements. Second, it offers effective support to cultivate the attributions and behavioral competencies of SM talents, such as perseverance, adaptability, and cooperation. Third, it promotes students’ capacities for critical thinking and problem solving so that they can deal with the difficulties that physical systems have and motivates them to pursue careers with new syllabi, functions, and process techno-logies. The received positive evaluations and assessments confirm that this MRPL-based reference system is beneficial for modern SM talent training in higher engineering education.

Smart Process Manufacturing for Formulated Products

We outline the smart manufacturing challenges for formulated products, which are typically multicom- ponent, structured, and multiphase. These challenges predominate in the food, pharmaceuticals, agricul- tural and specialty chemicals, energy storage and energetic materials, and consumer goods industries, and are driven by fast-changing customer demand and, in some cases, a tight regulatory framework. This paper discusses progress in smart manufacturing namely, digitalization and the use of large data- sets with predictive models and solution- nding algorithms in these industries. While some progress has been achieved, there is a strong need for more demonstration of model-based tools on realistic prob- lems in order to demonstrate their bene ts and highlight any systemic weaknesses.

A multi-agent architecture for scheduling in platform-based smart manufacturing systems

During the past years, a number of smart manufacturing concepts have been proposed, such as cloud manufacturing, Industry 4.0, and Industrial Internet. One of their common aims is to optimize the collaborative resource configuration across enterprises by establishing platforms that aggregate distributed resources. In all of these concepts, a complete manufacturing system consists of distributed physical manufacturing systems and a platform containing the virtual manufacturing systems mapped from the physical ones. We call such manufacturing systems platform-based smart manufacturing systems (PSMSs). A PSMS can therefore be regarded as a huge cyber-physical system with the cyber part being the platform and the physical part being the corresponding physical manufacturing system. A significant issue for a PSMS is how to optimally schedule the aggregated resources. Multi-agent technology provides an effective approach for solving this issue. In this paper we propose a multi-agent architecture for scheduling in PSMSs, which consists of a platform-level scheduling multi-agent system (MAS) and an enterprise-level scheduling MAS. Procedures, characteristics, and requirements of scheduling in PSMSs are presented. A model for sched-uling in a PSMS based on the architecture is proposed. A case study is conducted to demonstrate the effectiveness of the proposed architecture and model.

Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment

Smart manufacturing in the“Industry 4.0”strategy promotes the deep integration of manufacturing and information technologies,which makes the manufacturing system a ubiquitous environment.However,the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers.To deal with this challenge,this study focuses on the real-time hybrid flow shop scheduling problem(HFSP).First,the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed,and its scheduling problem is described.Second,a real-time scheduling approach for the HFSP is proposed.The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the real-time status in the hybrid flow shop.With the scheduling rule,the priorities of the waiting job are calculated,and the job with the highest priority will be scheduled at this decision time point.A group of experiments are performed to prove the performance of the proposed approach.The numerical experiments show that the real-time scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances.Therefore,the contribution of this study is the proposal of a real-time scheduling approach,which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.

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.

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.

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.