The evolution of industry has enabled the integration of physical and digital systems, facilitating the collection of extensive data on manufacturing processes. This integration provides a reliable solution for improving process quality and managing equipment health. However, data collected from real manufacturing processes often exhibit challenging properties, such as severe class imbalance, high rates of missing values, and noisy features, which hinder effective machine learning implementation. In this study, a rare class prediction approach is developed for in situ data collected from a smart semiconductor manufacturing process. The primary objective is to build a model that addresses issues of noise and class imbalance, enhancing class separation. The developed approach demonstrated promising results compared to existing literature, which would allow the prediction of new observations that could give insights into future maintenance plans and production quality. The model was evaluated using various performance metrics, with ROC curves showing an AUC of 0.95, a precision of 0.66, and a recall of 0.96
Smart manufacturing systems are being deployed at a growing rate because of their ability to interpret a wide variety of sensed information and act on the knowledge gleaned from system observations. In many cases, the principal goal of the smart manufacturing system is to rapidly detect (or anticipate) failures to reduce operational cost and eliminate downtime. This often boils down to detecting anomalies within the sensor date acquired from the system. The smart manufacturing application domain poses certain salient technical challenges. In particular, there are often multiple types of sensor…
Jožef Stefan International Postgraduate School（约热夫·斯特凡国际研究生院）；Jožef Stefan Institute（约热夫·斯特凡研究所）；Qlector d.o.o.（Qlector有限公司）
A general approach for building a smart assistant that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps is presented. We develop a methodology to build such a system. The system is demonstrated on a demand forecasting use case in manufacturing. The methodology can be extended to several use cases in manufacturing. The system provides means for knowledge acquisition, gathering data from users. We envision active learning can be used to get data labels where labeled data is scarce.
IBM China Research Lab（IBM中国研究院）；Shanghai Jiao Tong University（上海交通大学）
With the rapid growth of display devices, quality inspection via machine vision technology has become increasingly important for flat-panel displays (FPD) industry. This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. The system is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture. A hybrid application of conventional computer vision technique and the latest deep convolutional neural network (DCNN) leads to an integrated defec…
Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models
1Department of Software；IT Engineering, École de technologie supérieure, Montreal, Canada 2School of Electrical \& Computer Engineering, National Technical University of Athens
Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing
Birmingham City University（伯明翰城市大学）；Macao Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems（澳门系统工程研究院和智能科学与系统联合实验室）；New Jersey Institute of Technology（新泽西理工学院）
University of Trier（特里尔大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
The transition to Industry 4.0 requires smart manufacturing systems that are easily configurable and provide a high level of flexibility during manufacturing in order to achieve mass customization or to support cloud manufacturing. To realize this, Cyber-Physical Systems (CPSs) combined with Artificial Intelligence (AI) methods find their way into manufacturing shop floors. For using AI methods in the context of Industry 4.0, semantic web services are indispensable to provide a reasonable abstraction of the underlying manufacturing capabilities. In this paper, we present semantic web services …
In the current industrial practices, the exponential growth in terms of availability and affordability of sensors, data acquisition systems, and computer networks forces factories to move toward implementing high integrating Cyber-Physical Systems (CPS) with production, logistics, and services. This transforms today's factories into Industry 4.0 factories with significant economic potential. Industry 4.0, also known as the fourth Industrial Revolution, levers on the integration of cyber technologies, the Internet of Things, and Services. This paper proposes an Augmented Reality (AR)-based syst…
Software Competence Center Hagenberg（哈根贝格软件能力中心）；Pak-Austria Fachhochschule - Institute of Applied Sciences and Technology（巴基斯坦-奥地利应用科学与技术学院）
With the proliferation of AI-enabled software systems in smart manufacturing, the role of such systems moves away from a reactive to a proactive role that provides context-specific support to manufacturing operators. In the frame of the EU funded Teaming.AI project, we identified the monitoring of teaming aspects in human-AI collaboration, the runtime monitoring and validation of ethical policies, and the support for experimentation with data and machine learning algorithms as the most relevant challenges for human-AI teaming in smart manufacturing. Based on these challenges, we developed a re…
2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Center for Industrial Artificial Intelligence, Department of Mechanical Engineering, University of Maryland, College Park（工业人工智能中心，机械工程系，马里兰大学College Park分校）；Department of Management, Economics and Industrial Engineering, Politecnico di Milano（管理、经济与工业工程系，米兰理工学院）；Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University（工业与系统工程系，香港理工大学）；Centre for Advanced Manufacturing & Supply Chains, World Economic Forum（先进制造与供应链研究中心，世界经济论坛）；Department of Mechanical Engineering, Indian Institute of Technology Indore（机械工程系，印度理工学院Indore分校）；Future Information Innovative College, Fudan University（未来信息创新学院，复旦大学）；Department of Mechanical Engineering, Seoul National University（机械工程系，首尔国立大学）；Department of Mechanical and Information Engineering, University of Seoul（机械与信息工程系，首尔大学）；Onepredict Corp.（Onepredict公司）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（机械与航空航天工程学院，南洋理工大学）；Singapore Centre for 3D Printing, Nanyang Technological University（新加坡3D打印中心，南洋理工大学）；Mechanical Engineering, Purdue University（机械工程系，普渡大学）；Digital Twin International Research Center, International Institute for Interdisciplinary and Frontiers, Beihang University（数字孪生国际研究中心， interdisciplinary and Frontiers 国际研究院，北京航空航天大学）；School of Automation Science and Electrical Engineering, Beihang University（自动化科学与电气工程学院，北京航空航天大学）；Department of Engineering, University of Cambridge（工程系，剑桥大学）；Center for Advanced Manufacturing, University of Southern California（先进制造中心，南加州大学）；School of Sustainability Engineering and Environmental Engineering, Purdue University（可持续工程与环境工程系，普渡大学）；School of Mechanical Engineering, Sungkyunkwan University（机械工程系，全南大学）；Intelligent Maintenance and Operations Systems, EPFL（智能维护与运营系统，苏黎世联邦理工学院）；Department of Mechanical Engineering, Massachusetts Institute of Technology（机械工程系，麻省理工学院）；J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University（J. Mike Walker ’66 机械工程系，德克萨斯A&M大学）；Department of Mechanical and Process Engineering, ETH Zürich（机械与工艺工程系，苏黎世联邦理工学院）
Hyperdimensional Computing for Sustainable Manufacturing: An Initial Assessment
School of Mechanical, Aerospace, and Manufacturing Engineering University of Connecticut Storrs, Connecticut, USA（机械、航空航天与制造工程学院 美国康涅狄格大学 斯托尔斯分校）；Department of Mechanical and Aerospace Engineering Rutgers University New Brunswick, NJ, USA（机械与航空航天工程系 罗格斯大学 新泽西州新布朗斯维爾分校）
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；West Virginia University（西弗吉尼亚大学）
The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of…
Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry
Infineon Technologies Austria AG（英飞凌奥地利技术公司）；Graz University of Technology（格拉茨工业大学）；Université Bourgogne Franche-Comté（勃艮第-弗朗什-孔泰大学）；Know-Center GmbH（Know-Center有限公司）
University of New Brunswick（新不伦瑞克大学）；Eigen Innovation Inc.（Eigen创新公司）；RMIT University（皇家墨尔本理工大学）
Quality assurance is crucial in the smart manufacturing industry as it identifies the presence of defects in finished products before they are shipped out. Modern machine learning techniques can be leveraged to provide rapid and accurate detection of these imperfections. We, therefore, propose a transfer learning approach, namely TransferD2, to correctly identify defects on a dataset of source objects and extend its application to new unseen target objects. We present a data enhancement technique to generate a large dataset from the small source dataset for building a classifier. We then integ…
University of Nantes（南特大学）；Centrale Nantes（南特中央理工学院）
In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and suc…
University of Applied Sciences Upper Austria（上奥地利应用科学大学）；Tampere University（坦佩雷大学）；Institute of Biosciences and Medical Technology（生物科学与医疗技术研究所）；UMIT-The Health and Life Science University（UMIT-健康与生命科学大学）；Nankai University（南开大学）
The implementation of robust, stable, and user-centered data analytics and machine learning models is confronted by numerous challenges in production and manufacturing. Therefore, a systematic approach is required to develop, evaluate, and deploy such models. The data-driven knowledge discovery framework provides an orderly partition of the data-mining processes to ensure the practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data-- and model-development--re…
CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing
Birmingham City University（伯明翰城市大学）；Helen and John C. Hartmann Department of Electrical and Computer Engineering（海伦和约翰·C·哈特曼电气与计算机工程系）；New Jersey Institute of Technology（新泽西理工学院）
Mepco Schlenk Engineering College（梅普科·施伦克工程学院）；Fordham University（福特汉姆大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Qatar University（卡塔尔大学）
The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers, has paved the way for Artificial Intelligence (AI) and Big Data in support of smart manufacturing. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, Big data, Blockchain, 5G communications, in support of smart manufacturing and the dynamical processes in modern industries. In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4.0 with a particular focus on key applications, techniques…
Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review
Purple Mountain Laboratories（紫金山实验室）；Northwestern Polytechnical University（西北工业大学）；Wuhan University of Technology（武汉理工大学）；Peking University（北京大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Carleton University（卡尔顿大学）
With the advent of Industry 4.0, Data Science and Explainable Artificial Intelligence (XAI) has received considerable intrest in recent literature. However, the entry threshold into XAI, in terms of computer coding and the requisite mathematical apparatus, is really high. For fault diagnosis of steel plates, this work reports on a methodology of incorporating XAI based insights into the Data Science process of development of high precision classifier. Using Synthetic Minority Oversampling Technique (SMOTE) and notion of medoids, insights from XAI tools viz. Ceteris Peribus profiles, Partial De…
Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges
South China University of Technology（华南理工大学）；Zhongkai University of Agriculture and Engineering（仲恺农业工程学院）；Macau University of Science and Technology（澳门科技大学）；The University of Iowa（爱荷华大学）；Loughborough University（拉夫堡大学）
Applications in the smart industry domain, such as interaction with collaborative robots using vocal commands or machine vision systems often requires the deployment of deep learning algorithms on heterogeneous low power computing platforms. The availability of software tools and frameworks to automatize different design steps can support the effective implementation of DL algorithms on embedded systems, reducing related effort and costs. One very important aspect for the acceptance of the framework, is its extensibility, i.e. the capability to accommodate different datasets and define customi…
Cyber-Physical Systems (CPS) play a crucial role in the era of the 4thIndustrial Revolution. Recently, the application of the CPS to industrial manufacturing leads to a specialization of them referred as Cyber-Physical Production Systems (CPPS). Among other challenges, CPS and CPPS should be able to address interoperability issues, since one of their intrinsic requirement is the capability to interface and cooperate with other systems. On the other hand, to fully realize theIndustry 4.0 vision, it is required to address horizontal, vertical, and end-to-end integration enabling a complete aware…
Macau University of Science and Technology（澳门科技大学）；Norwegian University of Science & Technology（挪威科技大学）；Tianjin University（天津大学）；South China University of Technology（华南理工大学）；King Saud University（沙特国王大学）
The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing. Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytic…
Status and Future Prospects of the Standardization Framework Industry 4.0: A European Perspective
Fraunhofer Institute for Manufacturing Engineering and Automation (IPA)（弗劳恩霍夫智能制造与自动化研究所）；German Commission for Electrotechnical, Electronic, and Information Technologies（德国电气电子和信息通信技术委员会）；Technical University of Applied Sciences Augsburg（应用技术大学阿沙布斯）
Sichuan University（四川大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Huazhong University of Science and Technology（华中科技大学）
The bin packing problem exists widely in real logistic scenarios (e.g., packing pipeline, express delivery), with its goal to improve the packing efficiency and reduce the transportation cost. In this NP-hard combinatorial optimization problem, the position and quantity of each item in the box are strictly restricted by complex constraints and special customer requirements. Existing approaches are hard to obtain the optimal solution since rigorous constraints cannot be handled within a reasonable computation load. In this paper, for handling this difficulty, the packing knowledge is extracted …
