Review of Cloud Service Composition for Intelligent Manufacturing
School of Cyber Science and Engineering, Zhengzhou University（郑州大学网络空间安全学院）；School of Electrical and Information Engineering, Zhengzhou University（郑州大学电气与信息工程学院）；Department of Automation, Tsinghua University（清华大学自动化系）
Intelligent manufacturing is a new model that uses advanced technologies such as the Internet of Things, big data, and artificial intelligence to improve the efficiency and quality of manufacturing production. As an important support to promote the transformation and upgrading of the manufacturing industry, cloud service optimization has received the attention of researchers. In recent years, remarkable research results have been achieved in this field. For the sustainability of intelligent manufacturing platforms, in this paper we summarize the process of cloud service optimization for intelligent manufacturing. Further, to address the problems of dispersed optimization indicators and nonuniform/unstandardized definitions in the existing research, 11 optimization indicators that take into account three-party participant subjects are defined from the urgent requirements of the sustainable development of intelligent manufacturing platforms. Next, service optimization algorithms are classified into two categories, heuristic and reinforcement learning. After comparing the two categories, the current key techniques of service optimization are targeted. Finally, research hotspots and future research trends of service optimization are summarized.
Intelligent Collaborative Optimization for Rubber Tyre Film Production Based on Multi-path Differentiated Clipping Proximal Policy Optimization
State Key Laboratory of Physical Oceanography and the Faculty of Information Science and Engineering, Ocean University of China（物理海洋学国家重点实验室和信息科学与工程学院，中国海洋大学）；School of Mathematical and Computer Sciences, Heriot-Watt University（数学与计算机科学学院，赫瑞-沃德大学）；Mesnac Co., Ltd.（梅斯纳克公司）
Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based random forest (RF) models and a human knowledge based analytical hierarchical process (AHP) multi-criteria structure in accordance to the objective and the subjective factors of the te…
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 digitalization of manufacturing has created opportunities for consumers to customize products that fit their individualized needs which in turn would drive demand for manufacturing services. However, this pull-based manufacturing system production of extremely low quantity and limitless variety for products is expensive to implement. New emerging technology in design automation driven by data-driven computational design, manufacturing-as-a-service marketplaces and digitally enabled micro-factories holds promise towards democratization of innovation. In this paper, scientific, technology an…
Huazhong University of Science and Technology（华中科技大学）；Tsinghua University（清华大学）
Recent advances in the industrial inspection of textured surfaces-in the form of visual inspection-have made such inspections possible for efficient, flexible manufacturing systems. We propose an unsupervised feature memory rearrangement network (FMR-Net) to accurately detect various textural defects simultaneously. Consistent with mainstream methods, we adopt the idea of background reconstruction; however, we innovatively utilize artificial synthetic defects to enable the model to recognize anomalies, while traditional wisdom relies only on defect-free samples. First, we employ an encoding mo…
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）
Anomaly detection is important for industrial automation and part quality assurance, and while humans can easily detect anomalies in components given a few examples, designing a generic automated system that can perform at human or above human capabilities remains a challenge. In this work, we present a simple new anomaly detection algorithm called FADS (feature-based anomaly detection system) which leverages pretrained convolutional neural networks (CNN) to generate a statistical model of nominal inputs by observing the activation of the convolutional filters. During inference the system comp…
A Large Language Model-based multi-agent manufacturing system for intelligent shopfloor
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（系统控制与信息处理教育部重点实验室）；Yangzhou University（扬州大学）
NetEase Cloud Music（网易云音乐）；Malong Technologies（马泷科技）；China University of Geosciences（中国地质大学）
The development of artificial intelligent composition has resulted in the increasing popularity of machine-generated pieces, with frequent copyright disputes consequently emerging. There is an insufficient amount of research on the judgement of artificial and machine-generated works; the creation of a method to identify and distinguish these works is of particular importance. Starting from the essence of the music, the article constructs a music-rule-identifying algorithm through extracting modes, which will identify the stability of the mode of machine-generated music, to judge whether it is …
Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-n…
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 …
A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems
Birmingham City University（伯明翰城市大学）；Institute of Systems Engineering, Macau University of Science and Technology（澳门科学理工学院系统工程研究所）；Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology（新泽西理工学院亨利和约翰·C·哈特曼电气与计算机工程系）
