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）
Jiafu Wan, Xiaomin Li, Hong-Ning Dai, Andrew Kusiak, Miguel Martínez-García, Di Li
The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to external needs, and extract the processed knowledge, including business models, such as intelligent production, networked collaboration, and extended service models.
This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and the construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.
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…
KAPPS: A knowledge-based CPPS Architecture for the Circular Factory
Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology（材料搬运与物流研究所（IFL），卡尔斯鲁厄技术大学）；Department of Production Engineering, KTH Royal Institute of Technology（生产工程系，皇家理工技术大学）；Institute of Production Science (wbk), Karlsruhe Institute of Technology（生产科学研究所（wbk），卡尔斯鲁厄技术大学）；Analytic Computing, Institute for Artificial Intelligence, University of Stuttgart（分析计算，人工智能研究所，斯图加特大学）；Electronics and Computer Science, University of Southampton（电子与计算机科学，南安普顿大学）
IMT Lille Douai（里尔-杜埃国立高等矿业电信学院）；Université de Lille（里尔大学）；University Rey Juan Carlos（胡安卡洛斯国王大学）；Valencian Research Institute for AI（瓦伦西亚人工智能研究院）；Universitat Politècnica de València（瓦伦西亚理工大学）
Open and shared manufacturing factories typically dispose of a limited number of robots that should be properly allocated to tasks in time and space for an effective and efficient system performance. In particular, we deal with the dynamic capacitated production planning problem with sequence independent setup costs where quantities of products to manufacture and location of robots need to be determined at consecutive periods within a given time horizon and products can be anticipated or backordered related to the demand period. We consider a decentralized multi-agent variant of this problem i…
TU Wien（维也纳工业大学）；Helmut Schmidt University（赫尔穆特·施密特大学）；University of Applied Sciences Upper Austria（上奥地利应用科学大学）；Fraunhofer IESE（弗劳恩霍夫实验软件工程研究所）
Industry 4.0 envisions Cyber-Physical Production Systems (CPPSs) to foster adaptive production of mass-customizable products. Manufacturing approaches based on capabilities and skills aim to support this adaptability by encapsulating machine functions and decoupling them from specific production processes. At the 2022 IEEE conference on Emerging Technologies and Factory Automation (ETFA), a special session on capability- and skill-based manufacturing is hosted for the fourth time. However, an overview on capability- and skill based systems in factory automation and manufacturing systems is mis…
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…
Ludwig-Maximilians Universität Munich（慕尼黑路德维希-马克西米利安大学）；Oxford University（牛津大学）；Max-Planck-Institut für Informatik（马克斯·普朗克信息学研究所）；Siemens AG CT（西门子股份公司中央研究院）；Siemens PLM Software（西门子产品生命周期管理软件公司）
Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e.g., in the form of knowledge graphs. Creation and maintenance of such knowledge is expensive and requires automation. In this work we show how machine learning that is specifically tailored towards industrial applications can help in knowledge grap…
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…
