Additive manufacturing (AM) is a term often used interchangeably with 3D Printing (3DP) and has brought about a significant transformation in the design and production process by enabling a continuous flow of material...
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ISBN:
(纸本)9783031382406;9783031382413
Additive manufacturing (AM) is a term often used interchangeably with 3D Printing (3DP) and has brought about a significant transformation in the design and production process by enabling a continuous flow of material data to the finished product. New opportunities for diversification, increased agility, and potentially lower overall costs compared to traditional manufacturing methods arise. Therefore, various industries are moving away from traditional manufacturing ways and turning to this technology to improve their processes and outputs and ultimately for improving their supply chain efficiency. A comprehensive innovation and application of AM processes and equipment, in conjunction with emerging technologies such as cloud manufacturing, big data, and the Internet of Things are necessary in the improvement of AM process. In fact, there are still several challenges and issues that must be addressed for AM to be fully industrialized and widely adopted across different industries. The authors of this paper have succinctly and effectively highlighted significant concerns related to production and operational management. They have presented these concerns as an inspiration for readers to tackle challenges and capitalize on opportunities during the industrialization process of additive manufacturing.
Recently, the pharmaceutical manufacturing is going through a transition from classic batch manufacturing to continuous manufacturing, that is a faster as well as a more effective approach. The presented work establis...
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We develop a novel approach to construct a knowledge graph encompassing legal case documents and relevant legislation to improve legal information organization and retrieval. Our method involves data collection, entit...
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A methodology for creating a core database and producing personalized recommendations of learning content for knowledge refreshing is suggested. The recommendation for a learner is based on the similarity calculated f...
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Administration nowadays relies heavily on the ability to make sound decisions. Professionals can respond to difficulties by considering the evidence, assessing options, and settling on a course of action because of th...
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As a means to achieve the integration of cyber-physical systems, the digital twin shop-floor (DTS) provides extensive data about shop-floor operations through dynamic interaction. Operational data collected during the...
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As a means to achieve the integration of cyber-physical systems, the digital twin shop-floor (DTS) provides extensive data about shop-floor operations through dynamic interaction. Operational data collected during the manufacturing process is characterized by its multi-dimensional, heterogeneous, and time-series nature. To fully leverage this information and facilitate informed decision-making, it is essential to develop an effective method for representing the data in a unified and integrated manner. This paper proposes a knowledge graph-based operational data representation method for the DTS. The knowledge graph captures the entities and relationships related to the DTS, providing semantic integration of production line and process information. Operational data from various information systems are analyzed and managed comprehensively, utilizing the knowledge graph-based method to establish a unified, quantified, formal, and logical representation of the DTS operational data. This multi-dimensional knowledge structure encompasses manufacturing resource information, manufacturing process information, manufacturing task information, and manufacturing execution information. The proposed method is demonstrated through an example of a refrigerator manufacturing shopfloor. The resulting data representation facilitates DTS operational data analysis and cognitive decision-making promoting smart production during the manufacturing process. Additionally, this method explores the organization, acquisition, and reuse of knowledge in the DTS, along with the self-learning and self-cognitive capabilities of smart manufacturingsystems.
Hybrid models for accuracy and explainability in AI systems are those that combine different types of AI techniques, such as machine learning, fuzzy logic, and knowledge representation, to achieve both high performanc...
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Humans have often been perceived as a leading cause of error in Zero-defect manufacturing (ZDM) processes. There is thus a reduction of human interventions in the deployment of industry 4.0 (I4.0) technologies used fo...
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ISBN:
(纸本)9783031381645;9783031381652
Humans have often been perceived as a leading cause of error in Zero-defect manufacturing (ZDM) processes. There is thus a reduction of human interventions in the deployment of industry 4.0 (I4.0) technologies used for ZDM such as Machine Learning (ML). However, as manufacturing (e.g., I4.0 context) is often placed within a socio-technological context involving the co-integration of humans and technology, the manufacturing processes are now more vulnerable to cyber risk and threats. System vulnerabilities also derive from limitations associated with ML. This paper highlights three challenges associated with ML: explainability, data privacy, and security for ZDM. We argue that due to the high level of data complexity and lack of flexibility in ML models, humans play a critical role in ZDM decision-making. The paper explores the concept of security culture as an enabler for transformative resilience and zero-defect manufacturing and contributes to rethinking the human-centered approach in ZDM. The paper stresses a need to enhance contextual and empirical understanding of transformative resilience and security culture in ML/ZDM environments to better address adverse events such as cyber threat situations.
Medicinal plants have been integral to traditional medicine, offering a wide range of health benefits and natural remedies. However, accurate identification is essential for safe use and the preservation of this valua...
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Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, an...
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