A review on the characteristics of cyber-physical systems for the future smart factories

Napoleone, Alessia; Macchi, Marco; Pozzetti, Alessandro

doi:10.1016/j.jmsy.2020.01.007

Cited by 140 publications

(63 citation statements)

References 189 publications

(221 reference statements)

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“…Various devices in industries such as sensors, servers, mobile robots, workstations are integrated to work together efficiently in a cyber physical system (CPS). The system should be able to communicate within itself and also send data through networking [ 50 ]. It is considered that the acquisition and storing of large amount of data in cloud can provide better performance of industry.…”

Section: Metrology and Impact Of Industry 40mentioning

confidence: 99%

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Varshney

Garg

Nagla

et al. 2021

MAPAN

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The current advances and innovations in sensor technologies attributed to Industry 4.0 serve as the backbone for the inclusive growth of industry and ramping up the economy of any country. Industry 4.0 was basically conceptualized by introducing the Internet of Things (IoT) and Information and Communication technologies (ICT) that serve as an interface between digital and physical world through the fusion of smart sensors. The role of smart sensors and IoT-enabled industrial infrastructure is pivotal for adapting to the advanced technologies based on fusion of smart sensors. Digital meteorological traceability and uses of intelligent sensors, instrumentation and machinery in Industry 4.0, Smart Cities, Digital India and AtmaNirbhar Bharat missions of the government of India, are not only highly important but also in huge demand, which is going to increase manifolds in the years to come. The present paper is an attempt to provide a terse review and perspectives related to the advanced technological developments in this field and the challenges therein.

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Section: Metrology and Impact Of Industry 40mentioning

confidence: 99%

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Varshney

Garg

Nagla

et al. 2021

MAPAN

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“…Despite the challenges faced in industry, emerging technologies are opening up new ways to deal with such challenges and to adapt to unpredictable environments [ 4 ]. Some of the characteristics of the future smart factories studied by the scholars are complexity encapsulation, interoperability, integration, modularity, virtualisation, intelligence, collaboration and dynamic reconfigurability [ 5 ]. Real-time bi-directional data integration between physical systems and digital models combined with simulation-based data analytics and VR capabilities promise viable solutions to face current and future scenarios.…”

Section: Introductionmentioning

confidence: 99%

Demonstration and evaluation of a digital twin-based virtual factory

Yildiz

Møller

Bilberg

2021

Int J Adv Manuf Technol

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Smart manufacturing, tailored by the 4th industrial revolution and forces like innovation, competition, and changing demands, lies behind the concurrent evolution (also known as co-evolution) of products, processes and production systems. Manufacturing companies need to adapt to ever-changing environments by simultaneously reforming and regenerating their product, process, and system models as well as goals and strategies to stay competitive. However, the ever-increasing complexity and ever-shortening lifecycles of product, process and system domains challenge manufacturing organization’s conventional approaches to analysing and formalizing models and processes as well as management, maintenance and simulation of product and system life cycles. The digital twin-based virtual factory (VF) concept, as an integrated simulation model of a factory including its subsystems, is promising for supporting manufacturing organizations in adapting to dynamic and complex environments. In this paper, we present the demonstration and evaluation of previously introduced digital twin-based VF concept to support modelling, simulation and evaluation of complex manufacturing systems while employing multi-user collaborative virtual reality (VR) learning/training scenarios. The concept is demonstrated and evaluated using two different wind turbine manufacturing cases, including a wind blade manufacturing plant and a nacelle assembly line. Thirteen industry experts who have diverse backgrounds and expertise were interviewed after their participation in a demonstration. We present the experts’ discussions and arguments to evaluate the DT-based VF concept based on four dimensions, namely, dynamic, open, cognitive, and holistic systems. The semi-structured conversational interview results show that the DT-based VF stands out by having the potential to support concurrent engineering by virtual collaboration. Moreover, DT-based VF is promising for decreasing physical builds and saving time by virtual prototyping (VP).

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“…With the trend of intelligent manufacturing, the accuracy and automation of injection molding can further be improved through artificial intelligence (AI) [ 15 ], cyber-physical systems [ 16 ], Internet of Things [ 17 ] and data mining [ 18 ]. AI is a method that combines domain, statistical and computer science knowledge by simulating human intelligence.…”

Section: Introductionmentioning

confidence: 99%

Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network

Ke¹,

Huang²

2020

Polymers

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Injection molding has been widely used in the mass production of high-precision products. The finished products obtained through injection molding must have a high quality. Machine parameters do not accurately reflect the molding conditions of the polymer melt; thus, the use of machine parameters leads to erroneous quality judgments. Moreover, the cost of mass inspections of finished products has led to strict restrictions on comprehensive quality testing. Therefore, an automatic quality inspection that provides effective and accurate quality judgment for each injection-molded part is required. This study proposes a multilayer perceptron (MLP) neural network model combined with quality indices for performing fast and automatic prediction of the geometry of finished products. The pressure curves detected by the in-mold pressure sensor, which reflect the flow state of the melt, changes in various indicators and molding quality, were considered in this study. Furthermore, the quality indices extracted from pressure curves with a strong correlation with the part quality were input into the MLP model for learning and prediction. The results indicate that the training and testing of the first-stage holding pressure index, pressure integral index, residual pressure drop index and peak pressure index with respect to the geometric widths were accurate (accuracy rate exceeded 92%), which demonstrates the feasibility of the proposed method.

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A review on the characteristics of cyber-physical systems for the future smart factories

Cited by 140 publications

References 189 publications

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Demonstration and evaluation of a digital twin-based virtual factory

Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network

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