scholarly journals Humans are not Machines - Anthropocentric Human-Machine Symbiosis for Ultra-Flexible Smart Manufacturing

Author(s):  
Yuqian Lu ◽  
Juvenal Sastre Adrados ◽  
Saahil Chand ◽  
Lihui Wang

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>

2021 ◽  
Author(s):  
Yuqian Lu ◽  
Juvenal Sastre Adrados ◽  
Saahil Chand ◽  
Lihui Wang

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>


2021 ◽  
Author(s):  
Saahil Chand ◽  
Yuqian Lu ◽  
Lihui Wang ◽  
Juvenal Sastre Adrados

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>


2021 ◽  
Author(s):  
Yuqian Lu ◽  
Juvenal Sastre Adrados ◽  
Saahil Chand ◽  
Lihui Wang

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>


2021 ◽  
Author(s):  
Saahil Chand ◽  
Yuqian Lu ◽  
Lihui Wang ◽  
Juvenal Sastre Adrados

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>


2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.


2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.


2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.


2021 ◽  
Vol 11 (6) ◽  
pp. 2850
Author(s):  
Dalibor Dobrilovic ◽  
Vladimir Brtka ◽  
Zeljko Stojanov ◽  
Gordana Jotanovic ◽  
Dragan Perakovic ◽  
...  

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.


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