Special Issue: Machine Learning Applications in ManufacturingSource: Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 002DOI: 10.1115/1.4046427Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Machine learning (ML) has recently become a power-engine transforming various manufacturing research and applications. In the era of Smart Manufacturing and I4.0, the abundance of smart sensors and industrial Internet of things has made manufacturing systems a data-rich environment. ML techniques play a significant role in uncovering fine-grained complex production patterns and offering timely decision support in a wide range of applications, to name a few, robotics and human–machine interaction, predictive maintenance, process optimization, task scheduling, quality improvement, and so on. While different ML techniques have been researched and deployed in manufacturing, many open challenges and questions still remain, from data understanding, data and knowledge representation, and data reasoning in ML to advanced topics such as predictive analytics, edge computing, and cybersecurity. Therefore, this special issue is dedicated to harvesting the latest research and development of ML in manufacturing. The papers are in no particular order.
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| date accessioned | 2022-02-04T14:51:09Z | |
| date available | 2022-02-04T14:51:09Z | |
| date copyright | 2020/03/03/ | |
| date issued | 2020 | |
| identifier issn | 1530-9827 | |
| identifier other | jcise_20_2_020301.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4274517 | |
| description abstract | Machine learning (ML) has recently become a power-engine transforming various manufacturing research and applications. In the era of Smart Manufacturing and I4.0, the abundance of smart sensors and industrial Internet of things has made manufacturing systems a data-rich environment. ML techniques play a significant role in uncovering fine-grained complex production patterns and offering timely decision support in a wide range of applications, to name a few, robotics and human–machine interaction, predictive maintenance, process optimization, task scheduling, quality improvement, and so on. While different ML techniques have been researched and deployed in manufacturing, many open challenges and questions still remain, from data understanding, data and knowledge representation, and data reasoning in ML to advanced topics such as predictive analytics, edge computing, and cybersecurity. Therefore, this special issue is dedicated to harvesting the latest research and development of ML in manufacturing. The papers are in no particular order. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Special Issue: Machine Learning Applications in Manufacturing | |
| type | Journal Paper | |
| journal volume | 20 | |
| journal issue | 2 | |
| journal title | Journal of Computing and Information Science in Engineering | |
| identifier doi | 10.1115/1.4046427 | |
| page | 20301 | |
| tree | Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 002 | |
| contenttype | Fulltext |