Predictive Manufacturing System - Trends of Next-Generation Production Systems

Lee, Jay; Lapira, Edzel; Yang, Shanhu; Kao, Ann

doi:10.3182/20130522-3-br-4036.00107

Cited by 106 publications

(64 citation statements)

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“…The terms predictive maintenance and condition based monitoring describe the same goal. Another relevant example of this approach can also be found in [30] where a digital twin of the real machine tool is developed in a cloud platform containing information both from the actual operation but also from data and system information that were determined during the machine tool design phase. The last set of data are used to establish a reference state of what is considered as nominal operation so that any other operational state can be compared to that.…”

Section: Iiot Applications In Machiningmentioning

confidence: 99%

Internet of Things and Industrial Applications for Precision Machining

Benardos

Vosniakos

2017

SSP

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The Internet of Things (IoT) can be regarded as an attempt to bring together the physical and the digital world by using devices for seamlessly exchanging and processing information that can be used anywhere, anytime. For industrial automation and manufacturing, the Industrial Internet of Things (IIoT) is regarded as the next step of industrial revolution that promises a step-change in productivity and operational efficiency.Precision machining is a field that has received a lot of research interest as it deals with phenomena and underlying mechanisms that are very complex and highly interacting. As the requirements and demand for products of high quality and tolerances that must be produced with shorter lead times are increasing, innovative approaches and methodologies need to be developed to compensate and IIoT offers an appropriate platform. This paper aims to present an overview of IIoT, investigate potential industrial applications for precision machining and predict future trends.

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Section: Iiot Applications In Machiningmentioning

confidence: 99%

Internet of Things and Industrial Applications for Precision Machining

Benardos

Vosniakos

2017

SSP

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“…Technological improvements in sensing and connectivity enable seamless aggregation of raw data from various production facilities [16]. According to [5,6,14,17] the actual processing of big data into meaningful information is performed by the prognostics and health management (PHM) predictive analytics which is chosen based on the following criteria: speed of computation, investment cost and ease of deployment.…”

Section: Big Datamentioning

confidence: 99%

“…The main goal of PMS is to create manufacturing environment that enables the implementation of all new technologies based on capabilities such as "self-awareness", "self-predicting", "self-maintaining" and "self-learning". According to Lee et al [5] those capabilities allow that predictive manufacturing systems have a greater transparency in order to avoid the potential issues inside the system.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, PMS depend on the development of manufacturing science and technology [4]. PMS is an insufficiently researched emerging field that deals with issues of uncertainty and invisibles related to productivity, efficiency, flexibility and safety [5,6]. The main goal of PMS is to create manufacturing environment that enables the implementation of all new technologies based on capabilities such as "self-awareness", "self-predicting", "self-maintaining" and "self-learning".…”

Section: Introductionmentioning

confidence: 99%

“…In order to achieve transparency, manufacturing enterprise has to utilize different advanced predictive analytics along with IoT, data mining, intelligent sensor networks and big data to convert a large amount of collected data into useful information and to store it in the cloud, where it can be available at anytime and anywhere. According to [5,6,7], this represents the key effort in order to discover the invisible uncertainties in the manufacturing systems.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Manufacturing Systems in Industry 4.0: Trends, Benefits and Challenges

Nikolić¹,

Ignjatic²,

Suzić³

et al. 2017

DAAAM Proceedings

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The fourth industrial revolution, known as Industry 4.0, has tendency to push the boundaries of science and technology. This is especially true for the manufacturing industry. One of the biggest challenges facing the manufacturing industry today is how to make intelligent systems for production with "self-aware", "self-predict and "self-maintain" abilities. Predictive manufacturing systems (PMS) are new intelligent systems that provide these abilities in the production, processes and machines. The PMS combines different technologies and techniques: statistics, data mining, modelling and artificial intelligence methods. These technologies and techniques are used to convert data into information and make predictions about the observed system. This paper provides an overview of the various challenges, existing solutions and benefits of PMS, with a focus on success factors in Industry 4.0.

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