Intelligent systems in manufacturing: current developments and future prospects

Meziane, Farid; Vadera, Sunil; Kobbacy, Khairy A. H.; Proudlove, Nathan

doi:10.1108/09576060010326221

Cited by 84 publications

(38 citation statements)

References 98 publications

(131 reference statements)

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“…This scheme was defined by Meziane et al [16] when identifying different areas of manufacturing where Artificial Intelligence (AI) was being applied. Table 5 provides a summary of each area of manufacturing.…”

Section: Area In Manufacturingmentioning

confidence: 99%

Big data in manufacturing: a systematic mapping study

O’Donovan

Leahy

Bruton

et al. 2015

Journal of Big Data

151

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The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These smart facilities are focused on creating manufacturing intelligence from real-time data to support accurate and timely decision-making that can have a positive impact across the entire organisation. To realise these efficiencies emerging technologies such as Internet of Things (IoT) and Cyber Physical Systems (CPS) will be embedded in physical processes to measure and monitor real-time data from across the factory, which will ultimately give rise to unprecedented levels of data production. Therefore, manufacturing facilities must be able to manage the demands of exponential increase in data production, as well as possessing the analytical techniques needed to extract meaning from these large datasets. More specifically, organisations must be able to work with big data technologies to meet the demands of smart manufacturing. However, as big data is a relatively new phenomenon and potential applications to manufacturing activities are wide-reaching and diverse, there has been an obvious lack of secondary research undertaken in the area. Without secondary research, it is difficult for researchers to identify gaps in the field, as well as aligning their work with other researchers to develop strong research themes. In this study, we use the formal research methodology of systematic mapping to provide a breadth-first review of big data technologies in manufacturing.

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Section: Area In Manufacturingmentioning

confidence: 99%

Big data in manufacturing: a systematic mapping study

O’Donovan

Leahy

Bruton

et al. 2015

Journal of Big Data

151

View full text Add to dashboard Cite

show abstract

“…The monitoring system envisages tool life by means of diverse sensors for congregation information stand on regression model that permits for the distinctions involving tools and different machine setups [22]. The regression model constructs its forecast by using the narration of other tools and combining it with the information attained about the tool under contemplation.…”

Section: Hybrid Systemsmentioning

confidence: 99%

“…Table 1 is summarizing the evaluation techniques. Falkenauer and Bouffouix [8] Neural Networks Hao et.al [11] Haykin [13] Kosko [19] Lo and Bavarian [20] Hybrid Systems Malakooti et al [21] Ming et al [23] Proudlove et al [24] Maziane et al [22] VI. CONCLUSIONS In this paper an assessment of fuzzy methods for scheduling in flexible manufacturing system was made.…”

Section: Hybrid Systemsmentioning

confidence: 99%

Function of Artificial Intelligence in Production Scheduling: A Critical Evaluation and Comparison of Key Approaches

Shukla¹

2018

IJRASET

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Production scheduling is an element of operational research that relies on combinational optimization explained by discrete methods. This extensive area covers diverse variety of problems as; vehicle routing problem, bin packing problem and job priority. With the intention of solving these problems, operational research applies two main principles: exact methods which give the complete finest solution but resolve merely small sized problems, and approximate methods which give only good solution except resolve near real life sized problem. The next category provides various methods separated into-problem dedicated methods entitled heuristics and general method entitled metaheuristics. Several of these metaheuristic methods are guiding the literature of production scheduling for past two decade, as; Genetic Algorithm, Neural Network, and Fuzzy Logic which will be discuss in this paper. This evaluation shows that there are simply few research works which compare heuristic techniques on scheduling problem.

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“…For those who are interested in Intelligent Systems application in advanced manufacturing systems the review of developments and future prospects of Intelligent Systems in manufacturing can be advised [22]. The review put stress on human aspects in Intelligent Manufacturing Systems as well.…”

Section: Recent Research On Human-machine Interactionsmentioning

confidence: 99%

Man-machine interactions in advanced manufacturing systems

Oborski

2004

The International Journal of Advanced Manufacturing Technology

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Development of modern machines and production equipment, supported by advanced control systems, based on recent achievements of computer and software technology is necessary to fulfil clients requirements. Significant number of manufacturing systems can work automatically with limited contribution of employees.However even in advanced manufacturing systems still one of the most important factors is human being. Usually the whole system performance depends on human decisions, the significance of them is higher than in the past, because of more complex and costly production systems. In such a situation, the importance of efficient utilisation of manufacturing equipment by proper man-machine interaction is necessary. The paper presents the problem of man-machine interactions, as well as the attempt to model human behaviour. The interaction media have been explained and short survey of research in this field has been done. The aim of the paper is to order the knowledge about man-machine interactions.

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Intelligent systems in manufacturing: current developments and future prospects

Cited by 84 publications

References 98 publications

Big data in manufacturing: a systematic mapping study

Big data in manufacturing: a systematic mapping study

Function of Artificial Intelligence in Production Scheduling: A Critical Evaluation and Comparison of Key Approaches

Man-machine interactions in advanced manufacturing systems

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