Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining

Maher, Ibrahem; Eltaib, M. E. H.; Sarhan, Ahmed A.D.; El-Zahry, R. M.

doi:10.1007/s00170-014-6379-1

Cited by 52 publications

(20 citation statements)

References 22 publications

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“…This high accuracy exhibited by the fuzzy logic modelling indicates that it can be used to predict the outcome of the experiments even prior to conducting the machining, thereby reducing material wastages and unnecessary trials. This finding agrees with previous research works by authors …”

Section: Discussionsupporting

confidence: 94%

Fuzzy logic method to investigate grinding of alumina ceramic using minimum quantity lubrication

Dambatta

Sarhan

Sayuti

et al. 2019

Int J Applied Ceramic Tech

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The use of nanofluid in lubrication during machining of advanced engineering ceramics has been found to be highly efficient and eco-friendly. This work involves experimental investigation of grinding Alumina (Al 2 O 3 ) ceramic to determine the effect of the grinding variables. The grinding variables considered include depth of cut, feed rate, type of diamond wheel, and lubrication type. Moreover, the response parameters considered include grinding power, coefficient of friction, and surface quality. The responses obtained during the experiments were used to develop a fuzzy logic prediction model. The findings from this work can be concluded as follows: (a) The depth of cut and feed rate have direct proportional relationship with the grinding power and coefficient of friction. (b) The metallic bonded diamond wheel was found to have higher machining efficiency than the resinoid bonded one. (c) Higher number of diamond grits produces lower frictional coefficient. (d) The carbon nanotube based nanofluid when used in the minimum quantity lubrication (MQL) process proffers better lubrication capability than conventional flood cooling system. (e) The developed fuzzy logic models were found to have high prediction accuracies of 97.22%, 98.60%, and 96.8%, respectively, for grinding power, grinding force ratio, and surface roughness. K E Y W O R D S grinding, alumina, nanoparticles, carbon nanotube, fuzzy logic, alumina

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Section: Discussionsupporting

confidence: 94%

Fuzzy logic method to investigate grinding of alumina ceramic using minimum quantity lubrication

Dambatta

Sarhan

Sayuti

et al. 2019

Int J Applied Ceramic Tech

View full text Add to dashboard Cite

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“…ANFIS has also been applied to model the process of predicting machining performance (Maher et al, 2015a;Maher et al, 2014).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production

Maher

Sarhan

Barzani

et al. 2015

Journal of Cleaner Production

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“…Monitoring the machining status is very important in managing the machined part quality during the machining process [27,[67][68][69][70][71][72]. For applying the proposed CPS concept to areal case in manufacturing field, the self-adjusting cutting condition on a high-speed machining machine was focused on.…”

Section: Practical Application To the Real Machinementioning

confidence: 99%

“…Intelligent machines with cognitive capabilities were proposed [37,63,64]. The machines can communicate with each other [65], as well as enable the management of machine health [66], and to monitor the machining condition [67][68][69][70][71][72]. The new trend is to change the machine from an automatic system to an autonomous system such as CPS [73][74][75][76].…”

mentioning

confidence: 99%

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

Tran

Park²,

Nguyen

et al. 2019

Applied Sciences

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Featured Application: A proposed smart manufacturing system has the ability to adapt to manufacturing changes. Abstract:The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.Currently, manufacturing demands are changing from mass production to mass customization, which is focused on exclusive and individualized demand [2,3]. This trend requires the manufacturing systems to have the ability to adapt to the fast change of the manufacturing environment. To face this challenge, the new trend of manufacturing system development is to apply autonomous behaviors inspired from living systems and nature to have the sustainable manufacturing systems. Industry 4.0 enables the realization of sustainable manufacturing using the ubiquitous information and communication technology (ICT) infrastructure [4]. National strategies for developing smart manufacturing systems are focused on by both developing and developed countries [5,6]. The development of manufacturing not only considers technological innovations but also economic, social and environmental effects [4,7]. In the literature, many novel paradigms known as intelligent manufacturing systems (IMS) [8][9][10][11][12][13][14][15] have been proposed such as holonic [16], biological [17], reconfigurable [18], and cloud manufacturing systems [19].In the manufacturing field, Industry 4.0 is characterized by the autonomous systems with cyber and physical representation [20]...

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Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining

Cited by 52 publications

References 22 publications

Fuzzy logic method to investigate grinding of alumina ceramic using minimum quantity lubrication

Fuzzy logic method to investigate grinding of alumina ceramic using minimum quantity lubrication

Increasing the productivity of the wire-cut electrical discharge machine associated with sustainable production

Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context

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