Fuzzy logic for modeling machining process: a review

Adnan, Mustafa; Sarkheyli, Arezoo; Zain, Azlan Mohd; Haron, Habibollah

doi:10.1007/s10462-012-9381-8

Cited by 130 publications

(41 citation statements)

References 134 publications

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“…After the membership functions were obtained in practice, a rule table was created. The Fuzzy rules were created by using the training algorithms with input-output variable pairs, and the Fuzzy rules were indicated in the IF-THEN form that was expressed the relation between machining performance and machining parameters [28,29] . It contains the real experiment results generated according to the characteristic change of the input parameters.…”

Section: Resultsmentioning

confidence: 99%

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Yumurtacı Tavuklarda Yumurta Veriminin Tahminlenmesinde Bulanık Mantık Uygulaması

Gevrekçi¹,

Altintaş²,

Yeğenoğlu³

et al. 2018

Kafkas Univ Vet Fak Derg

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Fuzzy logic has a great potential for researchers and it has been developed over the last two decades. In animal science, there are limited numbers of studies on fuzzy logic approach. This study was carried out to examine the Fuzzy logic applications for prediction of the egg production data. Egg production records were obtained from the commercial poultry farm in Izmir, Turkey. Egg production traits of brown laying hens at 22 to 40 weeks of age were analyzed with Fuzzy logic system. In this study, Fuzzy logic model was developed for the prediction of egg production values of three classes; top, middle and lower (bottom for the cage effect) production. For this purpose 120 data lines representing 4 inputs consisting of cage, age at sexual maturity (ASM), body weight at sexual maturity (BWSM), body weight at mature age (BW) and 1 output, egg production (EP) that collected daily and individually were used in a Fuzzy logic model. The similarities between predicted and original production records were investigated, the coefficient of determination (R 2) was found as 0.89 which was also shown the prediction's success rate. The probability of egg production at ASM of 168 days, BWSM of 1500 g and BW of 1820 g was found 98.97% while egg production's probability at ASM of 157 days, BWSM of 1720 g and BW of 1940 g was determined as 97.97%. These results were also concluded that layers reached at sexual maturity later have lower egg production. The results illustrated that Fuzzy model could provide an effective and accurate prediction for classifying egg production of laying hens. However, since the applications of fuzzy logic related to the prediction of egg production are limited, this work will be pioneered by future studies.

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Section: Resultsmentioning

confidence: 99%

“…However, it has been observed that mental abilities such as intuition, non-monotonic and reasonable reasoning cannot be resolved with the classical logic understanding; therefore different studies have been made. The Decision support system is the one of those alternatives [24,29] .…”

Section: Discussionmentioning

confidence: 99%

Yumurtacı Tavuklarda Yumurta Veriminin Tahminlenmesinde Bulanık Mantık Uygulaması

Gevrekçi¹,

Altintaş²,

Yeğenoğlu³

et al. 2018

Kafkas Univ Vet Fak Derg

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“…Hence, Oladokun et al (2017) suggested that a resilience measuring model may be more amenable to a soft computing analytical technique such as fuzzy logic. Fuzzy set theory provides a mathematical tool for modeling uncertain, imprecise, vague and subjective data which represents a huge class of data encountered in most real-life situations (Adnan et al, 2015;Lincy and John, 2016 (Zadeh, 1996), Fuzzy Logic = Computing with Words. FL logic mimics human reasoning and capability to summarize data and focus on decisionrelevant information in problems involving incomplete, vague, imprecise or subjective information (Oladokun and Emmanuel, 2014).…”

Section: Toward Model Analysis: An Overview Of Fuzzy Logicmentioning

confidence: 99%

Towards Measuring Resilience of Flood Prone Communities: A Conceptual Framework

Oladokun¹,

Montz²

2018

Preprint

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Abstract. Community resilience has become an important policy and research concept for understanding and addressing the challenges associated with the interplay of climate change, urbanization, population growth, land use, sustainability, vulnerability and increased frequency of extreme flooding. Although measuring resilience has been identified as a fundamental step toward its understanding and effective management, there is, however, lack of an operational measurement framework due to the difficulty of systematically integrating socio-economic and techno-ecological factors. The study examines the challenges, constraints and construct ramifications that have complicated the development of an operational framework for measuring resilience of flood prone communities. Among others, the study highlights the absence of definitional convergence with its attendant proliferation of conceptual frameworks, challenges of data availability, data variability and data compatibility. The study suggests the adoption of an agreed definitional platform as the basis for developing conceptual constructs across all disciplines dealing with resilience. Using the National Academies’ definition of resilience (NRC 2012), a conceptual and mathematical model was developed using the dimensions, quantities and relationships established by the definition. A fuzzy logic equivalent of the model implemented to generate a resilience index for three flood prone communities in the US. It is concluded that the proposed framework offers a viable approach for measuring community flood resilience even when there is a limitation on data availability and compatibility.

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“…Fuzzy logic (FL) can by effectively used in modeling of machining processes such as predicting the surface roughness and controlling the cutting force. Adnan et al (2013) present FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process.…”

Section: Metaheuristic Techniques In Process Planning and Manufacturingmentioning

confidence: 99%

A metaheuristic for fast machining error compensation

Stryczek

2014

J Intell Manuf

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In case of complex parts machining or multidirectional machining in multi-part fixtures the error compensation in multi-dimensional decision space poses a difficult problem. The article focuses on the limitation of defective products by means of systematic increase of the remaining error budget due to correction of the setup data. A vectorial equation for machine tool space description is presented. The development of geometric dimensioning and tolerancing scheme to the levels connected with the setup data is proposed. The optimization algorithm used here is based on the paradigm particle swarm optimization (PSO), but it includes a few significant modifications inspired by the growth of the coral reef thus the name of the method-coral reefs inspired particle swarm optimization (CRIPSO). CRIPSO has been compared with three other popular metaheuristics: classic PSO, genetic algorithm, and cuckoo optimization algorithm. There is a practical example in this article.

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Fuzzy logic for modeling machining process: a review

Cited by 130 publications

References 134 publications

Yumurtacı Tavuklarda Yumurta Veriminin Tahminlenmesinde Bulanık Mantık Uygulaması

Yumurtacı Tavuklarda Yumurta Veriminin Tahminlenmesinde Bulanık Mantık Uygulaması

Towards Measuring Resilience of Flood Prone Communities: A Conceptual Framework

A metaheuristic for fast machining error compensation

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