A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules

Nandi, Arup Kumar; Davim, J. Paulo

doi:10.1016/j.mechatronics.2008.08.004

Cited by 80 publications

(25 citation statements)

References 37 publications

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“…Three levels of flow rate and air pressure were used: 20, 40, and 60 ml/h and 1, 2, and 3 bars. The choice of those levels was based on previous studies on MQL drilling of aluminum alloys which applied flow rates in the range of 10 to 100 ml/h and in some cases up to 250 ml/h [8,9,[59][60][61][62][63][64][65][66][67][68]. …”

Section: Mql Drilling Trials Setupmentioning

confidence: 99%

Breakthrough of regenerative chatter modeling in milling by including unexpected effects arising from tooling system deflection

Totis

2016

Int J Adv Manuf Technol

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Conventional twist drilling is a widely used machining process for creating holes in aerospace and automobile structures. Drilling at room temperature can sometime affect the quality of machined holes due to increased thermal effects on the workpiece. Thermal effects can be a cumbersome when machining composites and fiber metal laminates due to their different thermal expansion coefficients, which may introduce additional stress in the structure. Thermal machining effects can be minimized using coolants supplied either directly or indirectly to the cutting tool-workpiece interaction zone, to remove away part of the generated heat. The use of coolants adds extra costs for handling, disposal, and environmental impact. Therefore, environmentally friendly cooling technologies are replacing conventional cooling methods to reduce costs and impact on the environment. In addition, the selection of machining parameters has great influence on the hole quality. This paper investigates the impact of drilling parameters and two modern cooling technologies namely cryogenic liquid nitrogen and minimum quantity lubrication on the hole perpendicularity error of fiber metal laminates commercially known as GLARE® (Glass Laminate Aluminum Reinforced Epoxy). It was also found that applying cryogenic liquid nitrogen or minimum quantity lubrication does not lead to an improvement in hole perpendicularity error in GLARE® laminates.

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Section: Mql Drilling Trials Setupmentioning

confidence: 99%

Breakthrough of regenerative chatter modeling in milling by including unexpected effects arising from tooling system deflection

Totis

2016

Int J Adv Manuf Technol

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“…In recent years, interest has grown in performing machining operations under dry or near-dry conditions, which may be partly explained by health and also economic reasons (Nandi and Davim 2009). One of the most complex manufacturing processes to change from high pressure conventional flood cooling to near-dry cut is the drilling of deep holes, also known as deep drilling.…”

Section: Deep Drilling and Lubrication Systemsmentioning

confidence: 99%

“…With regard to soft computing techniques, many publications are either devoted to drilling modelling (Chandrasekaran et al 2010) or refer to the problem (Choudhary et al 2009), but not so many examine deep drilling and even fewer study MQL deep drilling, where the physical phenomena differ from standard drilling. Fuzzy logic has been used to predict forces and surface quality on MQL deep drilling of aluminium (Nandi and Davim 2009), drill life (Biglari and Fang 1995;Jantunen and Vaajoensuu 2010) and better cutting conditions (Hashmi et al 2000) in deep drilling of steel with conventional flood cooling. Artificial neural networks (ANN) modelling approaches have been used for predicting burr size (Davim et al 2006) and drill wear (Sanjay et al 2005) in deep drilling of steel with conventional flood cooling.…”

Section: Deep Drilling and Lubrication Systemsmentioning

confidence: 99%

A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

2010

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A soft computing system used to optimize deep drilling operations under high-speed conditions in the manufacture of steel components is presented. The input data includes cutting parameters and axial cutting force obtained from the power consumption of the feed motor of the milling centres. Two different coolant strategies are tested: traditional working fluid and Minimum Quantity Lubrication (MQL). The model is constructed in three phases. First, a new strategy is proposed to evaluate and complete the set of available measurements. The primary objective of this phase is to decide whether further drilling experiments are required to develop an accurate roughness prediction model. An important aspect of the proposed strategy is the imputation of missing data, which is used to fully exploit both complete and incomplete measurements. The proposed imputation algorithm is based on a genetic algorithm and aims to improve prediction accuracy. In the second phase, a bag of multilayer perceptrons is used to model the impact of deep drilling settings on borehole roughness. Finally, this model is supplied with the borehole dimensions, coolant option and expected axial force to develop a 3D surface showing the expected borehole roughness as a function of drilling process settings. This plot is the necessary output of the model for its use under real workshop conditions. The proposed system is capable of approximating the optimal model used to control deep drilling tasks on steel components for industrial use.

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“…Linguistic models are built up by fuzzy rules that express human-readable descriptions in a format suitable for regression analysis [23] [24]. A fuzzy rule is a logical linguistic "If-Then" statement [25], where the "If" expression is referred to as the antecedent and the "Then" expression as the consequent.…”

Section: Introductionmentioning

confidence: 99%

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Wang

Cao

Yan

et al. 2016

Chemometrics and Intelligent Laboratory Systems

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Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (N O x , SO 2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of "If-Then" fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function neural networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

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A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules

Cited by 80 publications

References 37 publications

Breakthrough of regenerative chatter modeling in milling by including unexpected effects arising from tooling system deflection

Breakthrough of regenerative chatter modeling in milling by including unexpected effects arising from tooling system deflection

A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

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