Fuzzy surface roughness modeling of CNC down milling of Alumic-79

Dweiri, Fikri; Aljarrah, Mohannad T.; Al-Wedyan, Hussien

doi:10.1016/s0924-0136(02)00847-6

Cited by 89 publications

(35 citation statements)

References 5 publications

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“…For micro and macro milling operations different modeling techniques have been applied for tool wear or part quality estimation and they include response surfaces [8,[22][23][24][25], and Artificial Intelligence (AI) models such as Artificial Neural Networks (ANN) [8, 11-13, 15, 23-27], Adaptive Neuro Fuzzy Inference Systems (ANFIS) [28,29], Fuzzy Systems [12,30,31], Hidden Markov Models (HMM) [24], Bayesian Networks (BN) [32,33] and Least Squares Support Vector Machines (LS-SVM) [34][35][36]. Comprehensive reviews about modeling techniques applied in machining can be found in [19,37,38].…”

Section: Estimation Modulementioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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Section: Estimation Modulementioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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“…Machining studies were conducted on the AlSiC composite work pieces with high-speed steel (HSS) end-mill tools in a milling machine at different speeds and feeds. Dweiri et al [28] studied the downmilling machining process of Alumic-79 with an adaptive neuro fuzzy inference system to estimate the effect of machining variables, i.e. spindle speed, feed rate, depth of cut and number of flutes on the surface finish.…”

Section: Conventional Machiningmentioning

confidence: 99%

Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study

Pare¹,

Agnihotri²,

Krishna³

2015

SV-JME

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“…Therefore, Lo (2003) used the ANFIS with the hybrid learning algorithm to model the relationship between the surface roughness and the milling parameters (i.e., spindle speed, feed rate and depth of cut) in the end milling process. Down milling process of Alumic-79 using ANFIS to predict the effect of surface variables on the surface roughness (Dweiri et al 2003).The papers are presented on modelling of end milling considering speed, feed and depth of cut as an input machining parameter and output parameter as surface roughness and tool wear. The work is also reported with and without step over ratio and speed feed and depth of cut as an input cutting parameter (Topal, 2009).…”

Section: Introductionmentioning

confidence: 99%

Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

Desale¹,

Jahagirdar²

2014

10.5267/j.ijiec

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The aim of this study is to correlate work piece material hardness with surface roughness in prediction studies. The proposed model is for prediction of surface roughness of tool steel materials of hardness 55 HRC to 62 HRC (±2 HRC). The machining experiments are performed under various cutting conditions using work piece of different hardness. The surface roughness of these specimens is measured. The result showed that the influence of work piece material hardness on surface finish is significant for cutting speed and feed in CNC end milling operation. It is also observed that the surface roughness prediction accuracy of Adaptive neuro fuzzy inference system using triangular membership function is better than Gaussian, bell shape membership function and regression analysis. Surface roughness prediction accuracy with material hardness as input parameter is 97.61%.

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Fuzzy surface roughness modeling of CNC down milling of Alumic-79

Cited by 89 publications

References 5 publications

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study

Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

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