Constrained optimum surface roughness prediction in turning of X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm

Zinati, Reza Farshbaf; Razfar, Mohammad Reza

doi:10.1007/s00170-011-3393-4

Cited by 18 publications

(5 citation statements)

References 18 publications

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“…However, many other previous works have focused on the determination of the optimal cutting conditions for different objectives [20][21][22][23][24][25][26][27]. Zuperl and Cus [20] described a method for optimizing multi-purpose turning cutting conditions with the help of neural networks aimed at increasing productivity and reducing costs, and providing an acceptable surface roughness.…”

Section: Introductionmentioning

confidence: 99%

“…Optimal processing parameters were selected using the Pareto chart. Zinati and Razfar [22] derived prediction models for cutting conditions that ensure minimal surface roughness for longitudinal turning (turning long parts) of X20Cr13. Jafarian et al [23] investigated three separate neural networks in order to minimize the surface roughness and maximize tool life in the turning.…”

Section: Introductionmentioning

confidence: 99%

“…Sangwan et al [26] used artificial neural networks (ANN) and genetic algorithm (GA) to obtain the optimum processing parameters leading to minimal surface roughness when turning a titanium alloy Ti6Al-4 V. Gupta et al [27] focused on the optimization of the process parameters of turning operations, namely surface roughness, the back surface of tool wear and power consumption. However, in the abovementioned research [20][21][22][23][24][25][26][27] the task of finding the optimum cutting conditions is one-sided, taking into account only the surface roughness without its relationship to the productivity and machining time of unit volume, which does not present the optimum handling of an expensive material such as high-strength steel. Now, we consider the work focused on the determination of optimal parameters for turning with multi-objective optimization [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of highstrength steel featuring three factors-cutting speed V, feed rate f, and depth of cut t-on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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“…Thus, incorporating the fractional concept with the HSA algorithm enhances the iteration speed for the polyphase code design. The FHSA uses the properties of the fractional theory (Pires et al , 2010; Ren-Qiang et al , 2009; Mahata et al , 2016; Pu, 2016) and the HSA (Sergio et al , 2012; Geem et al , 2001; Reza et al , 2012; Yi et al , 2016; Gil-Lopez et al , 2011).…”

Section: Proposed Methodology: Optimal Polyphase Code Design Using Th...mentioning

confidence: 99%

FHSA: fractional harmony search algorithm for polyphase code design in radar signal processing

Rao¹,

V.²

2022

IJPCC

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Purpose The purpose of this research is to design and develop a technique for polyphase code design for the radar system. Design/methodology/approach The proposed fractional harmony search algorithm (FHSA) performs the polyphase code design. The FHSA binds the properties of the harmony search algorithm and the fractional theory. An optimal fitness function based on the coherence and the autocorrelation is derived through the proposed FHSA. The performance metrics such as power, autocorrelation and cross-correlation measure the efficiency of the algorithm. Findings The performance metrics such as power, autocorrelation and cross-correlation is used to measure the efficiency of the algorithm. The simulation results show that the proposed optimal phase code design with FHSA outperforms the existing models with 1.420859, 4.09E−07, 3.69E−18 and 0.000581 W for the fitness, autocorrelation, cross-correlation and power, respectively. Originality/value The proposed FHSA for the design and development of the polyphase code design is developed for the RADAR is done to reduce the effect of the Doppler shift.

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“…Harmony search (HS) is a novel heuristic optimization algorithm proposed by Geem et al [19] in 2001, which has attracted much public attention and was successfully applied in solving economic load dispatch problem [20], the switch location problem (SLP) [21], the design of steel frames [22], clustering [23], multicast routing [24], predicting the optimal cutting conditions [25], and the optimization of energy efficient network migration [26], while using HS algorithm to solve ASP problems has been rarely studied yet. To find a better way for solving ASP problems, an improved harmony search (IHS) is introduced here.…”

Section: Introductionmentioning

confidence: 99%

Assembly sequence planning based on an improved harmony search algorithm

Qin

Zeng

et al. 2015

Int J Adv Manuf Technol

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As a typical, discrete, and NP-hard problem, assembly sequence planning (ASP) has direct impact on assembly quality and costs. So far, lots of graph-based and meta-heuristic approaches cannot solve this problem effectively. This paper proposes an effective ASP algorithm based on the harmony search (HS) algorithm, which has an outstanding global search ability to obtain the global optimum more efficiently. To solve the ASP problem, an improved harmony search (IHS) algorithm is proposed mainly in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. Finally, the advantage of the proposed ASP algorithm over the competing algorithms in solving ASP problems is verified by two experiments. Moreover, the proposed ASP algorithm is applied to a practical problem, a propeller of azimuth thruster to verify its practicability.

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Constrained optimum surface roughness prediction in turning of X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm

Cited by 18 publications

References 18 publications

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

FHSA: fractional harmony search algorithm for polyphase code design in radar signal processing

Assembly sequence planning based on an improved harmony search algorithm

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