Modeling and optimization of turning duplex stainless steels

Koyee, Rastee D.; Heisel, Uwe; Eisseler, Rocco; Schmauder, Siegfried

doi:10.1016/j.jmapro.2014.05.004

Cited by 57 publications

(29 citation statements)

References 36 publications

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“…Recently, Koyee et al . () used TOPSIS to overcome the conflict between the need to minimise production cost and to maximise the production rate in manufacturing, and then neural network‐based models were developed to study the major variables that affected this trade‐off.…”

Section: Methodsmentioning

confidence: 99%

Predicting Efficiency in Angolan Banks: A Two‐Stage TOPSIS and Neural Networks Approach

Barros

Macanda

2015

South African J Economics

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This paper presents an efficiency assessment of the Angolan banks using Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). TOPSIS is a multi‐criteria decision‐making technique similar to data envelopment analysis, which ranks a finite set of units based on the minimisation of distance from an ideal point and the maximisation of distance from an anti‐ideal point. In this research, TOPSIS is used first in a two‐stage approach to assess the relative efficiency of Angolan banks using the most frequent indicators adopted by the literature. Then, in the second stage, neural networks are combined with TOPSIS results as part of an attempt to produce a model for banking performance with effective predictive ability. The results reveal that variables related to cost structure have a prominent negative impact on efficiency. Findings also indicate that the Angolan banking market would benefit from higher level of competition between institutions.

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Section: Methodsmentioning

confidence: 99%

Predicting Efficiency in Angolan Banks: A Two‐Stage TOPSIS and Neural Networks Approach

Barros

Macanda

2015

South African J Economics

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“…The surface machining is carried out to improve the quality of the surface layer of the materials. Thus is a need to shape the desired properties of the surface layer by selecting appropriate technological processes [2][3][4][5][6][7][8][9][10][11][12][13]18]. It is significant that in special applications of corrosion resistant steel, low surface roughness is obtained.…”

Section: Introduction To the Topicmentioning

confidence: 99%

The Effect of Parameters and Geometry Cutting Edge After Turning of the Duplex Cast Steel on the Maximum Tool Flank Wear

Dyl

2019

Journal of KONES

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It is important to shape the required properties of the surface layer in the technological process. This issue is an important problem due to the ability of the kinematic pair elements to the required reliable operation of machine parts. The latest generation devices work with increasing operational loads. This forces the search forever-newer construction materials or innovative manufacturing engineering technologies that would ensure high reliability and durability of machine components. For mechanical engineering, continue to be used on steel structures of machine parts. In the petrochemical and shipbuilding industry, stainless steels are very poplars. Those materials are used in the constructions of seawater and acid installations. Currently, a newer material with more favourable properties is two-phase stainless steel. This is the so-called duplex steel. It can be applied to pump shafts in acid or seawater solutions. Duplex cast steel is a difficult-to-cut material. It is important to determine the effect of cutting parameters on the surface quality of the shafts and wear of cutting edge. Traditional methods of finishing surface treatment of shafts are machining (turning, grinding, superfinishing). Considering the possibilities of equipping a marine power plant workshop, it would be best to use machining by turning. The paper specifies the relationship between the wear of the cutting edge and the geometrical structure of the machined surface as well as the type of tool material used and the shape of the cutting inserts.

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“…According to Mukherjee and Ray [17], traditional techniques, such as Taguchi and RSM, are commonly used to optimize machining processes. However, modern techniques that are considered as non-conventional approaches, such as genetic algorithms (GA), particle swarm optimization, and mixture designs have been used as an alternative for estimating the optimal result in machining [23,12,29].…”

Section: Introductionmentioning

confidence: 99%

“…Koyee et al [12] studied the turning of duplex stainless steel of the grades EN 1.4462 and EN 1.4410 DEDM using the response surface methodology and multilayer perceptron artificial neural network. According to the authors, after the three phases, the machinability of EN 1.4462 was higher than the machinability of EN 1.4410 and the use of lower cutting speed, intermediate feed rate, and lower depth of cut ranges tend to maximize the operational sustainability index in dry and wet cutting.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

et al. 2016

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Modeling and optimization of turning duplex stainless steels

Cited by 57 publications

References 36 publications

Predicting Efficiency in Angolan Banks: A Two‐Stage TOPSIS and Neural Networks Approach

Predicting Efficiency in Angolan Banks: A Two‐Stage TOPSIS and Neural Networks Approach

The Effect of Parameters and Geometry Cutting Edge After Turning of the Duplex Cast Steel on the Maximum Tool Flank Wear

Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

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