A fuzzy based decision model for nontraditional machining process selection

Temucin, Tolga; Tozan, Hakan; Vayvay, Özalp; Harničárová, Marta; Valíček, Ján

doi:10.1007/s00170-013-5474-z

Cited by 51 publications

(44 citation statements)

References 27 publications

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“…Chakraborty [14] applied multi-objective optimization by ratio analysis (MOORA) method to choose NTMPs for various engineering applications. Temuçin et al [15] provided some methodical approaches and a decision support model in fuzzy and crisp situations to deal with NTMP selection problems. Karande and Chakraborty [16] applied an integrated preference ranking organization method for enrichment evaluation (PROMETHEE) as well as a geometrical analysis for an interactive aid (GAIA) method for solving NTMP selection problems.…”

Section: Review Of Literature On Ntmp Selectionmentioning

confidence: 99%

“…The suggested approach would act as a visual decision support to the process engineers. Temucin et al [17] proposed a fuzzy decision support system for selecting NTMPs considering the vagueness of several inter-related decision criteria. Choudhury et al [18] developed a TOPSIS-AHP based expert system for NTMP selection and considered several paradoxical criteria.…”

Section: Review Of Literature On Ntmp Selectionmentioning

confidence: 99%

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A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

Chatterjee

Mondal²,

Boral

et al. 2017

FU Mech Eng

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Section: Review Of Literature On Ntmp Selectionmentioning

confidence: 99%

Section: Review Of Literature On Ntmp Selectionmentioning

confidence: 99%

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

Chatterjee

Mondal²,

Boral

et al. 2017

FU Mech Eng

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show abstract

“…Support vector machine (SVM) is a supervised classifier which aims to find hyper-planes that separate the dataset with a maximum margin [12]. Given a set of labeled data (x 1 , y 1 ), ..., (x n , y n ), where x i is a multidimensional sample vector and…”

Section: Discussionmentioning

confidence: 99%

“…The classification model obtained by SVM is a hyperplane that maximizes the width of the margin between the classes while minimizing the margin of errors [11,12]. Convex optimization theory was applied in the algorithm to train and find a hyper-plane [13].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Remote Sensing Image Sample Selection Based on Convex Theory

Pan

Sun

2015

Int. J. Onl. Eng.

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Abstract-Advancements in remote sensing technology have led to improvements in the acquisition of land cover information. The extraction of accurate and timely knowledge about land cover from remote sensing imagery largely depends on the classification techniques used. Support vector machine has been receiving considerable attention as a promising method for classifying remote sensing imagery. However, the support vector machine learning process typically requires a large memory and significant computation time for treating a large sample set, in which some of the samples might be redundant and useless for the support vector machine model training. Therefore, higher-quality and fewer samples from the sample selection should be utilized for support vector machine-based remote sensing classification. A convex theory-based remote sensing sample selection algorithm for support vector machine classifiers is developed in this work. A Landsat-5 Thematic Mapper imagery acquired on August 31, 2009 (orbit number 113/27) is adopted in our experiments. The study area's land cover/use was divided into five categories. Using the region of interest tool, we select samples from the image of the study area, with each category consisting of 1000 independent pixels. Results show that for most cases, our method can achieve higher classification accuracy than random sample selection method.

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“…In contrast to shrinkage, deformation is defined as a change in shape while maintaining the constant volume of the mould. [2] These two concepts need to be distinguished, because a significant difference between these two concepts exists there, while deformation is very often the result of shrinkage. Figure1 shows the course of the change of pressure, volume, and temperature in injection moulding of thermoplastics.…”

Section: State Of the Artmentioning

confidence: 99%

Application of Simulation for Product Quality Enhancement

Monková¹,

Hric²,

Knapčíková³

et al. 2017

dtetr

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Abstract. The article deals with a problem related to a product produced by injection moulding technology focusing on the importance to design the product geometry in the right way. The mould structure is often characterized by uneven wall thickness throughout the entire volume. Thicker walls and a greater accumulation of material can cause the overflow and other distortions. The accumulated material solidifies unequally, so internal tension is developing. Its influence may result in various deformations and surface defects. There is described the application of specific solution for the mould wall thickness correction in real practice. The relief pins method is concerned that was used at the product with commercial name Box Series S-BOX 106 made of polyamide PA 66. After proposed modification of the form, the mould quality has improved and production efficiency has increased. Software Cadmold 3D-f was used for simulation of cooling and injection pressure.

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A fuzzy based decision model for nontraditional machining process selection

Cited by 51 publications

References 27 publications

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

A Novel Hybrid Method for Non-Traditional Machining Process Selection Using Factor Relationship and Multi-Attributive Border Approximation Method

A New Approach for Remote Sensing Image Sample Selection Based on Convex Theory

Application of Simulation for Product Quality Enhancement

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