A multiobjective optimization model for machining quality in the AISI 12L14 steel turning process using fuzzy multivariate mean square error

Gaudêncio, Juliana Helena Daroz; Almeida, Fabrício Alves de; Turrioni, João Batista; Quinino, Roberto da Costa; Balestrassi, Pedro Paulo; Paiva, Anderson Paulo de

doi:10.1016/j.precisioneng.2019.01.001

Cited by 20 publications

(8 citation statements)

References 31 publications

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“…, β p ] of coefficients is estimated using the ordinary least-squares (OLS) regression method [35]. This methodology has been extensively used in [27], [36], [37].…”

Section: B Response Surface Methodologymentioning

confidence: 99%

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

et al. 2020

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One of the main goals in flux-cored arc welding processes is the optimization of bead geometry, in which multiple geometric characteristics of the welding bead are important; therefore, multiobjective optimization programming is often applied. However, several optimization problems that use stochastic programming do not consider the impact of the correlation between the output variables on their probabilistic constraints. In this context, this paper aims to present a multiobjective optimization method based on multivariate stochastic programming. To demonstrate the applicability of the proposal, we conducted a design of experiments to optimize a flux-cored arc welding process for stainless-steel claddings. The weighting-sums method was applied to formulate the multiobjective optimization problem. It was possible to formulate a multivariate probability distribution for the penetration and dilution. In addition, a 95% probability to meet the predefined specification limits of the geometric characteristics was achieved. INDEX TERMS Bead geometry, bivariate normal distribution, flux-cored arc welding, multiobjective optimization programming, stainless steel claddings, stochastic programming.

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“…, β p ] of coefficients is estimated using the ordinary least-squares (OLS) regression method [35]. This methodology has been extensively used in [27], [36], [37].…”

Section: B Response Surface Methodologymentioning

confidence: 99%

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

et al. 2020

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“…Content may change prior to final publication. (20) The variance components for the GR&R study are presented in Table 5, where MSA, MSB and MSAB represent the mean squares for the part factor, operator factor, interaction term, respectively, and MSE the mean square for the error term. Based on the previous analyses, the measurement system must be classified by the contributions to the percentage of variability (%R&Rm) and also the number of distinct categories (ndcm).…”

Section: ( ) ( )mentioning

confidence: 99%

“…The use of multivariate strategies in engineering problems is a modern practice, as it is in optimization methods [20,21]. However, searching to improve the process using optimization and other strategies may not bring enough results, since the variability is often attributed to the measurement process [22].…”

Section: Introductionmentioning

confidence: 99%

A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation

et al. 2020

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A new approach to identify and diagnose the quality of extensive and multivariate data is presented, using the gage repeatability and reproducibility (GR&R) study through the weighting of rotated factor scores. The proposal uses axis rotation to improve the explanation and interpretations of latent information, providing a statistically appropriate alternative when dealing with two or more correlated data sets. To analyze data with a significant variance-covariance structure, factor analysis (FA) is applied for calculating the eigenvalues and extracting of the rotated scores. Once obtained, these scores are then weighted with their respective eigenvalue for each factor. This procedure results in a single response vector, which is capable of properly interpreting all of the quality responses analyzed. To illustrate an application of the method, a real data set from a resistance spot welding process is selected, and two different types of rotation are compared. The proposed method provided an output that contemplated all of the significant variability of the data in a unique and significant way. In addition, the method enabled a reduction in the data dimensionality, thus minimizing the time for analysis and computational effort. INDEX TERMS Multivariate measurement system, repeatability and reproducibility, orthogonal rotation, weighted factor analysis, resistance spot welding.

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“…In manufacturing processes that have many quality characteristics, one must also verify the data's multi-correlation [2], so the principal components' scores of these measurements are also presented, along with their eigenvalues and eigenvectors. The information based on principal components was used due to the great applicability of this strategy, as in works of [3,4]. There are two distinct conditions in this data set, where each of them presents 270 information for the original data and 270 information of the principal components' scores.…”

Section: Datamentioning

confidence: 99%

Measurement data from bobbins of Partially Oriented Yarns: Univariate and multivariate aspects

et al. 2019

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In this paper, we present data from measurements made in the textured fibers bobbins in two different conditions, presenting critical quality characteristics such as diameter, mass and density. In order to obtain a significant amount of information, in each of the two conditions, 270 measurements were obtained for each of the quality characteristics. Three different equipments (Automatic Package Analyzer - APA) were used in ten different parts, replicated three times for each of them. Considering the two measurement data collection, an amount of 540 bobbins measurements were obtained. Almeida et al., (2019) applied these measurement data in his study. Taking into account the multicorrelated nature of the information, we also have the representation of the principal components' scores for these measurements, besides the eigenvalues and eigenvectors of the data.

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A multiobjective optimization model for machining quality in the AISI 12L14 steel turning process using fuzzy multivariate mean square error

Cited by 20 publications

References 31 publications

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

Multivariate Stochastic Optimization Approach Applied in a Flux-Cored Arc Welding Process

A Gage Study Through the Weighting of Latent Variables Under Orthogonal Rotation

Measurement data from bobbins of Partially Oriented Yarns: Univariate and multivariate aspects

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