Conservative multi-objective optimization considering design robustness and tolerance: a quality engineering design approach

Lee, Jong-Soo; Kwon, Yu-Jin

doi:10.1007/s00158-012-0823-3

Cited by 11 publications

(2 citation statements)

References 21 publications

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“…In the analysis of a manufacturing cost–tolerance model, the manufacturing process involves various machining characteristics. A number of mathematical models were developed using linear and exponential functions (Saravanan et al ., 2014), combined square and exponential functions (Lee and Kwon, 2013). When the capability of the model increases, the error function governing the model was also found increasing (Reed and Gillies, 2016).…”

Section: Introductionmentioning

confidence: 99%

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Saravanan

Jerald

Rani

2020

AIEDAM

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The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

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Section: Introductionmentioning

confidence: 99%

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Saravanan

Jerald

Rani

2020

AIEDAM

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“…There are a considerable number of studies addressing robust optimization (Beyer and Sendhoff 2007). However, robustness is not frequently included in multi-objective optimization algorithms (Ferreira et al 2008), and qualitative analysis has only recently been proposed (Lee and Kwon 2013).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary robustness analysis for multi-objective optimization: benchmark problems

Gaspar‐Cunha

Ferreira

Recio

2013

Struct Multidisc Optim

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This paper presents a new approach to robustness analysis in multi-objective optimization problems aimed at obtaining the most robust Pareto front solutions and distributing the solutions along the most robust regions of the optimal Pareto set. A new set of test problems accounting for the different types of robustness cases is presented in this study. Non-dominated solutions are classified according to their degree of robustness and are distributed along the Pareto front according to specific algorithm parameter values. Verification of the proposed method is carried out using the developed test problems and artificial and real world benchmark test problems present in the literature.

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Structural design of a level-luffing crane through trajectory optimization and strength-based size optimization

Kim

Lee

2014

Struct Multidisc Optim

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Conservative multi-objective optimization considering design robustness and tolerance: a quality engineering design approach

Cited by 11 publications

References 21 publications

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

Evolutionary robustness analysis for multi-objective optimization: benchmark problems

Structural design of a level-luffing crane through trajectory optimization and strength-based size optimization

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