Optimization of stamping process parameters based on an improved particle swarm optimization–genetic algorithm and sparse auto-encoder–back-propagation neural network model

Xie, Yanmin; Liu, Cheng; Li, Wei; Du, Meiyu; Feng, Kai

doi:10.1080/0305215x.2022.2152018

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…To try to reduce unsatisfactory defects conducted by the stamping process, such as WRI and cracking, [14] performs optimization on its parameters based on an improved Particle Swarm Optimization-Genetic Algorithm and Sparse Autoencoder-back-propagation Neural Network model. A double-C piece is used for the study, and its thickness variation is used as the assessment standard.…”

Section: Related Workmentioning

confidence: 99%

Surrogate Model and Multi-objective Evolutionary Algorithm Applied to Automotive Stamping

Silva,

Carneiro,

Tepedino

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Automotive parts stamping is a process of forming metallic parts that are used in the manufacture of automobiles, such as the Internal Tailgate. The problem is characterized as a multi-objective optimization problem, as it involves the optimization of multiple antagonistic objectives simultaneously. In this study, we used the Extra Trees multivariate regression algorithm to characterize the problem and use it as a surrogate model for the Non-dominated Sorting Genetic Algorithm II. Our goal is to explore the possibilities of simultaneously minimizing Fracture, Insufficient Elongation, and Wrinkling. In addition, we apply data correlation analysis and machine learning model sensitivity analysis tools to assess the quality of the surrogate model and allow preliminary decision-making. The tools of data correlation analysis and sensitivity analysis of the Machine Learning model are applied to assess the quality of the surrogate model and allow prior decision-making. The results achieved when using the proposed approach with these auxiliary tools help to establish an efficient model, identify suitable stamping parameters, and reduce the costs associated with conducting empirical research conducted by experts in the field.

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Section: Related Workmentioning

confidence: 99%

Surrogate Model and Multi-objective Evolutionary Algorithm Applied to Automotive Stamping

Silva,

Carneiro,

Tepedino

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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“…Bayesian inference can be used to estimate the parameters, θ, of a model and their associated uncertainty, given the available data. This is useful for informing robust engineering designs that can tolerate this uncertainty; see example applications in [1]- [7]. The approach relies on Bayes' theorem in which the modeler uses their knowledge of the system's physical behavior and mathematical constraints to develop a prior probability distribution for the parameters, p(θ), that is updated by the likelihood of observing the data x, p(x|θ).…”

Section: Introductionmentioning

confidence: 99%

New Diagnostic Assessment of MCMC Algorithm Effectiveness, Efficiency, Reliability, and Controllability

Kavianihamedani,

Quinn,

Smith

2024

IEEE Access

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Markov Chain Monte Carlo (MCMC) is a robust statistical approach for estimating posterior distributions. However, the significant computational cost associated with MCMC presents a considerable challenge, complicating the selection of an appropriate algorithm tailored to the specific problem at hand. This study introduces a novel and comprehensive framework for evaluating the performance of MCMC algorithms, drawing inspiration from diagnostics used for multi-objective evolutionary algorithms. We employ visualizations to evaluate key algorithmic characteristics: Effectiveness (the ability to accurately find representative posterior modes, quantified by the Kullback-Leibler Divergence (KLD) and Wasserstein Distance (WD)), Efficiency (the speed of posterior characterization), Reliability (consistency across different random seeds), and Controllability (insensitivity to hyperparameter variation). Evaluating three prominent MCMC algorithms-Metropolis-Hastings (MH), Adaptive Metropolis (AM), and Differential Evolution Adaptive Metropolis (DREAM)-on high-dimensional and bimodal test problems, our analysis uncovers several insights. First, across algorithms, the number of function evaluations most controls performance on the high-dimensional problem, while the number of chains most controls performance on the bimodal problem. While this suggests similar controllability across algorithms, differences emerge on the other algorithmic characteristics. For high numbers of functions evaluations, AM performs best on the highdimensional problem, while for low (<5) and high (>15) chain counts, MH and AM perform best on the bimodal problem, as measured by KLD. However, outside these specific cases, DREAM consistently demonstrates superior efficiency and reliability, making it a robust choice for both high-dimensional and multimodal problems. These findings can inform MCMC algorithm selection for Bayesian inference applications, as well as hyperparameterization of the chosen algorithm. More importantly, the diagnostics represent a generalizable contribution to research on MCMC diagnostics that can be used to evaluate and inform the design of new algorithms. INDEX TERMS Bayesian estimation, Markov Chain Monte Carlo, high dimensionality, multi-modality, model diagnosticsThis article has been accepted for publication in IEEE Access.

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Feature Variable Selection for Near‐Infrared Spectroscopy Based on Simulated Annealing Bee Colony Algorithm

Shi,

Tong,

Liu

et al. 2024

Journal of Chemometrics

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Variable selection is an effective method to enhance the modeling performance of near‐infrared spectroscopy. Given the promising application prospects of intelligent optimization algorithms in spectral feature variable selection, this article combines the artificial bee colony algorithm with the simulated annealing algorithm to construct a simulated annealing bee colony algorithm (SABC). To explore the feasibility of SABC for spectral variable selection, SABC was applied to construct a partial least squares spectral quantitative detection model for corn stover cellulose and soil organic matter contents. The modeling performance was compared with that of the full spectrum, genetic algorithm, simulated annealing algorithm, and artificial bee colony algorithm; it was found that the model regression precision established by SABC was the best. For the cellulose and organic matter content detection models, the coefficients of determination of the validation set were 0.9433 and 0.9853, with the relative root mean squared error of 1.7901% and 0.8011%, and the residual prediction deviation of 4.1741 and 8.3931, respectively, which could meet the corresponding actual detection needs. SABC adopted the strategy of multiple runs to select the repeated wavelength variables, effectively reduced variable dimensions and model complexity, improved the prediction performance of the regression model, and provided a new approach for building a high‐performance near‐infrared spectroscopy (NIRS) quantitative calibration model.

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Optimization of stamping process parameters based on an improved particle swarm optimization–genetic algorithm and sparse auto-encoder–back-propagation neural network model

Cited by 7 publications

References 31 publications

Surrogate Model and Multi-objective Evolutionary Algorithm Applied to Automotive Stamping

Surrogate Model and Multi-objective Evolutionary Algorithm Applied to Automotive Stamping

New Diagnostic Assessment of MCMC Algorithm Effectiveness, Efficiency, Reliability, and Controllability

Feature Variable Selection for Near‐Infrared Spectroscopy Based on Simulated Annealing Bee Colony Algorithm

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