Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process

Cruz, Yarens J.; Rivas, Marcelino; Quiza, Ramón; Villalonga, Alberto; Haber, Rodolfo E.; Beruvides, Gerardo

doi:10.1016/j.compind.2021.103530

Cited by 38 publications

(19 citation statements)

References 41 publications

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“…An artificial neural network (ANN) is an automatic learning and processing paradigm inspired by the functioning of the human nervous system [58], [59], [65]- [67], [74]. A neural network is composed of a set of neurons interconnected by links, where each neuron takes as inputs the outputs of the preceding neurons, multiplies each of these inputs by a weight and, by means of an activation function, calculates an output.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a twomonth field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage.Results showed that the model (R 2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

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Section: Methodsmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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“…The expression of the motor drive torque T m can be obtained as shown in [ 27 ]

where m is the equivalent mass of the rotor system; g is gravitational acceleration; x is the displacement of the motor axis in the X -direction; y is the displacement of the motor axis in the Y -direction; and T l is the load torque of the rotor system.…”

Section: Misalignment Malfunction Model and Diagnosis Mechanismmentioning

confidence: 99%

“…where θ is the angular position of the drive shaft; ω m is the rotational angular velocity of the drive shaft; t is the time; and φ is the initial angular position. e expression of the motor drive torque T m can be obtained as shown in [27]…”

Section: Parallel Misalignment Malfunctionmentioning

confidence: 99%

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Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method

Yu²

2022

Computational Intelligence and Neuroscience

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Aiming at the quantitative diagnosis of shafting misalignment malfunction, a novel method based on speed signal with singular value decomposition and Hilbert transform (SVD-HT) and cubic spline fitting-Pareto particle swarm optimization-echo state network (CSF-PPSO-ESN) method is proposed. The malfunction diagnosis mechanism based on the speed signal is obtained by constructing the shaft misalignment malfunction model. Then, the SVD-HT and CSF-PPSO-ESN methods are applied to obtain the relationship between the shaft misalignment malfunction and the amplitude of the time and the rotation frequency (fr) component of the speed signal. The parameters of the CSF-PPSO-ESN method are settled according to the shaft misalignment malfunction and the fr component of the speed signal. The accuracy of the proposed method is verified by using the fr component of the speed signal and the trained CSF-PPSO-ESN to obtain the value of the shaft misalignment malfunction. The repeating experimental results show that the diagnosing error of the shaft misalignment malfunction can reach less than ±10 μm. The method presented in this paper provides a novel way to diagnose shaft misalignment malfunction quantitatively.

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“…Zhao and Yao proposed a novel prognostic framework based on multisensory signal features and called it a multidislocation time series convolutional neural network (MDTSCNN) [ 26 ]. Cruz et al combined an integrated convolutional neural network and an evolutionary algorithm to propose a novel image classification method [ 27 ]. Xie et al proposed a short-term electric load forecasting method combining the Elman neural network and particle swarm optimization algorithm [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode

Tang

2022

Computational Intelligence and Neuroscience

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With the dynamic sharing of global resources shifted from the independent production of a single enterprise to and the development of collaborative manufacturing, today’s manufacturing industry has collaborative production among multiple enterprises. This paper extends the optimization method of the multienterprise dynamic equipment collaborative scheduling system with the dynamic manufacturing network theory approach, combining the neural network system and model predictive control theory to continuously and dynamically diagnose the changes of demand and capacity in the actual production process and design the corresponding multilayer game collaborative optimization robust model according to the uncertainty relationship. The study considers the production characteristics and management needs of multiple enterprises within the manufacturing industry, as well as the optimization of the dynamic equipment collaborative scheduling system, uncertain demand, dynamic equipment matching effectiveness, and equipment failure under the shared manufacturing model, deduces the market capacity demand for manufacturing enterprises by inverse prediction of dynamic market demand through the simple recurrent neural network (Elman network), and then maximizes the idle equipment utilization and load capacity under the shared manufacturing model in model rolling optimization. This paper gives model solutions based on big data analysis and combines improved population intelligence algorithms to determine the dynamic matching of manufacturing resources, product supply and demand paths, and transportation flows. The simulation results show that the application of model predictive control theory to the model of Elman neural network system diagnosis can effectively reduce the adverse effects of the two-way uncertainty environment on the profitability of enterprises. Meanwhile, considering the maximum performance matching of dynamic equipment scheduling and combining with robust optimization methods will reduce the cost loss of manufacturing enterprises in the worst case, and the cost loss reduction can reach 24.73%. The analysis results verify that the method is of great practical significance in the direction of intelligent production collaboration and industrial digital transformation.

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Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process

Cited by 38 publications

References 41 publications

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method

Multilayer Game Collaborative Optimization Based on Elman Neural Network System Diagnosis in Shared Manufacturing Mode

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