Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN

Efkolidis, Nikolaos; Markopoulos, Angelos P.; Karkalos, Nikolaos E.; García-Hernández, César; Talón, José Luís Huertas; Kyratsis, Panagiotis

doi:10.1080/08839514.2019.1646014

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…The effect process prediction summarizes applications which predict outcomes of production processes or operations. Efkolidis et al (2019) for example predicted the thrust force and torque during drilling of a workpiece. Xu et al (2021a) estimated the tool wear of coated tool during cutting operations.…”

Section: Resultsmentioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

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Advanced analytics applications in smart manufacturing provide beneficiary effects such as predictive maintenance, energy optimized factories, and automized quality management of products. While the benefits and thus the sustainability effects are well investigated, the research field is characterized by many single applications with little conceptual synthesis. Therefore, this research aims to systematize the field and to identify specific sustainability themes in context of advance analytics applications. This chapter provides an overview of existing advanced analytics applications in smart manufacturing and clusters the addressed sustainability themes. Applying a systematic literature review, following the PRISMA statement, 65 different applications within four research perspective could be identified. Furthermore, ten different sustainability-related effects of the applications in smart manufacturing unfold. The content analysis resulted in 27 sustainability themes, which holistically cover all spheres of sustainability, with a focus on the social and economic spheres.

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Section: Resultsmentioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

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“…The performance of the network can be affected by factors such as the network model used; the training algorithms prepared, the number of intermediate layers and neurons, and the number of samples [31,32]. LM algorithm can be combined with some algorithms such as the genetic algorithm (GA) to achieve optimization suitability, higher success, calculate results in a shorter time, and high convergence.…”

Section: Discussionmentioning

confidence: 99%

BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model

BARIŞIK¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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With the advancement of sciences such as machine learning, deep learning, and artificial intelligence, various algorithms are designed and developed. Learning and application models based on data types such as sensor data and databases of computers are made. The BLEVE effects, one of the most common types of fire in industries, are predicted using the Levenberg-Marquardt algorithm, which has become increasingly popular in recent years. Here, the equations in the TNO (Toegepast Natuurwetenschappelijk Onderzoek-Netherlands Applied Scientific Research Organization) (YellowBook Static) model of BLEVE are used.The aim of this research is to use artificial neural networks to predict the risk size of the BLEVE event. All the results from the TNO model of BLEVE effects were estimated using an artificial neural network model. Without utilizing equations, outcomes that are close to true results could be estimated in this method. Furthermore, findings were acquired fast, with linear outcomes in many settings. For this reason, the study's necessity and significance have shifted in this direction. Sixteen TNO model equations from BLEVE are applied, and heat flux values are calculated as a result. As a result of the studies, the BLEVE effects were predicted by the artificial neural network model created using the Levenberg-Marquardt algorithm. It is seen that the estimated results and the actual results calculated are close to each other. The statistical values between the predicted results of the artificial neural network model created by the Levenberg-Marquardt algorithm and the actual results were examined. The average relative error between the last stage of the BLEVE model created with ANN and the actual values was 3.34%. In addition, it has been observed that when the training iteration is increased within the algorithm, it gets closer to the real results, and statistical values such as standard error decrease even more. In other words, the computation was done by increasing the iteration sequence processing cycle of the result estimations and repeated with the number of training iterations in order to better evaluate the network's performance. It has been observed that as the number of iterations increases, closer and more realistic results emerge.

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“…For each neuron, a summation operation is first performed for the inputs that are multiplied by the appropriate weights, and then, the output is produced with the use of an "activation function." The output should be in the range [0, 1] and exhibit a behavior comparable to that of the activation of a biological neuron [23]. Therefore, a function with a sigmoid curve shape, such as a hyperbolic tangent function, is selected.…”

Section: Artificial Neuralmentioning

confidence: 99%

“…Based on the optimal structure and decay coefficient value, the proposed approach was compared to other classification methods, as well as to a nontuned ANN, and it was found that the GA-ANN performed better than other approaches. Efkolidis et al [23] used GA-ANN to predict the thrust (Fz) and torque (Mz) during the drilling of St60 work pieces. The structure, connection weights, and training algorithms of an ANN were optimized in turn based on GA.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Therefore, based on the trial-and-error method, 11 single-hidden-layer ANN models for predicting TCS were constructed and the number of neurons in the hidden layer ranged from 3 to 13. According to the study results from the literature [23], the Levenberg-Marquardt (LM) algorithm was selected as the training algorithm. Taking RMSE as the ANN model performance measurement standard, the lower the RMSE is, the better the model performance.…”

Section: Simple Ann Modelmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

et al. 2021

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Rock compressive strength is an important mechanical parameter for the design, excavation, and stability analysis of rock mass engineering in cold regions. Accurate and rapid prediction of rock compressive strength has great engineering value in guiding the efficient construction of rock mass engineering in a cold regions. In this study, the prediction of triaxial compressive strength (TCS) for sandstone subjected to freeze-thaw cycles was proposed using a genetic algorithm (GA) and an artificial neural network (ANN). For this purpose, a database including four model inputs, namely, the longitudinal wave velocity, porosity, confining pressure, and number of freeze-thaw cycles, and one output, the TCS of the rock, was established. The structure, initial connection weights, and biases of the ANN were optimized progressively based on GA. After obtaining the optimal GA-ANN model, the performance of the GA-ANN model was compared with that of a simple ANN model. The results revealed that the proposed hybrid GA-ANN model had a higher accuracy in predicting the testing datasets than the simple ANN model: the root mean square error (RMSE), mean absolute error (MAE), and R squared ( R 2 ) were equal to 1.083, 0.893, and 0.993, respectively, for the hybrid GA-ANN model, while the corresponding values were 2.676, 2.153, and 0.952 for the simple ANN model.

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Optimizing Models for Sustainable Drilling Operations Using Genetic Algorithm for the Optimum ANN

Cited by 10 publications

References 24 publications

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model

Intelligent Prediction Model of the Triaxial Compressive Strength of Rock Subjected to Freeze-Thaw Cycles Based on a Genetic Algorithm and Artificial Neural Network

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