Back-propagation Artificial Neural Network Research Articles

Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions.

Comparison of machine learning and statistical approaches to estimate rock tensile strength

Tensile strength is very important in drilling operations. The main objective of this study was to assess petrography, physical, and mechanical properties and predict the Brazilian tensile strength of sedimentary rocks by leveraging key parameters such as Schmidt hardness, compressional wave velocity, density, and porosity. A diverse array of predictive models was employed, encompassing simple regression, multivariate linear and nonlinear regression, backpropagation artificial neural network, gaussian process regression, classification and regression tree, K-nearest neighbor, random forest, and support vector regression. Based on thin section analysis and X-ray diffraction, the samples were identified. The sandstone samples were meticulously categorized into two distinct groups: arenite and litharenite. Additionally, the limestone samples were stratified into the categories of packstone to mudstone based on texture. The highest failure mode frequency of the samples under the Brazilian tensile strength test was identified as central fracturing. Upon meticulous examination, it was discerned that compressional wave velocity exerted the most substantial influence on Brazilian tensile strength estimates, while density exhibited the least impact. Comparing the outcomes derived from the diverse modeling techniques, it was unequivocally established that the support vector regression model showcased the highest level of performance for forecasting Brazilian tensile strength. This was evidenced by the remarkable coefficient of determination of 0.99 along with an impressively low root mean square error of 0.03.

Sensitivity analysis of the mechanical properties on atherosclerotic arteries rupture risk with an artificial neural network method

Considering the differences between individuals, in this paper, an uncertainty analysis model for predicting rupture risk of atherosclerotic arteries is established based on a back-propagation artificial neural network. The influence of isotropy and anisotropy on the rupture risk of atherosclerotic arteries is analyzed, and the results demonstrate the effectiveness of the artificial neural network in predicting the rupture risk. Moreover, the rupture risk of atherosclerotic arteries at different inflation sizes are simulated. This study contributes to a better understanding of the underlying mechanisms of atherosclerotic arteries rupture and promotes the advancement of artificial neural networks in atherosclerosis research.

Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms

Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS) were selected as regression and prediction algorithms in this study; the particle swarm optimization (PSO) algorithm was also used to optimize the structure and hyperparameters of each algorithm. The statistical results show that the performance of the assembled algorithms is better than that of an NN-based algorithm. In addition, PSO can effectively improve the prediction accuracy of the ML algorithms. The comprehensive performance of each model is analyzed using a Taylor diagram, and the PSO-XGBoost model has the best comprehensive performance, with R2 and MSE equal to 0.9072 and 11.4546, respectively.

OPTIMASI METODE JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK PERAMALAN CURAH HUJAN BULANAN DI KOTA DENPASAR

Rainfall is a natural phenomenon that depends on many factors that are an important part of life on earth. The high intensity of rainfall can lead to disasters. Therefore, this study aims to forecast monthly rainfall. The data used was obtained from BMKG Bali Province, namely monthly rainfall data for Denpasar City from 2009 to 2019. The method used is backpropagation artificial neural network. The artificial neural network method is an information processing method inspired by the human nervous system. Optimal backpropagation network architecture is needed so that the prediction results have a low error rate, by optimizing the use of training data and test data taken from sample data. Based on the results of the testing and prediction process with the parameters of one hidden layer with 50 neorons, epoch 11 and learning rate 0.01, the results obtained with the MSE value in network testing are 0.037. So it can be concluded that the backpropagation artificial neural network method has good accuracy results used as a reference for decision making in predicting monthly rainfall in Denpasar City in the future.

Neutron-gamma discrimination with broaden the lower limit of energy threshold using BP neural network

Neutron-gamma discrimination is a tough and significative in experimental neutrons measurements procedure, especially for low-energy neutrons signal discrimination. In this work, based on the Pulse Shape Discrimination (PSD) and Back-Propagation (BP) artificial neural networks, a neutron-gamma discrimination method is developed to broaden the lower limit of energy threshold with the hidden layer of 20 neurons. Compared with neutron-gamma discrimination method based on PSD only, the developed neutron-gamma discrimination method based on the PSD and BP-ANN can discriminate neutron and gamma-ray signals with low energy threshold, which can discriminate signals up to 99.93%. Moreover, this work can reduce the energy threshold from 350 keV to 70 keV, as well as the acquired data utilization increased from 60% to more than 99.9%, which overcome the hardware limitations and distinguish neutron and gamma-ray signals, effectively. The developed neutron-gamma discrimination method and the trained neural network can be directly used to other experimental neutrons measurements.

Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

BackgroundAdequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine learning.Materials and methodsCephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. Back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences comparison.ResultsThe regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and II.ConclusionsRotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.

Optimizing milling parameters based on full factorial experiment and backpropagation artificial neural network of lamina milling temperature prediction model.

Milling operations of laminae in spinal surgery generate high temperatures, which can lead to thermal injury and osteonecrosis and affect the biomechanical effects of implants, ultimately leading to surgical failure. In this paper, a backpropagation artificial neural network (Bp-ANN) temperature prediction model was developed based on full factorial experimental data of laminae milling to optimize the milling motion parameters and to improve the safety of robot-assisted spine surgery. A full factorial experiment design were used to analyze the parameters affecting the milling temperature of laminae. The experimental matrixes were established by collecting the corresponding cutter temperature Tc and bone surface temperature Tb for the milling depth, feed speed and different bone densities. The Bp-ANN lamina milling temperature prediction model was constructed from experiment data. Increasing milling depth increases bone surface and cutter temperature. Increasing feed speed had little effect on cutter temperature, but decreased bone surface temperature. Increasing bone density of laminae increased cutter temperature. The Bp-ANN temperature prediction model had best training results in the 10th epoch, and there is no overfitting (training set R= 0.99661, validation set R= 0.85003, testing set R= 0.90421, all temperature data set R= 0.93807). The goodness of fit R of Bp-ANN was close to 1, indicating that the predicted temperature was in good agreement with the experiment measurements. This study can help spinal surgery-assisted robot to select appropriate motion parameters at different density bones to improve lamina milling safety.

Real-time monitoring of the corrosion behaviour of the 304SS in HCl solution using BPNN with joint image recognition and electrochemical noise

Electrochemical noise has an important reference role in the study of early corrosion; however, a direct correlation between the microscopic morphology and the electrochemical noise of the corrosion process is lacking. This study uses a back propagation artificial neural network (BPNN) optimized by three intelligent optimization algorithms to real-time monitor the local corrosion of 304 stainless steel in HCl solution with joint recognition and feature extraction of corrosion images and electrochemical noise. The results show that the corrosion monitoring model established by optimized BPNN with genetic algorithms exhibits the highest convergence speed and accuracy.

QSAR studies of quinoline alkaloids camptothecin derivatives for prediction anticancer activity using linear and nonlinear methods

AbstractQuinoline alkaloid camptothecin (CPT) derivatives are compounds with a wide range of inhibitory activities and they are the basis of several groups of drugs. CPT is one of the prominent lead compounds in anticancer drug development. It inhibits DNA topoisomerase I (topo I) enzyme and has shown remarkable anticancer activity against lung, colon, rectum, ovarian, breast, and stomach cancers. Quantitative structure–activity relationship (QSAR) is basically a statistical approach correlating the response activity data with descriptors encoding chemical information. In the current research, a QSAR study was performed for modeling and predicting the anticancer activity (pIC50) of 76 CPT derivatives as an inhibitor of DNA topo I using the genetic algorithm multiple linear regression (GA‐MLR) and back propagation artificial neural network (BP‐ANN) methods. The results of this study indicate that the use of constitutional and geometrical descriptors provide good estimate for pIC50. The obtained results of statistical criteria, internal and external validation show the superiority of BP‐ANN model than GA‐MLR to predict the pIC50 values of the investigated compounds. QSAR model developed in this study can be used for design and development of novel potent CPT derivatives and for predicting their anticancer activity.

Peristaltic transport of Sutterby nanofluid flow in an inclined tapered channel with an artificial neural network model and biomedical engineering application

Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg–Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.

Deciphering Soil Fertility of Tobacco Planting Fields with Back Propagation Artificial Neural Networks in Southwest China

Deciphering Soil Fertility of Tobacco Planting Fields with Back Propagation Artificial Neural Networks in Southwest China

Intelligence detection of oil absorption in French fries by surface profiles

Surface profiles are important evaluation indices for oil absorption behavior of fried foods. This research established two intelligent models of partial least-squares regression (PLSR) and back propagation artificial neural network (BP-ANN) for monitoring the oil absorption behavior of French fries based on the surface characteristics. Surface morphology and texture of French fries by rapeseed oil (RO) and high-oleic peanut oil (HOPO) at different temperatures were investigated. Results showed that oil content of samples increased with frying temperature, accounting for 37.7% and 41.4% of samples fried by RO and HOPO respectively. The increase of crust ratio, roughness and texture parameters (Fm, Nwr, fwr, Wc) and the decrease of uniformity were observed with the frying temperature. Coefficients of prediction set of PLSR and BP-ANN models were more than 0.93, which indicated that surface features combined with chemometrics were rapid and precise methods for determining the oil content of French fries.

Ensemble learning model on Artificial Neural Network - Backpropagation (ANN-BP) architecture for coal pillar stability classification

Pillars are important structural units used to ensure mining safety in underground hard rock mines. Unstable pillars can significantly increase worker safety hazards and sudden roof collapse. Therefore, precise predictions regarding the stability of underground pillars are required. One common index that is often used to assess pillar stability is the Safety Factor (SF). Unfortunately, such crisp boundaries in pillar stability assessment using SF are unreliable. This paper presents a novel application of Artificial Neural Network-Backpropagation (ANN-BP) and Deep Ensemble Learning for pillar stability classification. There are three types of ANN-BP used for the classification of pillar stability distinguished by their activation functions: ANN-BP ReLU, ANN-BP ELU, and ANN-BP GELU. These three activation functions were chosen because they can solve the vanishing gradient problem in ANN-BP. In addition, a Deep Ensemble Learning process was carried out on these three types of ANN-BP to reduce the prediction variance and improve the classification results. This study also presents two labeling alternatives for pillar stability by considering its suitability with the SF. Thus, pillar stability is expanded into four categories: failed with a suitable safety factor, intact with a suitable safety factor, failed without a suitable safety factor, and intact without a suitable safety factor. There are five features used for each model: pillar width, mining height, bord width, depth to floor, and ratio. In constructing the model, the initial dataset is divided into training data, validation data, and testing data. In this case, four type of proportions are used. For training-testing division the proportions are: 80 % : 20 %, 70 % : 30 %, for training-validation-testing division the proportions are: 80 % : 10 % : 10 %, 70 % : 15 % : 15 %. Average accuracy, F1-score, and F2-score from 10 trials were used as performance indicators for each model. The results showed that the ANN-BP model with Ensemble Learning could improve ANN-BP performance with an average accuracy 86.48 % and an F2-score 96.35 % for the category of failed with a suitable safety factor.

Smart solutions for dissolved oxygen control in semi-batch fermenters: A machine learning approach

The monitoring and measurement of dissolved oxygen (DO), plays a significant role in industrial effluent, and disposal of effluents has been a serious challenge to researchers. Aeration is the primary step in effluent treatment. The basic method for determining DO is direct measurement. This study has been carried out in effluent from the paper industry to estimate the performance of dissolved oxygen in a semi-batch lark hygiene fermenter and was oxygenated till saturation at 25 °C under real-time study. This study has been carried out using Multi-Layered Feed Forward Back Propagation Artificial Neural Network (ML-FFBPANN) and the experimental results were optimized with fourteen different machine learning algorithms such as Polak-Ribiere Conjugate Gradient (CGP), Conjugate Gradient with Powell/Beale Restarts (CGB), Bayesian Regularization (BR), Gradient Descent (GD), BFGS Quasi-Newton (BFG), GD with Momentum (GDM), Gaussian Discriminate Analysis(GDA), Fletcher-Powell Conjugate Gradient (CGF), Resilient Backpropagation (RP), Variable Learning Rate Gradient Descent (GDX), One Step Secant (OSS), Regression (R), Levenberg-Marquardt (LM), Scaled Conjugate Gradient(SCG). Parameters Root Mean Squared Error (RMSE) and the correlation coefficient (R-square) were used to evaluate the performance of each model and mathematical modeling for correlation co-efficient of all algorithms are also presented. LM and R are the best algorithms but the ML-FFBPANN-LM algorithm provided the best optimization results of maximum correlation co-efficient. LM algorithm produced a maximum correlation coefficient under training (0.99818), testing (0.99351), valediction (1.0), and an overall correlation coefficient be 0.9568. From the study, it can be concluded that the ML-FFBPANN with LM algorithm can be used in the aeration process in effluent treatment for analysis and prediction of process parameters.

Discussion on the use of fashion elements in ceramic design in the context of big data

Abstract In the context of the significant data era, this study explores the application of fashion elements in ceramic design. By integrating modern aesthetics and traditional craftsmanship, it realizes the innovation of ceramic design and promotes the development of ceramic industry. A neural network-based assessment model, especially BP artificial neural network, is used to quantitatively analyze and apply fashion elements in ceramic design. First, the basic theory of neural network, including MP model and BP algorithm, is introduced. The study established a network model with a 5-30-5 structure by training 800 sets of color samples. The experimental results show that the network reaches the mean square error target (MSE=1e-7) after 1516 iterations, proving the model’s effectiveness in dealing with nonlinear color semantic mapping. The analysis of the prediction accuracy of the color scheme shows that the maximum errors are 2.22, 1.93, and 1.36 in the L, A, and B components, respectively. These results are lower than the national standard color difference range, indicating that the proposed model has practical application in ceramic design. The neural network model can effectively integrate fashion elements into ceramic design, providing a new perspective and technical support for traditional ceramic design.

Research on visualization monitoring technology of vulnerable high-voltage electrical equipment in substation based on BP artificial neural network

Abstract This study develops a visualization monitoring system for substation equipment operational status, utilizing mobile monitoring technologies. The system architecture integrates a core functional module aligned with comprehensive system requirements, enhanced by a BP neural network to optimize the server’s data mining capabilities. The research focuses on the analysis of typical faults in crucial substation electrical equipment, applying a Fourier algorithm for preprocessing the fault data. Employing the diagnostic principles of the BP neural network, the study designs a thermal fault diagnosis process for the substation apparatus. Experimental scenarios were established to evaluate the BP neural network’s performance by comparing three linear regression sample values. The practical application of the BP neural network model was assessed through integration with substation field data. Cross-validation of the field data indicates that the fault location algorithm accurately identifies 11 types of faults from 85 alarm signals in the secondary condition monitoring of substations, achieving a reliability of 98% or higher, which underscores its high applicability and operational feasibility.

AN APPLICATION OF ARTIFICIAL NEURAL NETWORK TOWARD THE MATHEMATICAL MODELING OF MHD TANGENT HYPERBOLIC NANOFLUID ACROSS A VERTICAL STRETCHING SURFACE

The Levenberg-Marquardt (LM) back propagation (BP) artificial neural networks (ANNs) (LM-BP-ANNs) procedure is used in this analysis to show the computational strategy of neural networks for the simulation of magnetohydrodynamics tangent hyperbolic nanofluid flow comprised of motile microorganism across a vertical slender stretching surface. The fluid flow were examined under the significance of chemical reaction, magnetic field, activation energy, and heat source. The modeled equations were simplified to the ordinary system of differential equations using similarity variables substitution. The Lobatto IIIA formula based on the finite difference method was employed for the nano-liquid flow problem with an accuracy up to five decimal points. The robustness of Lobatto IIIA is its straightforward execution of very nonlinear coupled differential equations. Several operations involving testing, authentication, and training were carried out by developing a scheme for different fluid problem elements using reference datasets. The accuracy of LM-BP-ANNs was tested through mean-square error, error histogram, curve fitting figures, and regression plot. Moreover, the examination of flow model factors for concentration, mass, and momentum outlines are expressed through graphs. It was perceived that the velocity field declines with the flourishing influence of the magnetic field and lessens with the upshot of Weissenberg number and power law index.

Real-time automatic identification methods for downhole whirling based on mechanical specific energy model of drill bit

In the drilling exploitation of hot dry rock for geothermal energy, whirling is one of the main low-efficiency conditions that affect the efficiency of Polycrystalline Diamond Compact (PDC) bit in horizontal well drilling. Realizing automatic and real-time identification with whirling is of great significance to save non-productive time and ensure drilling safety and benefit. In this paper, we first constructed a real-time drilling mechanical specific energy (MSE) model combined with while-drilling testing to reflect the real-time drilling conditions. The MSE model is used to normalize multi-source parameters. Secondly, we constructed a Back Propagation (BP) artificial neural network (ANN), and then the normalization effect verification, optimization of network parameters, and identification effect verification of whirling were carried out. The final results show that the established MSE model has a favourable effect on data normalization, which could also reduce the complexity of the required network model, and shorten the training time by 20–30 s/step. The optimal algorithm is Trainscg, whose optimal number of hidden layer nodes is 5, and the optimal maximum number of iteration steps is 1000. The established ANN model can accurately identify whirling based on MSE, the accuracy is about 0.94, and the average relative error is 1.3%. The method established in this paper provides a reference for the automatic identification of various low-efficiency conditions based on MSE.

Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

Addressing the pressing need for sustainable bioenergy sources amid the growing climate crisis, this study investigates potential crop bioenergy output in the Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict the accumulated net primary productivity (NPP) of various crops, i.e., single rice, double rice, maize, and wheat, in Jianghan Plain. The models highlight a strong predictive performance, with the SE model achieving an R2 of 0.83 and wheat model an R2 of 0.90, implying these models can be utilized reliably for future NPP forecasting. Our evaluation of past and future NPP values reveals a negative correlation between the increase in NPP values and observed temperature trends. This emphasizes the significant impact of temperature on crop bioenergy potential. For future NPP predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields a favourable outcome for all crops, particularly SE. Additionally, the results suggest that aggressive decarbonization and global warming mitigation strategies are vital for enhancing NPP values. However, the study also highlights potential trade-offs and stresses the necessity for region-specific climate reconciliation strategies; for instance, midland counties benefit from temperature control, whereas the sideline favours CO2 mitigation. The study further investigates the spatial distribution of potential bioenergy, with the southwestern part of the plain holding more bioenergy potential, providing an essential reference for future energy planning. By 2030, in Jianghan Plain, when fully integrated with reasonable climate responses, there could be substantial bioenergy potential from crop residues reaching (1.2–1.9) × 109 MJ, which is 11% less than the current.