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Friction stir welding (FSW) has become one of the most used solid-state joining methods because of the increased mechanical properties and weld quality that can be obtained. The present investigation focuses on the effects of Titanium Carbide nanoparticles (TiCnp) reinforcement with the welds of AZ31 magnesium alloy using the grey relational coefficient optimization technique with the aid of artificial neural networks (ANNs) for modeling. The parameters considered are TiCnp content of approximately 1.5wt.%, tool inclination angle of 0^∘, 1^∘, and 2^∘, tool spindle speed of 1000, 1250, and 1500rpm, tool geometry square, cylinder, and triangle, feed rate of 25, 50, and 75mm/min and axial force of 5, 10, and 15kN. Other mechanical properties determined involve microhardness, Tensile Strength (TS), wear rate (WR), and impact strength (IS). The results show the improvement of mechanical properties with an increase in TiCnp concentration within the range which implies that the highest TS of 242MPa is obtainable when the amount of TiCnp is optimally added. Interestingly, while identifying the optimal parameters for mechanical properties, it was ascertained that 1250rpm of rotational speed (RS), 50mm/min of traverse speed (TS), 1^∘ of tilt angle (TA), and square tool profile shape were found to have the best results. Similar findings were backed up by the ANN models whereby the introduction of TiCnp into the AZ31Mg alloy boosts TS to about 130MPa, microhardness to 70MPa and IS to about 89.34MPa, and lowers WR to 0.0046m³/m. This integrated approach highlights the possibility of applying ANN coupled with grey relational analysis for the improvement of FSW process for improving the material characteristics.

A Battery Management System (BMS) can prolong the life of the battery but it depends on the accuracy of the adopted scheme. Different techniques have been developed to enhance the BMS by monitoring the State of Health (SOH) of the battery. In this paper, the detection of battery voltage is analyzed by using the cycle counting method, which is a conventional technique and compared with Artificial Neural Network (ANN), a heuristic method. The advantage of the proposed ANN method is that SOH can be monitored without disconnecting the battery from the load. Also, the sampling data to the ANN are derived from various techniques including Open Circuit Voltage (OCV) method, Ambient temperature measurement, and valley point detection. A feed-forward backpropagation algorithm is used to achieve the purpose of real-time monitoring of the LAB. The results show that the precise estimation of SOH can be obtained by Feed-Forward Neural Network (FFNN) when trained with more sampling data.

New advancements in deep learning issues, motivated by real-world use cases, frequently contribute to this growth. Still, it’s not easy to recognize the speaker’s emotions from what they want to say. The proposed technique combines a deep learning-based brain-inspired prediction-making artificial neural network (ANN) through social ski-driver (SSD) optimization techniques. When assessing speaker emotion recognition (SER), the recognition results are compared with the existing convolutional neural network (CNN) and long short-term memory (LSTM)-based emotion recognition methods. The proposed method for classification based on ANN decreases the computational costs. The SER algorithm allows for a more in-depth classification of different emotions because of its relationship to ANN and LSTM. The SER model is based on ANN and the recognition impact of the feature reduction. The SER in this proposed research work is based on the ANN emotion classification system. Speaker recognition accuracy values of 96.46%, recall values of 95.39%, precision values of 95.21%, and F-Score values of 96.10% are obtained in this proposed result, which is higher than the existing result. The average accuracy results by using the proposed ANN classification technique are 4.38% and 2.89%, better than the existing CNN and LSTM techniques, respectively. The average precision results by using the proposed ANN classification technique are 4.67% and 2.49%, better than the existing CNN and LSTM techniques, respectively. The average recall results by using the proposed ANN classification technique are 2.90% and 1.42%, better than the existing CNN and LSTM techniques, respectively. The average precision results using the proposed ANN classification technique are 3.80% and 3.10%, better than the existing CNN and LSTM techniques, respectively.

Agriculture not only plays a vital role in human survival but also contributes to the nation’s greater economic development. With the use of technologies like IoT, WSNs, remote sensing, camera surveillance, and many more, precision agriculture is the newest buzzword in the field of technology. Its primary goal is to lessen the labour of farmers while increasing the output of farms. Many machine learning techniques are designed to improve the productivity and quality of the crops, but the improper irrigation and disease prediction of the existing techniques leads to loss of productivity and quality. Hence, the IoT based wireless communication system is designed for smart irrigation and rice leaf prediction using ANN and ResNeXt-50 model. In this designed model, smart irrigation is controlled by collecting the temperature and moisture of the soil in the agricultural field by using the WSN. Likewise, a surveillance camera is placed in the agricultural field to capture the rice leaf to find the disease such as rice blast, leaf smut, brown spot and bacterial blight. These collected data are processed and classified for smart irrigation and rice leaf disease prediction. For evaluating the performance of both the ANN and ResNeXt-50 trained model, the performance metrics such as accuracy, sensitivity, specificity, precision, error etc. The performance metrics for the ANN and ResNeXt-50 model are 0.9427, 0.925, 0.903, 0.86, 0.0573 and 0.967, 0.935, 0.943, 0.939 and 0.033. Thus, the evaluation of the designed model results that the proposed approach is performing better compared to the current techniques.

Nitriding is usually used to improve the surface properties of steel materials. In this way, the wear resistance of steels is improved. We conducted a series of studies in order to investigate the microstructural, mechanical and tribological properties of salt bath nitrided AISI 4140 steel. The present study has two parts. For the first phase, the tribological behavior of the AISI 4140 steel which was nitrided in sulfinuz salt bath (SBN) was compared to the behavior of the same steel which was untreated. After surface characterization using metallography, microhardness and sliding wear tests were performed on a block-on-cylinder machine in which carbonized AISI 52100 steel discs were used as the counter face. For the examined AISI 4140 steel samples with and without surface treatment, the evolution of both the friction coefficient and of the wear behavior were determined under various loads, at different sliding velocities and a total sliding distance of 1000 m. The test results showed that wear resistance increased with the nitriding process, friction coefficient decreased due to the sulfur in salt bath and friction coefficient depended systematically on surface hardness. For the second part of this study, four artificial neural network (ANN) models were designed to predict the weight loss and friction coefficient of the nitrided and unnitrided AISI 4140 steel. Load, velocity and sliding distance were used as input. Back-propagation algorithm was chosen for training the ANN. Statistical measurements of R², MAE and RMSE were employed to evaluate the success of the systems. The results showed that all the systems produced successful results.

This research attempts to explore the utilization of Gas-Assisted Electrical Discharge Machining (GAEDM) of die steel. High pressure inert gas (argon) in conventional electric discharge machining with constraint state was utilized to assess the surface roughness (SR). Analysis of Variance (ANOVA) was used to find out the process parameters that notably affected the SR. In this study, a mathematical model has been investigated to know the SR by using Buckingham pie-theorem. The fit summary confirmed that the quadratic model is statistically appropriate and the lack of fit is insignificant. Root mean square error and absolute standard deviation, obtained through response surface method (RSM), were also used for developing the model and for predicting its abilities through ANN, ANFIS. The experiment and anticipated estimates of SR during the process, obtained by RSM, dimensional analysis, ANN and ANFIS, were found to be in accord with each other. However, the ANFIS technique proved to be more fitting to the response as compared to the ANN, dimensional analysis and the RSM.

In this paper, an effort is made to determine the optimized parameters in laser welding of Hastelloy C-276 using Artificial Neural Network (ANN) and Genetic Algorithm (GA). CO₂ Laser welding was performed on a sheet of thickness 1.6mm based on Taguchi L27 orthogonal array. Laser power, welding speed and shielding gas flow rate were chosen as input parameters and Bead width, depth of Penetration and Microhardness were measured for assessing the weld quality. ANN was applied for modeling the welding process parameters i.e. heat input, welding speed and gas flow rate. Various learning algorithms such as Batch Back Propagation (BBP), Incremental Back Propagation (IBP), Quick Propagation (QP) and Levenberg–Marquardt (LM) were comprehensively tested for estimating the output parameters and a comparison was also made among them, with respect to prediction accuracy. BBP method was found to be the best learning algorithm. Experimental validation test was performed based on the ANN and GA predicted optimized responses and this welding input parameters provided satisfactory weld metal characteristics in terms of penetration depth, bead width and microhardness.

Geopolymer concrete is established to have brilliant designing properties with a decreased carbon impression. Geopolymer with fibers has gained prevalence in recent years for use in concrete, for the most part, inferable from their low cost and outstanding individuality. In this examination, Fiber-Reinforced Geopolymer Concrete (FRGPC) is utilized as a binder with the impacts of the intrinsic sulfate solution in Gypseous Soil. GPC blend is included with alkaline activators, for example, NaOH and KOH with molarities [M. Z. N. Khan, Y. Hao, H. Hao and F. U. A. Shaikh, Cem. Concr. Compos.85 (2018) 133; P. Sukontasukkul, P. Pongsopha, P. Chindaprasirt and S. Songpiriyakij, Constr. Build. Mater.161 (2018) 37; Y. Alrefaei and J.-G. Dai, Constr. Build. Mater.184 (2018) 419] and hybrid fibers (steel and polypropylene) with the fiber content of 0–0.16%. Fibers are added to upgrade the strength to the concrete to meet given functionality necessities. The Gypseous Soil is taken with the percentage of G13,G25,G54, to assess the effects, compressive strength, ductility, collapsibility potential, and coefficient of penetrability tests were performed with a soaked and unsoaked solution. These outcomes are validated with the assistance of an Artificial Neural Network (ANN) optimization algorithm. The validation results played out that ideal precision and high strength of the activated solution. At long last, the simulated outcomes give better execution compared with existing papers.

Equal channel angular pressing (ECAP) processed materials have higher grain refinement and strength, and they exhibit more surface roughness when they are machined. This enhancement in the properties highly influences the surface roughness and material removal rate of the materials. The commercial pure aluminum has a wide variety of applications when it is enhanced with high strength properties. In this paper, the machinability of commercially pure aluminum processed through ECAP is investigated in turning operations. Different ECAP processes are carried out to study the microstructural characterization and mechanical properties of the material. The material removal rate and surface roughness are tested by performing the turning operation in the CNC lathe with chemical vapor deposited carbide tool such that the feed rate, spindle speed and depth of cut are considered as the machining variables. To create a hypothesis for the experimentation, the empirical models are developed for the objective functions using the statistical technique response surface methodology (RSM) such that the response models are the objective functions and the model variables are the machining parameters. The response models are verified for the adequacy through ANOVA and $p$-test, and also verified for the closeness with the experimental results. Artificial neural network (ANN)-based empirical equations are also developed for the objective functions using the RSM design matrix and the results of both the RSM and ANN are compared for the suitability.

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-II (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives.

Innovation in developing optimized process parameters is vital to meet industrial demand in composite applications. Especially in the fiber composite drilling process, unexpected dimensional errors cause failure due to the anisotropic nature of cellulosic fibers and heterogeneity in reinforcement. A sequential artificial neural network (SANN) technique has been required for composite drilling to predict a wide range of operating conditions and analyze the dimensional imperfections. The work fabricates three different sisal/ copper foil/ hemp (SCH) hybrid composites with varying perforated copper foil pitches (10/20/30mm) designs. The drilling experiments were designed using a central composite design. The effect of experimental/ SANN input parameters [spindle speed, drill point angle (DPA) (92°, 112°, and 132°), composite type, feed rate] and output responses (thrust force, torque, and roundness) were analyzed. The SANN model was trained through the experimental data and verified with a test set of experiments. Finally, the proposed SANN model was used to predict the drilling responses at a lower limit to the higher operating range for 70 validation experiments. The interaction responses were analyzed using the response surface methodology technique between the drilling (Input/ Output) process parameters. The experimental results inferred that the SCH-3 type with a higher pitch between the perforations shows minor roundness error. A drill bit with higher DPA (132°) induces more delamination effects in the laminates. The validation experiments revealed that the predicted ANN data correlated with the experimental data with less than 2% mean absolute error.

The machining of Ti–6Al–4V alloy faces several confronts like generation of higher cutting temperature, fast tool wear, poor surface finish, higher tool vibration and chattering. Therefore, this research presents the detailed analysis of the surface roughness, tool flank wear, and amplitude of vibration and chip morphology under MQL enabled Ti–6Al–4V CNC machining. The experimental scheme is chosen as Taguchi L${}_{18}$ orthogonal array (OA) with cutting speed, feed and cutting depth considered as the input processing parameters. Further, WPCA optimization is implemented to evaluate the best combinations of input factors to get the optimal values of outputs.

The implementation of the fused deposition modeling (FDM) technique in the production system is mainly due to its flexibility and ability to fabricate complex 3D prototypes and geometries. However, the mechanical strength of the printed parts needs to be investigated which was influenced by the process parameters such as layer thickness (LT), raster angle (RA), and Infill Density (ID). Therefore, these process parameters need to be optimized to attain better mechanical strength from the FDM printed parts. In this research, ePA-CF filament material was used to fabricate the specimens based on the selected process parameters such as LT (0.07, 0.14, and 0.20mm), RA (0^∘, 45^∘, and 90^∘) and ID (50%, 75%, and 100%). The artificial neural network (ANN) method was implemented to determine the influential printing process parameters. Tensile, flexural, and impact tests were considered as the response parameters based on the various combination of the input parameters. It was concluded that the printing of nylon carbon parts using $\mathrm{\text{LT}}=0.14$mm, $\mathrm{\text{ID}}=100\%$, $\mathrm{\text{RA}}=90^{\circ }$ retains improved tensile strength of 66 MPa, flexural strength of 87MPa and impact strength of 12.5KJ/m². Further, the propagation of cracks and the mode of failure were examined using SEM fractography. These observations substantiate that the selection of an optimal combination of FDM parameters assists in enhancing the mechanical strength of the printed nylon carbon parts.

Digital technologies sustain today’s world. Every part of the world is working towards digital technologies, which none of us can eliminate. Enormous growth is achieved only by unexpected acceleration by digital technologies, including the Internet of Everything (IoE), Artificial Neural Networks (ANN), Machine Learning (ML), Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning (DL), and many more. These technologies started occupying all the engineering sectors, including manufacturing. This paper focuses on tribology analysis related to manufacturing concerning various digital manufacturing technologies. The paper narration includes Tribology using digital technologies wherein the journals and patent landscape analysis abet them. In trend, Tribology utilizes all these technologies today and envisages its growth with the predominant technological invention in the border view. The survey of various literature reveals that only three digital technologies, including AI, ML, and ANN, are used by tribologists around the globe. Other Technologies like Evolutionary Algorithm (EA), Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Interference Systems (ANFIS) are not used predominantly.

The aim of this study is to provide insights into the performance of copper-based brake pads used in high-speed trains and contribute to a more predictable braking system by leveraging mathematical and artificial intelligence (AI) models. The wear behavior of Cu-based brake pads in high-speed trains was investigated using a pin-on-disc test setup under different speeds, temperatures, and loads with a constant sliding distance. Additionally, mathematical and AI models were developed to predict the friction coefficient and wear rate values obtained from the experiments. This innovative approach initiates a significant discussion in line with a current need, and the sharing and publication of the obtained results are currently essential to address the knowledge gap in this field. The results revealed that an increase in temperature led to an increase in both the friction coefficient and wear rate. Conversely, an increase in load resulted in a decrease in both the friction coefficient and wear rate. The transition from abrasive wear to adhesive wear occurred due to the softening of copper between friction surfaces, leading to material transfer. According to the results obtained from the models, both the artificial neural network (ANN) and multiple regression models demonstrated comparable accuracy, predicting the friction coefficient with approximately 94% accuracy in both cases, indicating reliable predictions. For the wear rate, the models achieved approximately 90% and 92% accuracy, respectively.

Although mammography is still the benchmark technique for breast cancer detection, many advantages of thermography make it a suitable adjunct tool for early detection. This paper describes the development of a computer-aided system for use together with thermography to assist in the detection and visualization/analysis of breast tumors. The system consists of a detection module for predicting the presence of tumors from thermograms, and a visualization module for generating the 3-D volumetric geometry of the suspected tumor inside the breast based on the 2-D thermogram. Detection is achieved through an artificial neural network taking the thermogram image as input, while the visualization is obtained by generating the 3-D model of the breast that produces a matching thermal image as the thermogram under a 3-D finite element analysis. A study with 200 subjects indicate that the detection sensitivity was good but the specificity was poor, but the reverse performance result was true for another back-propagation neural network which used physiological data instead of thermograms as input. This suggests that overall prediction capability can be improved by appropriate combination of the two results.

The treatment of early development of breast tumor has a higher success rate. This paper presents a framework for the early discovery of breast cancer. The objective is to assist the general practitioners and specialists in the detection of breast tumor. The proposed detection process consists of a preliminary screening process and a prediction process. The preliminary screening process using thermography aims to complement the detailed screening operation using mammography. The prediction process using artificial intelligence techniques aims to use past records of other similar cases to enhance the forecast of breast cancer development. The paper discusses the issues and techniques for the implementation of the proposed framework. These include the preliminary screening process, the retrieval of the relevant cases, and the prediction of the risk of developing breast cancer based on the thermographs, environmental/social data, physiological information, genetic factors, and medical records. This work constitutes initial effort to lessen the burden of medical professionals and increase the chances of successful treatment for patients in the fight against breast cancer.

Magnetic resonance imaging (MRI) plays an integral role among the advanced techniques for detecting a brain tumor. The early detection of brain tumor with proper automation algorithm results in assisting oncologists to make easy decisions for diagnostic purposes. This paper presents an automatic classification of MR brain images in normal and malignant conditions. The feature extraction is done with gray-level co-occurrence matrix, and we proposed a feature reduction technique based on statistical test which is preceded by principal component analysis (PCA). The main focus of the work is to establish the statistical significance of the features obtained after PCA, thereby selecting significant feature values for subsequent classification. For that, a $t$-test is performed which yielded a $p$-value of 0.05. Finally, a comparative study using $k$-nearest neighbor (kNN), support vector machine and artificial neural network (ANN)-based supervised classifiers is performed. In this work, we could achieve reasonably good sensitivity, specificity and accuracy for all the classifiers. The ANN classifier gives better performance with sensitivity of 97.33%, specificity of 97.42% and accuracy of 98.66% on the whole brain atlas database. The experimental results obtained are comparable to the other recent state-of-the-art.

Volatility of gold price is of great significance for avoiding the risk of gold investment. It is necessary to understand the effect of external events and intrinsic regularities to make accurate price predictions. This paper first compared EMD with CEEMD algorithm, and the results find that CEEMD algorithm performance is better than that of EMD in analysis gold price volatility. Then this paper uses the complementary ensemble empirical mode decomposition (CEEMD) to decompose the historical price of international gold into price components at different frequencies, and extracts a short-term fluctuation, a shock from significant events and a long-term price. In addition, this paper combines the Iterative cumulative sum of squares (ICSS) with Chow test to test the three event prices for structural breaks, and analyzes the effect of external events on volatility of gold price by comparing the external events with the test results for structural breaks. Finally, this paper constructs support vector machine (SVM) models and artificial neural network (ANN) on three series for prediction, and finds that the SVM performed better in gold price prediction in one-step-ahead and five-step-ahead, and when we combine the SVMs and ANNs with price components to make predictions, the error of the combined prediction is smaller than SVMs and ANNs with separate terms of series extracted.

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.