Hybrid Artificial Neural Network-Based Models to Investigate Deformation Behavior of AZ31B Magnesium Alloy at Warm Tensile Deformation.

The uniaxial warm tensile experiments were carried out in deformation temperatures (50-250 °C) and strain rates (0.005 to 0.0167 s-1) to investigate the material workability and to predict flow stress of AZ31B magnesium alloy. The back-propagation artificial neural network (BP-ANN) model, a hybrid models with a genetic algorithm (GABP-ANN), and a constrained nonlinear function (CFBP-ANN) were investigated. In order to train the exploited machine learning models, the process parameters such as strain, strain rate, and temperature were accounted as inputs and flow stress was considered as output; moreover, the experimental flow stress values were also normalized to constructively run the neural networks and to achieve better generalization and stabilization in the trained network. Additionally, the proposed model's closeness and validness were quantified by coefficient of determination (R2), relative mean square error (RMSE), and average absolute relative error (AARE) metrics. The computed statistical outcomes disclose that the flow stress predicted by both GABP-ANN and CFBP-ANN models exhibited better closeness with the experimental data. Moreover, compared with the GABP-ANN model outcomes, the CFBP-ANN model has a relatively higher predictability. Thus, the outcomes confirm that the proposed CFBP-ANN model can result in the accurate description of AZ31 magnesium alloy deformation behavior, showing potential for the purpose of practicing finite element analysis.

Warm Deformation Behavior and Flow Stress Modeling of AZ31B Magnesium Alloy under Tensile Deformation.

Constitutive equations were recognized for AZ31B magnesium alloy at higher temperatures and strain rates from conventional empirical models like the original Johnson-Cook (JC), modified JC, and modified Zerilli-Armstrong (ZA) models for capturing the material warm deformation behavior. Uniaxial warm tensile tests were performed at temperatures (50 to 250 °C) and strain rates (0.005 to 0.0167 s-1) to probe AZ31 magnesium alloy flow stress values. Depending on the calculated flow stress, constitutive equations were recognized, and these established models were assessed by the coefficient of determination (R2), relative mean square error (RMSE), and average absolute relative error (AARE) metrics. The results demonstrated that the flow stress calculated by the modified JC and ZA models revealed good agreement against the test data. Thus, the outcomes confirmed that the recognized modified JC and modified ZA models could effectively forecast AZ31 magnesium alloy flow behavior by capturing the material deformation behavior accurately.

Modelling of urea hydrolysis kinetics using genetic algorithm coupled artificial neural networks in urease immobilized magnetite nanoparticles.

The presence of urea in runoff from fertilized soil could be contributing to the growth of dangerous blooms. Enzymatic urea hydrolysis is a well-known outstanding process that, when integrated with nanotechnology, would be much more efficient. This research provides a novel perspective on magnetic nanobiocatalysts that reduce diffusion barriers in effective urea hydrolysis. Surprisingly, the model developed with the use of a Genetic Algorithm (GA) and an Artificial Neural Network (ANN) demonstrated that the system's diffusion restrictions were reduced. In order to forecast accurate outputs using artificial intelligence (AI), a neural network with one hidden layer and 20 neurons was built utilizing multilayer feed-forward network and showed highest output (diffusion co-efficient) with least mean square error (MSE). The diffusion coefficients of free urease, urease immobilized onto porous MNs (U-aMNs), and nanobiocatalyst, i.e. urease immobilized onto surface modified MNs (U-MNß), were 1.9 × 10-17, 12.62 × 10-16, and 15.48 × 10-16 cm2/min, respectively. These results revealed that the addition of Chitosan to the surface of MNs had a considerable impact on enzyme dispersion. The decrease in Damkohler number (Da) from 2.37 ± 0.26 for U-aMNs to 2.19 ± 0.11 for U-MNß suggested a beneficial effect in overcoming diffusion constraints. Pseudo-first order and pseudo-second order models were used to analyze urea uptake kinetics, with the former model offering the best fit to the system, with R2 values that were much closer to unity.

Optimization of Forming Parameters in Incremental Sheet Forming of AA3003-H18 Sheets Using Taguchi Method.

The surface finish is an important characteristic in the incremental sheet forming (ISF) process and is often influenced by numerous factors within the forming process. Therefore, this research was aimed at identifying the optimal forming parameters through the Taguchi method to produce high-quality formed products. The forming tool radius, spindle speed, vertical step increment, and feed rate were chosen as forming parameters in the experimental design, with surface roughness as the response variable. Taguchi L16 orthogonal array design and analysis of variance (ANOVA) test were used to identify the parameter's optimal settings and examine the statistically significant parameters on the response, respectively. Results confirmed that a significant reduction in surface roughness occurred with a drop in vertical step size and an increase in feed rate. In detail, the vertical step size has the most significant influence on the surface roughness, followed by the feed rate and the forming tool radius. In conclusion, the optimum level settings were obtained: forming tool radius at level 3, spindle speed at level 1, vertical step size at level 1, and feed rate at level 4. Additionally, confirmation experiment results based on the optimal settings indicated a good agreement against the experimental observation. Further, the response surface methodology (RSM) was also exploited to devise a mathematical model for predicting the surface roughness. The results comparison confirmed that both techniques could effectively improvise the surface finish.

Formability and Failure Evaluation of AA3003-H18 Sheets in Single-Point Incremental Forming Process through the Design of Experiments.

The single-point incremental forming process (SPIF) is one of the emerging manufacturing methods because of its flexibility in producing the desired complex shapes with higher formability at low-cost compared to traditional sheet forming methods. In this research work, we experimentally investigate the forming process to determine the influence of process parameters and their contribution to enhancing the formability without causing a fracture by combining the design of experiments (DOE), grey relational analysis (GRA), and statistical analysis of variance (ANOVA). The surface morphology and the energy dispersive X-ray spectroscopy (EDS) method are used to perform elemental analysis and examine the formed parts during three forming stages. The DOE procedure, a central composite design with a face-centered option, is devised for AA3003-H18 Al alloy sheet for modeling the real-time experiments. The response surface methodology (RSM) approach is adopted to optimize the forming parameters and recognize the optimal test conditions. The statistically developed model is found to have agree with the test measurements. The prediction model's capability in R2 is computed as 0.8931, indicating that the fitted regression model adequately aligns with the estimated grey relational grade (GRG) data. Other statistical parameters, such as root mean square error (RMSE) and average absolute relative error (AARE), are estimated as 0.0196 and 2.78%, respectively, proving the proposed regression model's overall closeness to the measured data. In addition, the prediction error range is identified as -0.05 to 0.05, which is significantly lower and the residual data are distributed normally in the design space with variance and mean of 3.3748 and -0.1232, respectively. ANOVA is performed to understand the adequacy of the proposed model and the influence of the input factors on the response variable. The model parameters, including step size, feed rate, interaction effect of tool radius and step size, favorably influence the response variable. The model terms X2 (0.020 and 11.30), X3 (0.018 and 12.16), and X1X2 (0.026 and 9.72) are significant in terms of p-value and F-value, respectively. The microstructural inspection shows that the thinning behavior tends to be higher as forming depth advances to its maximum; the deformation is uniform and homogeneous under the predefined test conditions.

Experimental and Numerical Investigation of AA5052-H32 Al Alloy with U-Profile in Cold Roll Forming.

The cold roll forming process is broadly used to produce a specific shape of cold-roll formed products for their applications in automobiles, aerospace, shipbuilding, and construction sectors. Moreover, a proper selection of strip thickness and forming speed to avoid fracture is most important for manufacturing a quality product. This research aims to investigate the presence of longitudinal bow, the reason behind flange height deviation, spring-back, and identification of thinning location in the cold roll-forming of symmetrical short U-profile sheets. A room temperature tensile test is performed for the commercially available AA5052-H32 Al alloy sheets using Digital Image Correlation (DIC) technique, which allows complete displacement and strain data information at each time-step. The material properties are estimated from the digital images using correlation software for tested samples; the plastic strain ratios are also calculated from samples at 0°, 45°, and 90° to the rolling direction. The tested sample's surface morphology and the elemental analysis are conducted using scanning electron microscopy (SEM) method and energy-dispersive X-ray spectroscopy (EDS) analytical technique combined with element mapping analysis, respectively. The cold roll forming experiments are systematically carried out, and then finite element analysis is utilized to correlate the experiment with the model. The performed cold roll forming numerical model outcome indicates a good agreement with the experimental measurements. Overall, the presented longitudinal strain was observed to influence the geometry profile. The spring-back is also noticed at the profile tail end and is more pronounced at high forming speed with lower strip thickness. Conversely, while the forming speed is varied, the strain and stress variations are observed to be insignificant, and the similar results also are recognized for the thinning behavior.

Correction: Murugesan, M., et al. Johnson Cook Material and Failure Model Parameters Estimation of AISI-1045 Medium Carbon Steel for Metal Forming Applications. Materials 2019, 12, 609.

The author wishes to make the following corrections to this paper [...].

Two flow stress models for describing hot deformation behavior of AISI-1045 medium carbon steel at elevated temperatures.

In materials processing, practical understanding of materials behavior at elevated temperatures and high strain rates is necessary for modeling the real system behavior. The tensile deformation behavior of AISI-1045 steel material is investigated at deformation temperatures (923-1223 K) and strain rates (0.05-1.0 s-1). This paper proposes a detailed research to characterize the material flow behavior based on modified Johnson-Cook (JC) and Zerilli-Armstrong (ZA) models, respectively, as well as the predictability of these two models are discussed. The experimental flow stress-strain data are employed to fit the constitutive equations to estimate the elected model material parameters. To demonstrate the validity and the accuracy of the proposed models, the model adequacies such as coefficient of determination and average absolute relative error are discussed. From the observation made, the authors found that the modified ZA model is more appropriate for predicting the material behavior as the predicted flow stress data and the experimental data displayed better correlation among them. To make this point more concrete, random experiments are conducted to validate the proposed constitutive models and the obtained results also show that the developed modified ZA model exhibits a better relationship with the experimental data. Overall, the proposed research work can be used as an efficient tool in the initial design of numerical model to accurately replicate the experiment in order to save time and cost.

Johnson Cook Material and Failure Model Parameters Estimation of AISI-1045 Medium Carbon Steel for Metal Forming Applications.

Consistent and reasonable characterization of the material behavior under the coupled effects of strain, strain rate and temperature on the material flow stress is remarkably crucial in order to design as well as optimize the process parameters in the metal forming industrial practice. The objective of this work was to formulate an appropriate flow stress model to characterize the flow behavior of AISI-1045 medium carbon steel over a practical range of deformation temperatures (650⁻950 ∘ C) and strain rates (0.05⁻1.0 s - 1 ). Subsequently, the Johnson-Cook flow stress model was adopted for modeling and predicting the material flow behavior at elevated temperatures. Furthermore, surrogate models were developed based on the constitutive relations, and the model constants were estimated using the experimental results. As a result, the constitutive flow stress model was formed and the constructed model was examined systematically against experimental data by both numerical and graphical validations. In addition, to predict the material damage behavior, the failure model proposed by Johnson and Cook was used, and to determine the model parameters, seven different specimens, including flat, smooth round bars and pre-notched specimens, were tested at room temperature under quasi strain rate conditions. From the results, it can be seen that the developed model over predicts the material behavior at a low temperature for all strain rates. However, overall, the developed model can produce a fairly accurate and precise estimation of flow behavior with good correlation to the experimental data under high temperature conditions. Furthermore, the damage model parameters estimated in this research can be used to model the metal forming simulations, and valuable prediction results for the work material can be achieved.