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 ${}^{\circ }$ 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.

This paper discusses the wear and friction with the 2 W% Al2O3 nanocomposite content of pure Mg and AZ91D Mg alloys. Sliding speeds of 0.5 and 1.5 m/s in cast materials with normal stress conditions have been used in sliding distances up to 2000 m/s (0.5, 1.0, and 1 MPa). In order to evaluate the work hardness of the materials measured on temperature similar to the contact surface, we used hardness patterns and hot‐compression flow curves. Mg and AZ91D magnesium alloy pure monolithic Mg are low wear resistant due to an increase in contact temperature due to the adjustment of working conditions, but the wear rate was significantly lower in composite materials, mainly because of nanoparticle strength improvements. Although wear generally contributes to grain refining, increased wear capacity, and greater durability, wear resilience due to dislocation resistance and nanoparticles is seen as the primary wear mechanism in the existing nanocomposites.

The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ${}^{\circ }$ C with an interval of 100 ${}^{\circ }$ C and strain rates ranging from 0.05 to 1.0 s ${}^{-1}$ . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( $R^2$ ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.

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.

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.