Search Results (4)

Cold forging is widely used in many industries. Multi-stage cold forming is usually utilized in forging fasteners. In this study, numerical simulation and experimental investigations were carried out on a five-stage cold-forming process for the manufacturing of low-carbon steel AISI 1010 relief valve regulating nuts. The forming process through five stages included preparation and centering for backward extrusion, backward extrusion over die pin, upset, backward extrusion over a moving punch, and piercing. The formability of the workpiece was studied, such as the effects on forming force response, maximum forming force, effective stress and effective strain distributions, metal flow patterns, and strength. A comparison of the forming forces obtained in the forming experiment with the numerical simulation results of the five-stage cold forming showed a good agreement with the trend of the forming force growth. For the maximum forming force and forming energy, the fourth stage of backward extrusion over the moving punch at the upper face was the largest among the five stages. The total maximum forming forces from the first to the fifth stages were numerically 440.9 kN and experimentally 449.4 kN, meaning the FE simulation and experimental results were in good agreement. The numerically simulated effective strain distributions were consistent with the experimentally tested hardness distributions. Highly compacted grain flow lines also resulted in higher hardness. The overall hardness of the workpiece formed by five-stage cold forming increased by 31% compared to the initial billet. The hardness of the workpiece increased with the forming stages, and the strain-hardening effect was obvious. The strength of the workpiece was significantly increased by five-stage cold forming. Full article

The cold forging backward extrusion is employed to produce parts that are characterized by better mechanical strength. However, in this process, punches are often prone to breakages because of the large forces encountered in deforming the steel billets. The service life of the punches is affected majorly by the geometrical attributes, the type of steel undergoing deformation, and hence the present investigation focuses on the applications of natural computing algorithms such as artificial neural network (ANN) and differential evolution (DE) optimization algorithm to study the differential influence on the forming behavior of different grades steel and enhance the punch service life. The AISI steel grades, such as AISI 1010, 1018, and 1045, employed extensively in the production of automotive components, have been compared in terms of forming behavior, such as effective stress, strain, strain rate, and punch force. The multi-layer feed-forward ANN architecture was utilized for process modeling with forming responses of finite element (FE) simulations that are strategically planned through the design of experiments (DoE) approach. Considerable variations were found for the effective stress and punch force amongst the steels, while marginal deviations were observed for effective strain and strain rates. Confirmatory experiments were conducted to validate the results of optimal combinations obtained through the DE optimization technique, and the deviations were observed to be in the acceptable range. The cold forging backward extruded components have also been examined for better mechanical soundness through microstructure and micro-hardness analysis that clearly revealed the mechanical integrity and strength enhancement within the forged components. The proposed study would assist the industries engaged in the production of cold-forged steel components in determining the appropriate values of variables to minimize the forming responses and, thus, help in enhancing the life of the tooling. Full article

Cold forged parts are mainly employed in automotive and aerospace assemblies, and strength plays an essential role in such applications. Backward extrusion is one such process in cold forging for the production of axisymmetrical cup-like parts, which is affected by a number of variables that influence the quality of the products. The study on the influencing parameters becomes necessary as the complexity of the part increases. The present paper focuses on the use of a multi-layered feed forward artificial neural network (ANN) model for determining the effects of process parameters such as billet size, reduction ratio, punch angle, and land height on forming behavior, namely, effective stress, strain, strain rate, and punch force in a cold forging backward extrusion process for AISI 1010 steel. Full factorial design (FFD) has been employed to plan the finite element (FE) simulations and accordingly, the input variables and response patterns are obtained for training from these FE simulations. This ANN model-based analysis reveals that the forming behavior of the cold forging backward extrusion process tends to increase with the billet size as well as the reduction ratios. However, decreases in punch angle and land height lead to the reduction of punch forces, which in turn enhances the punch life. FE simulation along with the developed ANN model scheme would benefit the cold forging industry in minimizing the process development effort in terms of cost and time. Full article

A two-stage cold forging process was proposed to manufacture a drive shaft with an internal spline and spur gear geometries, and this process was mainly composed of a forward extrusion for preform and a forward-backward extrusion for the drive shaft. In the process design, the preform was designed using a volume apportioning scheme from the required target shape, thereafter, the initial round billet was outlined. AISI 1035 carbon steel was selected as the raw material, and a spheroidizing heat treatment was adopted. Using the raw and spheroidizing annealed workpieces, uni-axial tensile and compression tests were carried out to evaluate the effect of the heat treatment and to measure the mechanical properties. Finite element simulations were sequentially performed to assure the suitability of the proposed process design. Considering the results from the process design and the numerical simulations, the related tool components were prepared and applied to a series of experimental investigations. The preform and the drive shaft fabricated by the two-stage cold forging experiments were compared with the required target and the numerically predicted configurations. The results indicated that the two-stage cold forging process proposed in this study could be well applied to the production of the drive shaft with an internal spline and spur gear structures. Full article