FORMATION OF QUALITATIVE INDICATORS OF PRODUCTS BY ROLLING STAMPING PROCESSES

The influence of rolling stamping on the service characteristics of products is investigated in the work. Based on the analysis of deformation kinematics, stress-strain state, microstructure and evaluation of deformability of workpiece material, ways to increase geometric accuracy, vacuum tightness, electrolytic stability and mechanical characteristics of product material, as well as ways to improve the material of workpieces for their subsequent processing. The influence of active friction forces on the nature of the material flow during SHO was established, which contributed to the development of new processes that allow to bring the shape and dimensions of the workpiece as close as possible to the finished part. Thus accuracy of the sizes of details corresponds to 7-11th qualities of accuracy, and roughness of the processed surfaces makes Ra = 2,5… 0,63 microns. The process of reshaping the square billets into round ones by the method of SHO is effective, which increases the utilization factor of the metal and reduces the anisotropy of its mechanical properties. The characteristic of flat anisotropy λr, adopted in sheet metal stamping as a characteristic of the ability of the material to form scallops, decreases as a result of reshaping by 70-80%. The relative difference in yield strength in the plane of the sheet decreases from 0.10-0.15 to 0.03-0.05. The ultimate tensile strain increases by 8-10%, and the uniform uniform strain - by 5-8%. This improvement in the characteristics of the material reshaped by rolling blanks leads to the fact that when drawing cylindrical products, the value of scallops decreases by 2-2.5 times, and the value of the maximum degree of drawing increases by 10-15%. This reduces the relative difference in wall thickness along the perimeter of the elongated workpiece, and the change in wall thickness along its height becomes linear. Thus, the use of SHO processes significantly improves the quality characteristics of products.

МОДЕЛЮВАННЯ ПРОЦЕСІВ ВИРУБКИ-ПРОБИВКИ ПЕРЕДАВАЛЬНИМИ СЕРЕДОВИЩАМИ

During improvement of the quality of products and reducing its cost, sheet metal stamping production, being the basis for the aerospace industry, should more intensively introduce modern production technologies, especially design. There are a large number of factors influencing the stamping process (especially parts with complex geometry), more comprehensive consideration of which would allow to optimize such processes, thereby reducing the manufacturing cost and improving the quality. Currently the processes of forming and separation of complex parts by of an elastic pad are of interest from the point of view of optimization and the final determination of the nature of the behavior of the material. This includes the refinement of such parameters as the maximum permissible thinning, the strength of the die. Clarification of these and other parameters will significantly reduce energy required. Determination of these and other parameters of sheet metal stamping is possible due to application of the modern analysis methods. For numerical studies in the sheet stamping production, the variational method or FEM is the most suitable. Computer modeling makes it possible to investigate the behavior of the material, the kinematics of the workpiece movement during forming process, select the correct loading scheme for the workpiece, and also makes it possible to consider several options for the location of the workpiece in the die, which is very important for stamping thin sheet metal blanks. It provides a significant reduction of the time and costs for carrying out natural experiments, and decrease of technological preproduction preparation of sheet metal stamping. The development of a mathematical model based on the FEM makes it possible to determine not only the required parameters of the process, but also to consider the forming process during its certain stages, to determine the stress-strain state, indicating at the same time the problem zones of excessive thinning, loss of stability, the need to apply a die with a back pressure for cutting of thin sheet metal blanks. It allows to evaluate the quality of a ready product according to the calculated parameters, to use the results obtained for the design of elastic pad

Influence of geometric attributes on friction coefficient in sheet metal stamping

This paper presents a test device to explore the influence of geometric attributes of the contact surface on a friction coefficient along with sliding speed and contact pressure. Friction tests were conducted on a third-generation high-strength steel QP980. The friction coefficients for different surface curvatures, contact pressures, and sliding speeds were calculated, and the influences of these factors were analyzed. The formula for calculating the friction coefficients between curved contact surfaces was derived. The relationship between bending-induced surface roughness increase and friction coefficient was established. An enhanced friction coefficient model with pressure, velocity, and curvature dependence was proposed. The enhanced friction model was applied to simulate the stamping of an automotive part, and a better correlation was achieved.

Regression Approach to a Novel Lateral Flatness Leveling System for Smart Manufacturing

Sheet metal coils are widely used in the steel, automotive, and electronics industries. Many of these coils are processed through metal stamping or laser cutting to form different types of shapes. Sheet metal coil leveling is an essential procedure before any metal forming process. In practice, this leveling procedure is now executed by operators and primarily relies on their experience, resulting in many trials and errors before settling on the correct machine parameters. In smart manufacturing, it is required to digitize the machine’s parameters to achieve such a leveling process. Although smart manufacturing has been adopted in the manufacturing industry in recent years, it has not been implemented in steel leveling. In this paper, a novel leveling method for flatness leveling is proposed and validated with data collected by flatness sensors for measuring each roll adjustment position, which is later processed through the multi-regression method. The regression results and experienced machine operator results are compared. From this research, not only can the experience of the machine operators be digitized, but the results also indicate the feasibility of the proposed method to offer more efficient and accurate machine settings for metal leveling operations.

Influence Of Geometric Attribute On Friction Coefficient In Sheet Metal Stamping

This paper presents a test device to explore the influence of geometric attribute of the contact surface on friction coefficient along with sliding speed and contact pressure. Friction tests were conducted on a third-generation high strength steel QP980. The friction coefficients for different surface curvatures, contact pressures and sliding speeds were calculated and the influences of these factors were analyzed. The formula for calculating the friction coefficients between curved contact surfaces was derived. The relationship between bending induced surface roughness increase and friction coefficient was established. An enhanced friction coefficient model with pressure, velocity and curvature dependence was proposed. The enhanced friction model was applied to simulate the stamping of an automotive part and a better correlation was achieved.

Application of machine learning for acoustic emissions waveform to classify galling wear on sheet metal stamping tools

Galling wear in sheet metal stamping processes can degrade the product quality and adversely affect mass production. Studies have shown that acoustic emission sensors can be used to measure galling. In the literature, attempts have been made to correlate the acoustic emission features and galling wear in the sheet metal stamping process. However, there is very little attempt made to implement machine learning techniques to detect acoustic emission features that can classify non-galling and galling wear as well as provide additional wear-state information in the form of strong visualisations. In the first part of the paper time domain and frequency domain analysis are used to determine the acoustic emission features that can be used for unsupervised classification. Due to galling wear progression on the stamping tools, the behaviour of acoustic emission waveform changes from stationary to a non-stationary state. The initial change in acoustic emission waveform behaviour due to galling wear initiation is very difficult to observe due to the ratio of change against the large data size of the waveform. Therefore, a time-frequency technique “Hilbert Huang Transform” is applied to the acoustic emission waveform as that is sensitive to change of wear state, and is used for the classification of ‘non galling’ and the ‘transition of galling’. Also, the unsupervised learning algorithm fuzzy clustering is used as comparison against the supervised learning techniques. Despite not knowing a priori the wear state labels, fuzzy clustering is able to define three relatively accurate distinct classes: “unworn”, “transition to galling”, and “severe galling”. In the second part of the paper, the acoustic emission features are used as an input to the supervised machine learning algorithms to classify acoustic emission features related to non-galling and galling wear. An accuracy of 96% was observed for the prediction of non-galling and galling wear using Classification, Regression Tree (CART) and Neural Network techniques. In the last part, a reduced Short Time Fourier Transform of top 10 absolute maximum component acoustic emission feature sets that correlates to wear measurement data “profile depth” is used to train and test supervised Neural Network and CART algorithms. The algorithms predicted the profile depth of 530 unseen parts (530 unseen cases), which did not have any associated labelled depth data. This shows the power of using machine learning techniques that can use a small data training set to provide additional predicted wear-state on a much larger data set. Furthermore, the machine learning techniques presented in this paper can be used further to develop a real-time measurement system to detect the transition of galling wear from measured acoustic emission features.

Numerical design of load response in magneto-rheological actuators for sheet metal stamping

Auxiliary systems for sheet forming processes are widely used to improve products accuracy and increase tools life. As example, in blanking hydraulic dampers are widely used to reduce shocks and vibrations; nitrogen springs are often integrated in deep drawing tools to correct the ram tilt or to locally increase the blank-holder force, obtaining geometrical features on the stamped blank with one press pass. In this paper, a Magneto-Rheological (MR) semi-active actuator is developed for sheet forming operations and the interaction between MR fluid and electromagnetic field is investigated by Finite Element (FE) analysis. To overcome the limitations of gas springs and hydraulic actuator, the static electromagnetic circuits is reconfigured with respect of conventional MR actuators known in the state-of-the-art. The novel MR actuator has an inner bore where the electric windings are placed, while the narrow gap, in which the active MR fluid flows, is obtained between the inner bore and the cylinder internal surface. The resulting magnetic fields H and induction fields B, as well as the selection of components materials, are studied through the magneto-static FE model. The results from FE simulations show a longer activation length along the gap resulting in higher controllable forces values, without increasing the overall dimensions of the proposed prototype.