Investigations of The Hybrid Effects of The Operational Parameters In The Radial Forging Process Based On The Validation of The Hardness Test

In this study, considering all the parameters in radial forging and a three-dimensional model has been simulated using the finite element method. By implementing an elastoplastic state for the specimen tube, parameters such as friction type, residual stress distribution, effective strain distribution, material flow velocity and its effect on the neutral plate and the distribution of force in the die have been studied and analyzed. The effects of angle on the quality and characteristics of the specimen and the longevity of the die have also been obtained. Experimental results have been used to confirm the accuracy of the simulation. The results of the hardness test after forging were compared with the simulation results. Good agreement between the results indicates the accuracy of the simulation in terms of hardness. Therefore, this validation allows confirming the other obtained results for the analysis and prediction of various components in the forging process. After the validation and confirmation of the results through the hardness test, the hardness distribution was obtained by considering temperature changes and the effective strain on the specimen.

Selected aspects of a cold forging process for hollow balls

This paper presents the results of a study investigating a cold forging process for producing hollow balls with different wall thicknesses. The study was performed by FEM numerical modelling, which made it possible to obtain a wide spectrum of results. For the analysis of FEM results obtained for problematic cases (shape defects in forged balls), novel hypotheses for results interpretation are proposed. The FEM numerical model and hypotheses are then verified via experimental testing, and selected theoretical results are compared with experimental findings. Finally, obtained results are discussed (e.g. the effect of billet dimensions on forging conditions, wall thickness and hole size), a method for FEM results interpretation is presented, and design-related solutions ensuring the production of defect-free hollow balls are proposed.

The Influence of the Soaking Temperature Rotary Forging and Solution Heat Treatment on the Structural and Mechanical Behavior in Ni-Rich NiTi Alloy

The structural and thermophysical characteristics of an Ni-rich NiTi alloy rod produced on a laboratory scale was studied. The soak temperature of the solution heat-treatment steps above 850 °C taking advantage of the precipitate dissolution to provide a matrix homogenization, but it takes many hours (24 to 48) when used without thermomechanical steps. Therefore, the suitable reheating to apply between the forging process steps is very important, because the product’s structural characteristics are dependent on the thermomechanical processing history, and the time required to expose the material to high temperatures during the processing is reduced. The structural characteristics were investigated after solution heat treatment at 900 °C and 950 °C for 120 min, and these heat treatments were compared with as-forged sample structural characteristics (one hot deformation step after 800 °C for a 30 min reheat stage). The phase-transformation temperatures were analyzed through differential scanning calorimetry (DSC), and the structural characterization was performed through synchrotron radiation-based X-ray diffraction (SR-XRD) at room temperature. It was observed that the solution heat treatment at 950 °C/120 min presents a lower martensitic reversion finish temperature (Af); the matrix was fully austenitic; and it had a hardness of about 226 HV. Thus, this condition is the most suitable for the reheating stages between the hot forging-process steps to be applied to this alloy to produce materials that can display a superelasticity effect, for applications such as crack sensors or orthodontic archwires.

FPGA-based Neural Net for Failures Prediction in the Cold Forging Process

This paper presents and discusses the implementation of deep neural network for the purpose of failure prediction in the cold forging process. The implementation consists of an LSTM and a dense layer implemented on FPGA. The network was trained beforehand on Desktop Computer using Keras library for Python and the weights and the biases were embedded into the implementation. The implementation is executed using the DSP blocks, available via Vivado Design Suite, which are in compliance with the IEEE754 standard. The simulation of the network achieves 100% classification accuracy on the test data and high calculation speed.