Deformation behavior of profiled ring rolling and process optimization for uniformity

Research Status and Developing Trends on the Ring Rolling Process of Profile Ring Parts

Design of Profile Ring Rolling by Backward Simulation Using Upper Bound Element Technique (UBET)

The paper presents a brief history of ring rolling development at the Dresden University of Technology (DUT) from the 70s to the present. The technological scope covers the techniques explored at the DUT: Tangential profile ring rolling, axial profile tube rolling/roll plunging and the TRENPRO® method to produce shaped rings. Starting from the state of the art, technological principle, range of application and machine‐oriented implementation of these techniques are described. Furthermore, other common ring rolling variants, such as axial profile ring rolling, skew‐rolling and radial‐axial ring rolling, are taken into consideration. Finally, the authors provide a summary of problems still to be solved and ways to refine and disseminate the “ring rolling” discipline, assessing the rolling methods’ potential as extremely forward‐looking.

Simulation of T-section profile ring rolling by the 3-D rigid-plastic finite element method

Optimizing profiled ring preforms for enhanced deformation uniformity using the isothermal field method

Cold profiled ring rolling (CPRR) is an advanced local incremental forming process with complexity and high nonlinearity. In this process, the feed rate of mandrel is a key factor affecting the expanding speed of ring outer radius and the filling behavior; thus, it is very significant to rationally design the feed rate for precise controlling the deformation process. Aimed at the CPRR process, the problem of feed rate planning under two variable feed patterns, which are constant expanding speed of ring outer radius (P CES) and constant feed amount per ring revolution (P CFA), has been solved successfully by using the hypothesis-testing approach in this paper. First, according to the relation between the ring outer radius R and the ring reduction ΔH, a novel mathematical solution for planning the feed rates under P CES and P CFA has been proposed, in which a series of points of (R, ΔH) under constant feed rate (P CFR) pattern have been taken into consideration. Second, two differential equations obtaining variable feed rate curves under P CES and P CFA have been built and their solving processes have also been given, and subsequently a new quantitative planning flow of variable feed rates has been presented. Third, an example of three feed rates planning under P CFR, P CES, and P CFA for a practical closed cold T-shaped ring rolling process in the design phase has been realized, and their validity has been proven by three-dimensional finite element numerical simulation. The achievements can provide a guideline for planning the feed rates of the relevant complex profiled ring rolling processes to improve efficiency and reduce cost.

Ring rolling (RR) is a widely used process for producing seamless rings, but its complex thermo-mechanical behavior often requires costly experiments or FEM simulations. This study presents a novel analytical method for predicting torque and energy in RR that explicitly accounts for the fishtail effect, a lateral deformation of the ring cross-section. The approach combines a slab-based mechanistic model with a regression linking fishtail deformation to the kinematic ratio between mandrel feed and ring rotation. Validation was performed via FEM simulations on an industrial AISI 1045 steel case, covering thirty conditions with varying feed rates and rotational speeds. Results show that conventional models ignoring fishtail can overestimate errors by over 60% for torque and 50% for energy, whereas the proposed method reduces errors below 15% in most cases. These findings highlight the importance of including fishtail effects, offering a fast, reliable, and efficient tool for early-stage RR process design and optimization.

Although cold ring rolling (CRR) process is largely used in the manufacturing of profiled rings like bearing races, research on this purpose has been scant. In this study, based on a validated finite element (FE) model, CRR process is simulated regarding the variable and constant feed speeds of the mandrel roll which lead to constant and variable values of the ring's diameter growth rates respectively using a 3D rigid-plastic finite element method (FEM). Major technological problems involved in the process including plastic deformation behavior, strain distribution and its uniformity, Cockcroft and Latham damage field and final outer diameter of ring are fully investigated. The results of simulations would provide a good basis for process control especially feed speed controlled mills and guiding the design and optimization of both cold and hot ring rolling process.

During the rolling process of profile ring, the design of the rolling cavity directly affects the forming quality of the final ring. In order to improve the cavity filling rate and reduce the production cycle time, this paper proposes a general evaluation method of the filling difficulty for profile ring rolling cavity by combining the response surface method(RSM) and the finite element method(FEM) with the step-type ring as the research object. Firstly, a universally applicable design method for profile ring rolling cavity is proposed, and based on the description of the rolling cavity, three design variables and a corresponding response objective are defined to achieve multi-objective optimization. Secondly, a finite element model of this step-type ring rolling process is established, and the response model is built and improved by significance analysis and analysis of variance(ANOVA) based on the forming simulation results. Then, the effects of different variables on the optimization objectives are comprehensively investigated by this response model. Finally, the evaluation method of the filling difficulty for profile ring rolling cavity is obtained by combining the contour plot of the response objective.

Three-Dimensional UBET Simulation Tool for Seamless Ring Rolling of Complex Profiles

Artificial neural network is implemented to predict the required load and torque in T-section profile ring rolling process for the first time in this study. Moreover, an optimal condition of T-section profile ring rolling process for specific limit of input factor is acquired using genetic algorithm technique. Various three-dimensional finite element simulations are carried out for different collections of process variables to obtain initial data for training and validation of the neural network. Besides, the finite element model is verified via comparison with the experimental results of the other investigators. The back-propagation algorithm is utilized to develop Levenberg–Marquardt feed-forward network and the optimum architecture is achieved by estimating the performance considering different number of hidden layers and neurons. It is concluded that results of artificial neural network predictions have an appropriate conformity with those ones from simulation and experiments. Moreover, a reasonable accuracy is obtained from the implemented model by which the prediction of ring rolling load and torque in different conditions can be achieved.

On a precision forming criterion for groove-section profiled ring rolling process

Pre-stacked uncured metal–fiber-reinforced polymer (FRP) laminates, which are critical for aerospace components like double-curved fuselage panels, wing ribs, and engine nacelles, exhibit better deformation behavior than their fully cured counterparts. However, accurate process simulation requires precise material characterization and process optimization to achieve a defect-free structural design. This study focuses on two core material behaviors of uncured laminates—inter-ply friction at metal–prepreg interfaces and out-of-plane bending—and optimizes process parameters for their non-coherent deformation. Experimental tests included double-lap sliding tests (to quantify inter-ply friction) and clamped-beam bending tests (to characterize out-of-plane bending); a double-curved dome part was designed to assess the effects of the material constituent, fiber orientation, inter-ply friction, and clamping force, with validation via finite element modeling (FEM) in Abaqus software. The results indicate that the static–kinetic friction model effectively predicts inter-ply friction behavior, with numerical friction coefficient–displacement trends closely matching experimental data. Additionally, the flexural bending model showed that greater plastic deformation in metal layers increased bending force while reducing post-unloading spring-back depth. Furthermore, for non-coherent deformation, higher clamping forces improve FRP prepreg deformation and mitigate buckling, but excessive plastic deformation raises metal cracking risk. This work helps establish a combined experimental–numerical framework for the defect prediction and process optimization of complex lightweight components, which address the core needs of modern aerospace manufacturing.

High-temperature deformation behavior and microstructural evolution of as-cast and hot rolled β21S alloy during hot deformation

Abstract. High-entropy alloy is a promising structural material for high-temperature service applications due to the designable superior properties. Superplasticity, normally achieved at relatively high temperature and low strain rate, enables difficult to formation materials to manufacture complex-shaped parts. This paper presents a feasibility experimental study on the deformation and microstructure of CoFeMnNi high-entropy alloy under hot deformation conditions with low strain rate. A series of hot uniaxial tests were conducted at the temperature range between 800-1000℃ with typical strain rates of 10-4 and 10-5/s to determine the stress-strain behaviours. Additionally, to understand the deformation mechanism, microstructure evolution after deformation was characterized. Finally, a mechanism based constitutive model of high-entropy alloy was developed, which facilities the process optimization by finite element simulations.