Radial-axial Ring Rolling Research Articles

Rolling Eccentric Steel Rings on an Industrial Radial–Axial Ring Rolling Mill

Various industries, including mechanical engineering, utilize steel rings featuring variable cross-sectional profiles, such as eccentric rings. Presently employed methods for producing eccentric rings possess drawbacks like restricted geometries, significant material wastage or uneven microstructures. The radial–axial ring rolling process serves to create seamless rolled steel rings with near-net-shaped cross-sections. A novel technique involves achieving eccentricity by dynamically adjusting the mandrel’s position during the ring rolling process. This method’s fundamental feasibility has previously been showcased using a blend of oil clay and a labor test bench. Transferring the possibility of manufacturing eccentric rings on industrial radial–axial ring rolling mills would expand the product range of ring manufacturers without encountering drawbacks associated with existing manufacturing processes. The objective of this paper is to demonstrate the basic feasibility of the concept of an industrial radial–axial ring rolling mill. In the first step, FEA simulation studies were carried out to develop the rolling strategy and estimate the achievable eccentricity on the institute’s radial–axial ring mill. Subsequently, the rolling strategy was implemented on an industrial ring rolling mill with the help of a unique technology module programmed in C++. Finally, an eccentric ring was ring rolled and compared with the FEA simulation, and the reproducibility was demonstrated to be successful.

Development of a measurement method for tracking the position of hot steel rings on an industrial radial-axial ring rolling mill

Development of a measurement method for tracking the position of hot steel rings on an industrial radial-axial ring rolling mill

Effects of the Radial-Axial Ring Rolling Process on the Mechanical Properties and Microstructure of Centrifugally Cast AM60 Magnesium Rings

Effects of the Radial-Axial Ring Rolling Process on the Mechanical Properties and Microstructure of Centrifugally Cast AM60 Magnesium Rings

Extreme gradient boosting-inspired process optimization algorithm for manufacturing engineering applications

Design and process optimization are key aspects of manufacturing engineering. This contribution details a machine learning (ML) methodology capable of learning from simulation results, experimental data, or sensor signals, and able to predict and optimize specific user-defined process and design parameters. The prediction-optimization algorithm is based on an enhanced Extreme Gradient Boosting (XGB) algorithm, highly responsive and accurate even for large datasets and complex variable interactions. Once the XGB function is trained, the optimization is carried out by a metaheuristic search algorithm defined according to the Differential Evolution (DE) architecture, allowing for the definition of the combination of independent variables that grant the minimization, or maximization, of the user’s defined objective function. The prediction and optimization capabilities have been assessed by applying the XGB-DE algorithm to a previously published authors' dataset and experimental results relevant for the radial-axial ring rolling (RARR) process, showing a prediction accuracy and optimization capability equal to 2.33% and 27.4%, respectively, with respect to authors’ previous finite element and experimental results. The XGB-DE methodology showed a remarkable capability in catching the trend and global minimum of a multi-variable and complex objective function, such as the one involved in complex thermo-mechanical forming processes.

Time series modelling of a radial-axial ring rolling system

Time series modelling of a radial-axial ring rolling system

Time series modelling of a radial-axial ring rolling system

Time series modelling of a radial-axial ring rolling system

Towards Extremely Large-scale Radial-axial Ring Rolling for Constant Ring Growth State with Accurate Closed-loop Control Method

Towards Extremely Large-scale Radial-axial Ring Rolling for Constant Ring Growth State with Accurate Closed-loop Control Method

Numerical Simulation of Intelligent Fuzzy Closed-Loop Control Method for Radial–Axial Ring Rolling Process of Super-Large Rings

During the radial–axial ring rolling (RARR) process of super-large rings, abnormal deformation states such as instability and out of circularity often lead to rolling termination and quality fluctuation of ring products. In this work, an intelligent fuzzy closed-loop control method for RARR process of super-large rings is proposed, i.e., the ring’s offset adaptive fuzzy control (ROAFC) based on the regulation of the axial roll’s rotational speed and the ring’s circularity fuzzy control (RCFC) based on the regulation of the mandrel’s feed speed. In addition, a recursive average filtering algorithm is added to smooth the axial roll’s rotational speed and the mandrel’s feed speed according to the actual situation. Using the ABAQUS/Explicit software and its subroutine VUAMP, the intelligent fuzzy controller of the ring’s offset and circularity in the RARR process is designed, and the finite element (FE) model for RARR process of a Φ10 m super-large ring with an integrated intelligent fuzzy control algorithm is established. The variation laws of the ring’s offset and circularity error in the RARR process are studied with regard to different control methods such as conventional planning control (CPC), ROAFC, RCFC, and comprehensive control of ROAFC combined with RCFC (ROAFC + RCFC). The results obtained show that, compared with the CPC, the ring’s offset is reduced by 84.6% and the circularity error is decreased by 51.9% in the RARR process utilizing comprehensive control of ROAFC + RCFC. The research results provide methodological guidance for realizing the intelligent forming of super-large rings.

On a plastic instability criterion for ultra-large radial-axial ring rolling process with four guide rolls

A dynamic mechanical model is proposed to describe the complexing actions of all the rolls on the ring during the ultra-large radial-axial ring rolling (RARR) process with four guide rolls. Based on the model, the calculation models for bending moment and normal stress at any section of the ring are deduced by force method. If the maximum section bending normal stress exceeds the yield stress of the ring materials, the ring will be distorted thus leading to the instability of the RARR process. According to this, a plastic instability criterion for the ultra-large RARR process with four guide rolls is developed, based on which a mathematical model to calculate the critical guide force for avoiding plastic instability of ring is obtained. The influence rule of the position of guide roll on the dangerous ring section of plastic instability is revealed, from which it is found the dangerous ring section mainly appears at the radial and axial deformation regions and the contact positions of the guide rolls and ring. The optimized layout of guide roll around the ring in favor of stability is determined to be about α1 = 61° and α2 = 119°. The plastic instability criterion is proven to be reliable from the aspects of the critical guide force, the section bending moment and normal stress and the dangerous ring section of plastic instability. Intelligent simulation case studies for the RARR process of ultra-large aluminum alloy ring indicate that the stable forming of the process can be effectively realized by regulating the guide force based on the plastic instability criterion. This work could provide a valuable guidance for the control of guide rolls and the optimization of the ultra-large RARR process with four guide rolls.

Improving quality prediction in radial-axial ring rolling using a semi-supervised approach and generative adversarial networks for synthetic data generation

As artificial intelligence and especially machine learning gained a lot of attention during the last few years, methods and models have been improving and are becoming easily applicable. This possibility was used to develop a quality prediction system using supervised machine learning methods in form of time series classification models to predict ovality in radial-axial ring rolling. Different preprocessing steps and model implementations have been used to improve quality prediction. A semi-supervised approach is used to improve the prediction and analyze, to what extend it can improve current research in machine learning for quality prediciton. Moreover, first research steps are taken towards a synthetic data generation within the radial-axial ring rolling domain using generative adversarial networks.

Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

This research provides an insight on the performances of machine learning (ML)-based algorithms for the estimation of the energy consumption in metal forming processes and is applied to the radial-axial ring rolling process. To define the mutual influence between ring geometry, process settings, and ring rolling mill geometries with the resulting energy consumption, measured in terms of the force integral over the processing time (FIOT), FEM simulations have been implemented in the commercial SW Simufact Forming 15. A total of 380 finite element simulations with rings ranging from 650 mm < DF < 2000 mm have been implemented and constitute the bulk of the training and validation datasets. Both finite element simulation settings (input), as well as the FI (output), have been utilized for the training of eight machine learning models, implemented with Python scripts. The results allow defining that the Gradient Boosting (GB) method is the most reliable for the FIOT prediction in forming processes, being its maximum and average errors equal to 9.03% and 3.18%, respectively. The trained ML models have been also applied to own and literature experimental cases, showing a maximum and average error equal to 8.00% and 5.70%, respectively, thus proving once again its reliability.

Multivariable regression and gradient boosting algorithms for energy prediction in the radial-axial ring rolling (rarr) process

Energy prediction and starvation have become an essential part of process planning for the XXI century manufacturing industry due to cost-saving policies and environmental regulations. To this aim, the research presented in this paper details how machine learning-based algorithms can be an effective way to predict and minimize the energy consumptions in the widely spread radial-axial ring rolling (RARR) process. To analyze this bulk metal forming process, 380 numerical simulations have been developed using the commercial SW Simufact Forming 15 and considering three largely utilized materials, the 42CrMo4 steel, the IN 718 superalloy, and the AA6082 aluminum alloy. To create the database for both multi-variable regression and machine learning models, ring outer diameters ranging from 650 mm to 2000 mm and various process conditions including different sets of tool speeds and initial temperatures have been considered. For the case of the multi-variable regression model, to account for all the cross-influences between all the parameters, a second-order function including 26 parameters has been developed, resulting in a reasonable average accuracy (94 %) but also in an impractical huge equation. On the other hand, the machine learning model based on the Gradient Boosting (GB) approach allows obtaining a similar accuracy (96 %) but its compact form allows a more practical utilization and its training can be expanded almost indefinitely, by adding more results from both numerical simulations and experiments. The proposed approach allows to quickly and precisely predict the energy consumption in the RARR process and can be extended to other manufacturing processes.

Investigation of compaction by ring rolling on thermal sprayed coatings

Often mechanical parts have specific requirements in regard to the underlying alloy. Especially for axial symmetric parts like rings an often requested characteristic is a different requirement on the inner surface than on the outer. Therefore, different production processes exist. One strategy is to roll a basic ring e.g., via radial-axial ring rolling (RARR), and create a functional layer afterwards via thermal spraying. The temperature of the ring and the spraying particles influence the bonding behavior and compaction of the thermal sprayed layer benefits its mechanical properties. The hypothesis presented in this paper is a better performing thermal sprayed layer regarding the adhesion of the layer, reduced porosity and improved residual compressive stress through the combination of the thermal spraying process and the radial-axial ring rolling. After an introduction to the industrial processes of radial-axial ring rolling and thermal spraying, the experimental setup and the results are presented. In the first step, the approach of combining these processes includes an experimental design, in which the rolling of the basic ring, consisting of a 1.0570-steel, gets interrupted right before the target diameter is reached. The ring is then transported to the thermal spraying process where the ring gets covered with layers of 316L-Si, sprayed with a pressure of 8 bar, arc voltage of 34.0 V and a wire feedrate of 2.1 m/min. The coated rings were rolled to two different target diameters with a planned degree of deformation of 0.025 and 0.098 only in the radial direction. This deformation leads to an elongation of the outer circumference of 2.56 % and 10.26 %. Due to the rolling process, the applied coatings revealed a compacting effect and, thus, a significant reduction in the total porosity. The main goal of this study is to investigate the material pairings and parameters regarding thermal spraying and RARR to produce an intact coating with adequate adhesion to the substrate. The long term vision is to adapt thermally sprayed coating into the radial-axial ring rolling process.

Towards an intelligent FE simulation for real-time temperature-controlled radial-axial ring rolling process

The rolling path for a radial-axial ring rolling (RARR) process defines the matched motions of all the rolls including the main roll, mandrel, guide rolls and axial rolls. Materials with low thermal conductivity such as titanium alloys show an overheating in the local region of the ring due to the concentration of forming energy. The temperature sensitivity of these materials leads to severe microstructure and forming defects. Therefore, for the RARR process of titanium alloy rings, it is a core industrial technology to plan a rolling path by which the temperature in the whole ring can be maintained within a desired range (a designed control objective of ring temperature) to improve the mechanical properties. In this work, an intelligent real-time temperature-controlled finite element (FE) model for the RARR process of TA15 titanium alloy rings has been developed under ABAQUS/Explicit software. In the intelligent simulation, by establishing a ring growth velocity model relating to the ring temperature, the motions of various rolls could be driven in real time by the control objective of the ring temperature. Without definition of rolling path prior to the simulation, the new intelligent simulation realizes the self-adaptive control of rolling process to maintain the ring temperature within a desired range. By only one calculation, the rolling path satisfying the control objective of temperature can be obtained. This work achieves quick planning of the rolling path for real-time temperature-controlled RARR process.

반경-축 방향 링 롤링 공정의 정의 및 제어를 위한 분석적인 접근법

This research aims to provide a useful algorithm for the prediction of the geometrical expansion of flat rings in the radialaxial ring rolling process in case of multiple variations of the mandrel feeding speed during the process. The proposed algorithm was subjected to a 2-phases validation process, where results were compared with those of laboratory experiments, conducted at 150℃ on rings made of AA-1070 and AA-6061 aluminum alloys, and with numerical simulations, considering 7 different rings with outer diameter ranging from 800 to 2000 ㎜ and made of 42CrMo4 steel alloy, Ti6Al4V titanium alloy and AA-6061 aluminum alloys. In the first and second validation phases, the maximum deviation in the estimation of the outer diameter of the ring has been calculated in 1.7% and 6.82%, respectively. According to the results of the validation, the proposed algorithm is able to properly predict the geometrical expansion of the ring for multiple variations of the mandrel feeding speed during the process and has good accordance with both relatively small and large rings.

Control of ring rolling with variable thickness and curvature

Radial-Axial Ring Rolling (RARR) is an industrial forging process for making strong, seamless metal rings. Conventionally, rings are made circular with constant cross-section. In this work we demonstrate a sensing and control strategy to create rings with variable radial wall thickness and variable curvature using standard RARR hardware. This has a number of potentially useful applications but also provides an understanding of how to control these properties for conventional RARR. The sensing uses a calibrated video camera to take a series of images of the ring top surface. Image processing is employed to measure and track the ring material in-situ. The complete state of the ring is represented by the ring thickness and curvature as a function of its volume fraction, which is computed by combining the measurements from the unoccluded areas with estimates of the ring shape elsewhere. Additionally, we present a marking technique for tracking of material as it rotates through the rolling machine, even after significant deformation of the ring has occurred. We show that rings with a wide range of variation in local thickness and curvature can be formed using conventional RARR hardware and a photogrammetric state measurement technique, combined with open-loop scheduling and feedback control of thickness and curvature. Rings with both variable thickness and non-circular shapes have been produced virtually using numerical simulations and in reality using modelling clay as a material to simulate metals at forging temperatures. We demonstrate that this technique extends the range of shapes achievable with standard RARR hardware.

Rolling paths design assisted by target-temperature driven intelligent FE simulation of radial-axial ring rolling

The microstructures of hard-to-deform materials such as titanium alloy are very sensitive to temperature change in hot working process. During ring rolling process, unreasonable rolling paths will lead to drastic temperature change in local region of ring, thus damaging the microstructure and performance of rolled ring. This work proposes a method for designing the rolling paths which could accurately control the ring temperature by target-temperature driven intelligent FE simulation. The main idea of target-temperature driven intelligent simulation is introduced. An intelligent 3D-FE model for TA15 titanium alloy ring rolling is established under ABAQUS/Explicit environment. The rolling paths under different initial conditions are obtained by intelligent FE simulations. The influence rule of initial conditions on rolling paths is revealed. The temperature control effects and change under different initial conditions are discussed. Considering the temperature control effects, a feasible range of initial ring temperature is suggested. Using the proposed method, the quick and accurate design for the rolling paths in radial-axial ring rolling process is realized. It is of great significance for the design and optimization of rolling paths and the accurate regulation of ring temperature in actual production.

An improved process for grain refinement of large 2219 Al alloy rings and its influence on mechanical properties

The large 2219 Al alloy rings used to connect propellant tank components of a satellite launch vehicle to each other are conventionally manufactured by radial-axial ring rolling at 460 °C with 50% deformation, but often suffer from coarse elongated grain and low ductility. An improved process (hot ring rolling at 460 °C with 30% deformation, then air cooling to 240 °C, followed by ring rolling at 240 °C with 20% deformation) was tested for ring manufacturing. The corresponding microstructure evolution and mechanical properties of the produced rings were studied. The results show that the improved process can successfully be applied to manufacture the large 2219 Al alloy rings without formation of macroscopic defects, resulting in a product with fine and uniform grains after heat treatment. The fracture mechanism of both rings was mainly intergranular fracture. With the resulting grain size refinement due to the improved process, more homogeneous slip occured and the crack propagation path became more tortuous during the tensile testing process. Thus, the elongation in all three orthogonal directions was greatly improved, and the axial elongation increased from 3.5% to 10.0%.

Slip line model for forces estimation in the radial-axial ring rolling process

In the research presented in this paper, a slip line-based model is proposed for the estimation of both radial and axial force in the radial-axial ring rolling (RARR) process. Based on the shape of the contact arcs between ring and tools in the two deformation gaps present in the ring rolling process, a recursive algorithm for the calculation of the two slip line fields starting from the two pairs of opposite tools is derived and implemented in a commercial spreadsheet software (MS Excel). By considering the stress boundary conditions applied to the portion of material undergoing the deformation, both for the radial and axial deformation gaps, the pressure factors those make the two slip line fields starting from the two opposite tools to intersect are calculated and utilized for the estimation of radial and axial forces, for each round of the process. The developed model has been validated by cross-comparing its results with those of laboratory experiment and numerical simulation. For the validation study case, the average deviations, in comparison to the experimental results, are calculated in 1.86% and 4.55% for the slip line force model whereas in 6.86% and 0.88% for the numerical simulation, for the radial and axial forces respectively. The proposed slip line model has been also utilized for the estimation of radial and axial forming forces of nine different study cases of flat rings having the outer diameter ranging from 800 mm to 2000 mm, observing a maximum deviation, in comparison to the relevant FEM simulation, of 4.92% (radial force) and 5.88% (axial force). The developed slip line force model allows estimating almost in real time and with a reasonable accuracy the process forces and, for this reason, it may be of interest for both industrial and academic researchers dealing with the set-up and control of the radial-axial ring rolling process.

Effects of rolling curve on recrystallization evolution during hot radial-axial ring rolling of super lager alloy steel ring

Hot radial-axial ring rolling is one typical ring rolling method, which is suited to manufacture a large-scale ring at high temperature. Rolling curve describes the changing process of the cross-section of the blank ring to the target ring. For a certain target ring, when the blank ring is determined, different rolling curves can influence the rolling shape and microstructure of the final ring. In this study, effects of rolling curve on recrystallization evolution during hot radial-axial ring rolling of a super lager alloy steel ring were studied. Firstly, based on the material recrystallization mathematical model, a finite element modeling method for recrystallization evolution of large ring rolling process was developed by adopting subroutine in ABAQUS. The dynamic recrystallization, static recrystallization and metadynamic recrystallization were considered. A comparison between simulation and experiment result was made to verify the prediction accuracy. Then, the design method of the rolling curve was given. Finally, the recrystallization evolution including dynamic recrystallization, static recrystallization and meta-dynamic recrystallization during hot radial-axial ring rolling was revealed. Besides, simulations of different rolling curves were made and the effects on recrystallization evolution were analyzed. The results show that a S-type rolling curve is preferred to realize smaller grain size and more uniform grain distribution.