Workpiece Geometry Research Articles

Improving the shape and dimensional accuracy of hollow semi-finished products during hot reverse extrusion from square workpieces

Stamping of round hollow semi-finished products by hot reverse extrusion from round and square workpieces is the first stamping step in the manufacture of hollow products for special purposes. These products require certain mechanical properties along the wall height and in the bottom part, which can be achieved by working out the metal structure by plastic deformation. The bottom part is worked out by reverse extrusion. The wall properties are obtained in the following stages of drawing the semi-finished product with thinning after extrusion. The use of square workpieces results in more intensive working out of the bottom of the semi-finished products during the reverse extrusion. However, as a result of extrusion, four protrusions appear at the end of the semi-finished product wall in places that match the corner areas of the workpiece. The presence of protrusions leads to the need for an additional operation of cutting the end of the semi-finished product before extrusion, as well as to an increase in metal usage. Therefore, an urgent task is to study the method of eliminating protrusions in the semi-finished product and the additional cutting operation.To improve the accuracy of the shape and size of hollow semi-finished products during hot reverse extrusion from square workpieces.Elimination of protrusions is achieved by forming chamfers in the corner zones of the square workpiece by preliminary deposition before extrusion.The finite element method (FEM) was used to model the processes of hot deposition of corner zones on a square workpiece and subsequent reverse extrusion with the dispensing of round hollow semi-finished products. The dimensions of the chamfers on the high-carbon steel workpiece were determined, which ensured the elimination of protrusions at the end of the wall of the round semi-finished product after extrusion. The dependence of the deposition and extrusion forces on the punch movement was determined. The specific forces on the deforming tool were determined. The distributions of stresses, strains, and temperature in the deformed metal at the end of deposition and extrusion are presented. The working out of the metal structure by hot plastic deformation is evaluated. The design of a stamp for deposition and extrusion is developed.The geometry of a square workpiece with chamfers in the corner zones was determined, which ensures the elimination of protrusions at the wall end during hot reverse extrusion with the dispensing of round hollow semi-finished products

Control of hole rolling on 3D Servo Presses

Innovative press concepts with multiple degrees of freedom such as the 3D Servo Press (3DSP) allow the implementation of incremental forming processes, thus the production of previously unfeasible workpiece geometries. This publication demonstrates that elliptical double-sided collars can be formed out of sheet metal through closed-loop control of the ram pose and controlled ram tilting. For this purpose, a new tool has been developed that enables the process of flexible hole rolling on a 3DSP. We show that the geometry of produced parts can be influenced by adapting the tool path trajectories and present a compensation approach that ensures the highly accurate insertion of the collars into the sheet metal. The geometries as well as the resulting courses of the collar height over the circumference of the hole, are analysed using experimental and FEM-based simulation results.

Reinforcement learning for cooling rate control during quenching

PurposeThe premise of this research is that the coupling of reinforcement learning algorithms and computational dynamics can be used to design efficient control strategies and to improve the cooling of hot components by quenching, a process that is classically carried out based on professional experience and trial-error methods. Feasibility and relevance are assessed on various 2-D numerical experiments involving boiling problems simulated by a phase change model. The purpose of this study is then to integrate reinforcement learning with boiling modeling involving phase change to optimize the cooling process during quenching.Design/methodology/approachThe proposed approach couples two state-of-the-art in-house models: a single-step proximal policy optimization (PPO) deep reinforcement learning (DRL) algorithm (for data-driven selection of control parameters) and an in-house stabilized finite elements environment combining variational multi-scale (VMS) modeling of the governing equations, immerse volume method and multi-component anisotropic mesh adaptation (to compute the numerical reward used by the DRL agent to learn), that simulates boiling after a phase change model formulated after pseudo-compressible Navier–Stokes and heat equations.FindingsRelevance of the proposed methodology is illustrated by controlling natural convection in a closed cavity with aspect ratio 4:1, for which DRL alleviates the flow-induced enhancement of heat transfer by approximately 20%. Regarding quenching applications, the DRL algorithm finds optimal insertion angles that adequately homogenize the temperature distribution in both simple and complex 2-D workpiece geometries, and improve over simpler trial-and-error strategies classically used in the quenching industry.Originality/valueTo the best of the authors’ knowledge, this constitutes the first attempt to achieve DRL-based control of complex heat and mass transfer processes involving boiling. The obtained results have important implications for the quenching cooling flows widely used to achieve the desired microstructure and material properties of steel, and for which differential cooling in various zones of the quenched component will yield irregular residual stresses that can affect the serviceability of critical machinery in sensitive industries.

Joint Quality Assessment of Ultrasonic Metal Welded Parts by Fracture Surface Evaluation

In ultrasonic metal welding, low specific resistances and large joining surface cross-sections require the use of mechanical testing to quantify the joint quality. In this study, different quality features of ultrasonically welded joints made of pure copper sheet are investigated during the successive phases of joint formation. Two test series with different workpiece geometries are examined. It is shown that mechanical quality features such as shear and peel forces behave differently over the formation of the joint and are not transferable. As an alternative to these, laser scanning microscopy is used to record images of the fracture surface that describe the growth of the joint area during formation. The study finds that shear tensile force growth and joint area growth are non-linear and comparable, with optimized welds achieving joint areas of 30 mm2 out of 64 mm2 and 6 mm2 out of 16 mm2. Although overall quality increases with increasing welding time, the material strength in the joint zone decreases. Depending on the original rolling condition, between 43% and 59% of the original material strength can be identified as the joint strength. The automatic analysis of fracture images is a suitable alternative to mechanical testing for similar joints.

A predictive mesoscale model for continuous dynamic recrystallization

Thermomechanical processing of titanium alloys often requires complex routes to achieve the desired final microstructure. Recent advances in modeling and simulation tools have facilitated the optimization of these processing routes. However, existing models often fail to accurately predict microstructural changes at large deformations. In this study, we refine the physical principles of an existing mean-field model and propose a calibration method that uses experimental results under isothermal conditions, accounting for the actual local deformation within the workpiece. This new approach improves the predictability of microstructural changes due to continuous dynamic recrystallization during torsion and compression experiments. Additionally, we integrate the model into the commercial FEM-based DEFORM™ 2D software to predict the local microstructure evolution within hot torsion specimens thermomechanically treated by resistive heating. Validation using non-isothermal deformation tests demonstrates that the model provides realistic simulations at high strain rates, where adiabatic heat modifies temperature, flow stress and microstructure. This study demonstrates the intrinsic correlation between microstructure, flow behavior, and workpiece geometry, considering the impact of deformation history in thermomechanical processes.

Influence of workpiece geometry and natural frequencies on ultrasonic metal welding

Ultrasonic metal welding is a well-established solid state joining process for electrical applications. The process relies on the friction between workpieces and welding tools for joint formation. This friction is generated by the process force and the ultrasonic oscillation of the welding tools imposed on the workpieces. At such high frequencies, the occurrence of resonances in actual workpiece geometries is not surprising. It is known that critical dimensions in length and width lead to nearly no bond, depending on the welding frequency and the mechanical properties of the material. In real applications, this limits the possible designs of terminals and leads to extensive testing of clamping devices. It is also known that machine learning (ML) models for quality prediction based on power signals or tool oscillation can account for changes in welding position. In this study, we investigated the impact of part resonance and antiresonance on horn and anvil oscillation, power consumption and bond strength to identify typical behaviors induced by the workpieces. The influence of material thickness and roughness was considered, and numerical analysis of the natural frequencies of the workpieces was conducted. It can be shown that the results allow a distinction between the welding positions and workpiece geometries without directly measuring the oscillation patterns of the workpieces, allowing a simple validation of geometry weldability and clamping device in applications. Furthermore, the investigation allows the knowledge based specific deduction of signal parameters for future ML models, allowing a consideration of welding position and workpieces geometry with reduced test data.

Numerical evaluation of cutting strategies for thin-walled parts

Static form errors due to in-process deflections is a major concern in flank milling of thin-walled parts. To increase both productivity and part geometric accuracy, there is a need to predict and control these form errors. In this work, a modelling framework for prediction of the cutting force-induced form errors, or thickness errors, during flank milling of a thin-walled workpiece is proposed. The modelled workpiece geometry is continuously updated to account for material removal and the reduced stiffness matrix is calculated for nodes in the engagement zone. The proposed modelling framework is able to predict the resulting thickness errors for a thin-walled plate which is cut on both sides. Several cutting strategies and cut patterns using constant z-level finishing are studied. The modelling framework is used to investigate the effect of different cut patterns, machining allowance, cutting tools and cutting parameters on the resulting thickness errors. The framework is experimentally validated for various cutting sequences and cutting parameters. The predicted thickness errors closely correspond to the experimental results. It is shown from numerical evaluations that the selection of an appropriate cut pattern is crucial in order to reduce the thickness error. Furthermore, it is shown that an increased machining allowance gives a decreased thickness error for thin-walled plates.

Active learning for the prediction of shape errors in milling

In machining processes, various influences, such as workpiece and tool geometry, process parameters or tool wear, can lead to decreasing part quality. Thus, predicting these influences to enable better process planning is essential. However, no general, analytical model is available to facilitate this. Data-driven approaches, on the other hand, are costly due to the required data labeling efforts. To address this, the authors present a data-driven active machine learning approach to predict shape errors based on process data enhanced by a material removal simulation. Using two representative pocket milling datasets, it is shown that this approach can yield better (up to 5 % decrease of RMSE) and more consistent (up to 85 % decrease in standard deviation of RMSE) model accuracy for a given budget of tactile probing points compared to a passive learning strategy.

Gain scheduled task space control of multi DOF machine tools with non-linear parallel kinematics

Machine tools with multiple degrees of freedom and parallel kinematics offer tremendous opportunities for new kind of processes but at the same time also a lot of control challenges. They are subject to uncertainties in the form of kinematic errors, elasticities and thermal deflections. Their kinematic non-linearity requires the application of model-based control concepts whose full effectiveness can only be unlocked through controls in the task space. In particular, kinematic singularities occur in forming machines such as presses, which make closed-loop control at corresponding operating points considerably more difficult. In this paper, we use the example of a servo-driven press with three ram degrees of freedom to show how parallel kinematic machine tools can be controlled via inverse differential kinematics models in task space and to what extent the controls can benefit from scheduled gain factors. The results are validated both simulatively and experimentally. In the experimental tests, three-dimensional tool paths are traced that can be used to carry out orbital forging processes. Furthermore, the example of a punch hole rolling process demonstrates that the ram pose is controlled more accurately, which also has positive effects on the resulting workpiece geometry. The performance of the developed gain scheduling task space control is compared with that of a robust and non-robust control with static gains.

CrystalMind: A surrogate model for predicting 3D models with recrystallization in open-die hot forging including an optimization framework

This work introduces CrystalMind, a surrogate model developed to accurately reconstruct 3D models along with recrystallization induced by the open-die hot forging process. CrystalMind employs two types of data inputs, a 3D Model depicting pre-stroke workpiece geometry, and a forging vector outlining the proposed forging strategy. This forging strategy consists of bite infeed, bite offset, number of offsets and initial position. Moreover, a machine learning architecture with two different units, MLP-based (Multi-Layer Perceptron) and PointNET++-based, was implemented and compared, demonstrating similar performance in terms of recrystallization and deformation errors. However, MLP proved 36 times faster in computational time reaching an average computational time of 5ms pro run. Furthermore, CrystalMind adheres to the volume conservation condition and limits recrystallization error to less than 2% and deformation error to less than 0.1 mm or 0.9% for the test data. Finally, CrystalMind is employed in conjunction with an optimization algorithm, leading to remarkable enhancements in time efficiency. The optimization framework can effectively optimize a forging vector(s) for one, two, or three strokes. For instance, in the case of three strokes, CrystalMind enables the optimization process to be completed within an average of 7 min, a stark contrast to the approximately 2.3 years required when utilizing FEM simulation. Overall, CrystalMind provides fast and accurate predictions of deformed 3D models with corresponding recrystallization per point caused by a forging process, something not to be found in the state of the art so far.

A New Dynamics Analysis Model for Five-Axis Machining of Curved Surface Based on Dimension Reduction and Mapping

The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size. Five-axis computer numerical control (CNC) milling is the main parts machining method, while dynamics analysis has always been a research hotspot. The cutting conditions determined by the cutter axis, tool path, and workpiece geometry are complex and changeable, which has made dynamics research a major challenge. For this reason, this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces, and proposes an efficient dynamics analysis model. To simplify the research object, the cutter position points along the tool path were discretized into inclined plane five-axis machining. The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining. These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object. Based on the in-cut cutting edge solved by the space limitation method, the dynamics of the inclined plane five-axis machining unit were studied, and the results were uniformly stored in the abstract space to produce a database. Finally, the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency. Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model. This study has great potential for the online synchronization of intelligent machining of large surfaces.

A hybrid modeling method for predicting the cutting force in whirlwind milling of lead screw

Analyzing the cutting force in the process of lead screw whirlwind milling can help us understand the processing quality and processability. Whirlwind milling is a process in which multiple cutting tools are driven by the cutter head to sequentially cut and process the workpiece. The cutting force changes periodically in the process of lead screw whirlwind milling. Analyzing the cutting force of the nth cutting needs to consider the influence of the previous n-1 cutting. This paper employed a hybrid method combining mathematical modeling and finite element analysis to predict the cutting force in lead screw whirlwind milling. Firstly, the tool motion path of lead screw whirlwind milling is analyzed by considering the influence of cutter inclination angle and relative tool-workpiece motion. The relationship between the nth tool path and the (n-1)th tool path is analyzed by considering the feed motion of the tool and the rotation motion of the workpiece. Furtherly, mathematical models of the nth tool path and the (n-1)th tool path are established. Then, based on the established mathematical models, the three-dimensional geometric model of the tool and workpiece is established by three-dimensional software. The establishment of the workpiece geometry model considers the influence of the previous n-1 cutting. The tool geometry model rotates along the (n-1)th cutting path, and the tool intersects the workpiece. Finally, the finite element model of lead screw whirlwind milling is established based on the above three-dimensional geometric model. The periodical cutting force in the lead screw whirlwind milling is obtained by simulating the nth cutting with the finite element model. The relevant lead screw whirlwind milling experiment was carried out to verify the reliability of the model prediction. The appropriate processing parameter range of lead screw whirlwind milling is obtained through the process parameter optimization.

A self-calibration scheme for two-dimensional free-form probing measurement under the assumption of rigid-body machine kinematic model

A major uncertainty contributor to the on-machine probing is linear axis kinematic errors. This paper proposes a novel self-calibration scheme to separate the workpiece geometry and machine tool kinematic errors from the probed profiles. Many self-calibration schemes have been well developed, but they are limited to either of the straightness, roundness, and two-dimensional (2D) grid point positions, since they require the probed points being in a closed set, as the workpiece is rotated or translated. This paper proposes its extension to a free-form 2D geometry. A key idea is on the assumption that the machine tool’s positioning error is in accordance with the rigid-body kinematic model. It makes linear axis error motions, represented in a look-up table format, be in a closed set. Two experimental case studies are presented. The uncertainty assessment is essential to investigate the effectiveness of the proposed scheme for the given workpiece geometry.

Prediction and optimisation for surface roughness distribution in external spline shaping based on micro-element method

Previous surface topography prediction models for external spline shaping do not consider the relationship between the tool and workpiece geometry and cutter edge wear. In this work, by considering the tool–workpiece geometry correlation, a model for the tooth surface topography of the external spline is established based on the micro-element method. Combined with adhere model, a gear shaper wear model is founded on the wear mechanism of cutter edge micro-element derived from the abrasive wear mechanism. The distribution characteristics of tooth topography are analysed through an external spline shaping experiment, which verifies the validity of the presented model. For the uneven distribution of tooth surface topography, a shaping process optimisation algorithm is proposed based on the advance-and-retreat method, which can obtain tooth surfaces with uniform surface topography distribution and reduce tool wear. This work can guide the optimisation of the external spline shaping process, improve the integrity of the tooth surface and prolong the serviceable life of gear shaper.

Predicting cutting forces in 5-axis milling of sculptured surfaces directly from a CAM tool path

The present work is motivated by the fact that for predicting the cutting forces which arise under various cutting conditions, workpiece-tool pairs, machining depths and tool orientation, numerical methods are slower and less efficient than analytical methods. In addition, recent developments in Computer Assisted Machining (CAM) techniques have enabled analytical methods to be applied even with complex workpiece geometry. The method developed in this paper is both practical and inexpensive. It is based on an analytical method using only few approximations and uses the toolpath file as the main information source for the machined surface. This method takes into account both tool position and orientation in a 5-axis finishing milling process with ball-end cutter, independent of the machining parameters and the couple of tool-workpiece materials. However, this calculation must be supplemented by a mechanistic approach in order to obtain results equivalent to those of the experiment. The result is a model which is implemented in a program allowing the visualization of the cutting forces along a given path. The program is easy to apply in practical cases with the possibility of implementing it in a machining software.

Prediction of the Form of a Hardened Metal Workpiece during the Straightening Process

In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.

Increasing efficiency of an industrial robot when machining timber beams – introduction of a parametric solving model to determine optimised stacks

ABSTRACT The aim of this study was to increase the economic efficiency of industrial robots (IR) when subtractive machining timber beams. The most compact possible stack configuration, consisting of three different workpiece geometries, was calculated and machined. When compared to machining individual workpieces, a reduction of the process time is strived for. The workspace of the IR should be fully utilised by an optimised spatial stack configuration (number, location and spacing of workpieces). The stacks were calculated using a parameterised evolutionary solver model in Rhinoceros, Grasshopper, Galapagos and Opossum software. Machining trials were performed to obtain process time data for the IR and as a reference for a benchmark joinery machine (JM). By applying the newly introduced optimisation model, the process time per workpiece, when machining stacks instead of individual specimens, was reduced by 16%. This result was primarily achieved by shifting the share of non-machining time to machining time. When compared to individual machining with a JM, the process time of the IR is still higher. This fact can be outweighed by advantages of IRs such as cost, availability and flexibility, showing that the overall economic efficiency of IRs does in fact hold potential for prefabrication in the timber construction sector.

Integrated profile and thickness error compensation for curved part based on on-machine measurement

Curved parts are widely used in aerospace, automotive and energy industries. The profile and thickness accuracy are both critical to some curved parts such as hollow blades. Improving the profile accuracy while ignoring the wall thickness error will reduce the qualified rate of these curved parts, and vice versa. This paper proposes a comprehensive compensation method with constraints of both profile and thickness tolerances for the machining of curved parts. The actual geometry and wall thickness of the parts are obtained by on-machine measurement system after rough machining. Both touch-trigger probe and thickness gage are used to reconstruct the actual outer and inner surface of the workpiece. Then, the comprehensive constraint considering both profile and thickness constraints is established based on the reconstructed workpiece geometry. With the comprehensive constraint, a new target outer surface is constructed. The machining error is calculated from the target surface and compensated via toolpath adjustment. At last, machining experiments are conducted to verify the feasibility of the proposed method.

Simulation of deformation behaviour of Aluminium 7075 during Equal Channel Angular Pressing (ECAP)

This paper presents a finite element simulation of equal channel angular pressing (ECAP) since it is one of the most common and successful severe plastic deformation techniques. This study reports the influence of the most significant factors influencing the ECAP technique. Through finite element simulation, the effect of the die geometry, workpiece geometry, and the pressing speed on the effective strain distributions, damage, and pressing loads, were investigated. The influence of the ECAP method on different material models is also presented. Additionally, the prospective expansion and future applications of ECAP are herein highlighted. From the results, the die geometry of a 90° channel imparts the highest strains during ECAP. Additionally, specimens of rectangular geometry are susceptible to cracking and damage as compared to circular samples. It was found that very high processing speeds (>7mm/sec) are undesirable during ECAP since they cause very high internal stresses to the structure of the workpieces. Besides, processing at room temperature can achieve homogeneous strain distribution with minimum sample damage.

Development of flexible polishing tools for synchro-speed polishing processes using additive manufacturing

A new concept for synchro-speed polishing of flat and spherical surfaces is introduced: 3D printed gradient index (GRIN) polishing tools. By using additive manufacturing technologies in combination with photopolymer plastics, GRIN tools can be fabricated that are individually adapted to the workpiece geometry. By using two different plastics, the hardness and therefore the removal rate of certain tool areas can be defined. Surface structures, benefiting material removal rate and tool wear rate, are possible as well as lightweight structures with high mechanically stability. Tools can be fabricated as thin foils as well as solid pads, ranging from small (few mm) to large diameters. Additionally, the pads can be fabricated with an individual radius. This can enable the replacement of radius-dependent tool holders, because the pads can be mounted on flat tool interfaces, since the radius is not dependent from the tool body anymore. First results from the experimental setup are showing, that by using GRIN foils similar surface quality results can be achieved in comparison to conventional polyurethane foils, while the GRIN foils are offering a lot more possibilities regarding process optimization.