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In recent decades, the automotive industry has had a constant evolution with consequent enhancement of products quality. In industrial applications, quality may be defined as conformance to product specifications and repeatability of manufacturing process. Moreover, in the modern era of Industry 4.0, research on technological innovation has made th...
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... component studied in this work is the upper front cross member of a car currently being produced at Tiberina company (Sangro -Atessa (CH), Italy); Figure 1 shows the image of this component realized in HR 440Y580T-FB-UC steel (2 mm thick) that is common in the automotive field for cold forming of structural components. It is a hotrolled steel strip; in particular, it belongs to the family of ferritic-bainitic steel. ...
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... kriging models are chosen to interpolate the data and are fit using maximum likelihood estimation [14]; for this reason, the surfaces may not be perfectly smooth, unlike the surfaces that could be obtained with response surface modeling that typically employs least squares regression to fit a polynomial model to the sampled data. Figures 9-13 show the metamodels obtained in correspondence with the nominal force value (1470.5 kN). These metamodels represent, respectively, how the percentage of thickened zone, the percentage of area with insufficient stretching, the percentage of the safe zone, the percentage of zone with potential splits and the percentage of thinning at Critical Points A and B vary as the two noise variables (friction coefficient and yield stress) vary. ...
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... to this approach, total desirability is defined as: Table 2 presents the notation related to the equations. This optimization was a useful tool to obtain the regulation curves shown in Figure 14. These curves, in fact, were obtained considering the points of maximum desirability (D > 0.9). ...
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... noise parameters affect the sheet draw-in. In fact, in Figure 15, taking some reference points, the draw-in is compared as a function of the punch stroke for one of the conditions with maximum desirability (safe) and for a generic non-optimized condition (cracks). From the comparison, the different sliding of the sheet is observed in the non-optimized case compared to the optimal case; this leads to excessive thinning or rupture. ...
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... Research on the influence of punch speed on product quality was reported in 2021 by Ken-ichi Manabe [5]. Research projects on the influence of parameters such as blocking force and die geometry on the quality of stamping products, such as limiting earing height, wrinkling, and tearing [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. Research on the effects of friction to improve drawing efficiency has been published [17], [18], [19], [20], [21], [22], [23], [24], [25]. ...
Meso-and microforming is a technology to shape parts from extremely small metal billets. Parts with geometric dimensions are a few millimetres to a few micrometres. With the rapid development of the electrical-electronics industry and biomedical engineering, the technology of forming microscopic parts has been researched and applied because of its efficiency, accuracy, and high productivity. Deep drawing is an operation that turns flat sheet metal blanks into hollow, open-mouth parts. It is an essential process in sheet metal stamping. Micro deep drawing is one of the micro-shaping technologies that has been widely studied and applied in recent years. However, the bases for calculating technological and geometrical parameters in the micro-deep drawing have not yet been analyzed and evaluated in detail. Therefore, this paper has proposed a theoretical basis combined with simulation applied to the design of technology to manufacture a connector head part drawing die with a diameter of 300µm and height of 1500µm using materials SUS304 material. Numerical simulation also allows evaluation of the stamping part's internal stress state, the ability to pull the workpiece into the die, and the thickness distribution on the product wall. Experimental research has also verified that, with the determined parameters, the stamping parts meet the quality requirements. This indicates the proposed calculation methods for the tiny deep drawing operation are entirely suitable. The results of this research can be wholly applied to the production of micro-sized cylindrical cup parts using the deep drawing method.
... The constant evolution of advanced learning algorithms, driven by the interplay between the availability of large amounts of data (often referred to as "big data") and the exponential growth of computer performance, has revolutionized problem-solving in engineering fields [3,4]. In the context of sheet metal forming, ML algorithms offer a novel approach in studying topics such as [5,6] classification, detection, and prediction of forming defects [7][8][9]; material modeling and parameter identification [10,11]; process classification [12], design, and optimization [13,14]. ...
... The predicted Young's modulus for each material is summarized in Table 9. As expected, the Young's modulus of the three materials is closer to the reference modulus of steel, and the predicted values are also similar to those obtained using the analytical expression, Equation (12), with differences below 10 GPa. As the modulus of elasticity obtained falls within the limits allowed by the algorithm, i.e., [190 220], the ML model that predicts the Swift hardening parameters for steels can be applied. ...
The continuous evolution of metallic alloys in the automotive industry has led to the development of more advanced and flexible constitutive models that attempt to accurately describe the various fundamental properties and behavior of these materials. These models have become increasingly complex, incorporating a larger number of parameters that require an accurate calibration procedure to fit the constitutive parameters with experimental data. In this context, machine learning (ML) methodologies have the potential to advance material constitutive modeling, enhancing the efficiency of the material parameter calibration procedure. Recurrent neural networks (RNNs) stand out among various learning algorithms due to their ability to process sequential data and overcome limitations imposed by nonlinearities and multiple parameters involved in phenomenological models. This study explores the modeling capabilities of long short-term memory (LSTM) structures, a type of RNN, in predicting the hardening behavior of a sheet metal material using the results of a standardized experimental three-point bending test, with the aim of extending this methodology to other experimental tests and constitutive models. Additionally, a variable analysis is performed to select the most important variables for this experimental test and assess the influence of friction, material thickness, and elastic and plastic properties on the accuracy of predictions made by neural networks. The required data for designing and training the network solutions are collected from numerical simulations using finite element methodology (FEM), which are subsequently validated by experiments. The results demonstrate that the proposed LSTM-based approach outperforms traditional identification techniques in predicting the material hardening parameters. This suggests that the developed procedure can be effectively applied to efficiently characterize different materials, especially those extensively used in industrial applications, ranging from mild steels to advanced high-strength steels.
... Several studies [17][18][19][20] proposed a feedforward control for minimising the effect of process fluctuations. Several practical solutions were explored for measuring noise parameters in this type of control. ...
During the forming process, variations in noise parameters can negatively impact product quality. To prevent waste from these fluctuations, this study suggests a method for the in-line optimisation of the deep drawing process. The noise parameter considered is the friction coefficient, assuming the variability in lubrication conditions at the blank–tool interface. The proposed approach estimates the noise factor variability during the process by tracking the draw-in of the blank at critical points. Using this estimation, the optimal blank holder force (BHF) is calculated and then adjusted in-line to modify blank sliding and prevent critical issues on the component. For this purpose, a Finite Element (FE) model of a deep drawing case study was developed, and numerical simulation results were used to construct surrogate models while estimating both the friction coefficient and optimal BHF. The FE model’s predictive capability was verified through preliminary experimental tests, and the control logic was numerically validated. Results show the effectiveness of this control type. By adjusting the BHF just once, a defect-free component is achieved. This method overcomes the limitations of feedback controls, which often need multiple adjustment steps. The time required to estimate the friction coefficient and the maximum time available for adjusting the BHF without causing defects was identified.
... In this process, the blank is forced by a punch inside a die while it is held down against the die by a blank holder which applies a force [2]. The blank holder force is one of the most influential process parameters [5][6][7][8][9]. Specifically, the increase in the blank holder force leads to a reduction in material sliding, causing the risk of splits. ...
... The feed-forward control is a predictive type of control, which consists in measuring the coil material properties or the lubricant thickness on the blank before the process in order to control the noise variables and identify the optimal process parameters to set during the process. Palmieri et al. [8] performed a numerical study for the identification of regulation curves of the blank holder force as a function of the friction coefficient and the yield strength of the material to be measured on the blank entering the press machine. Research carried out by Fischer et al. [12,[16][17][18] on the deep drawing of a kitchen sink involved the use of an eddy current sensor to measure the material's yield strength, tensile strength, uniform elongation, elongation at break, and initial material grain size. ...
In the deep drawing process, the scatter of the friction coefficient between blank and tool interfaces as well as of the material properties between blank positions in the coil or between different coils significantly influences the part quality. These uncontrollable fluctuations increase the risk of waste. To avoid this problem, currently, the new era of Industry 4.0 aims at developing control algorithms able to in-line adjust process parameters and always meet the part quality requirements. Starting from this context, in this study a method for process control during the punch stroke is proposed. It assumes the blank draw-in in specific points as the control variable, while the blank holder force is adopted as an in-line adjustable process parameter. The approach was implemented for the deep drawing of a T-shaped component, using a blank in DC05 steel with a thickness of 0.75 mm. The results show that the measurement of blank draw-in is a representative index of the component quality, which in this study is evaluated in terms of formability (thinning) and cosmetic (surface deflections) defects. Once the optimal condition and the corresponding blank draw-in were identified, the feedback control algorithm was able to increase or reduce the blank holder force according to whether the recorded draw-in was higher or lower than the optimal one.
... The action of the blank holder can prevent defects on the drawn component such as cracks, wrinkles and thinning [1][2]. Therefore, one of the design parameters affecting the drawing process is the force on the blank holder [3][4]. In fact, it has been shown that an increase in the force on the blank holder can lead to a reduction in wrinkles. ...
... The quality of the deep-drawn component, however, can also be affected by the process variability due, for example, to the lubrication conditions and to the properties of the material which can change depending on the supplier or the coil adopted [3]. Positioning of the blank is another noise parameter that could affect the quality of the final drawn component [5]. ...
... The idea is the development of a control strategy able to modify on-line, in the presence of the process variability, the force on the blank holder in order to restore 18 th IMEKO TC10 Conference "Measurement for Diagnostics, Optimisation and Control to Support Sustainability and Resilience" Warsaw, Poland, September 26-27, 2022 the draw-in of the blank to the optimal value. To monitor online the draw-in of the blank, laser triangulation sensors were chosen [3], [4], [7][8][9][10]. Once the FE model was calibrated and the optimal design condition found, the experimental campaign on the hydraulic press machine aims at: (i) comparing the experimental results with the numerical ones and (ii) identifying the variability band attributed to the optimal draw-in profile of the blank. ...
... The Special Issue is comprised of a total of ten research articles related to ML applications for metal forming processes, including: prediction of forming results [1] and their energy consumption [2]; constitutive modelling [3] and parameters identification [4]; process parameters optimization [4,5]; prediction, detection and classification of defects [6][7][8]; prediction of mechanical properties [9,10]. The following paragraphs summarize the contributions of these works. ...
... The optimization of forming process parameters was also investigated by Palmieri et al. [5]. This work proposes a method to perform real-time control of the blank holder force during a deep-drawing process. ...
Machine Learning (ML) is a subfield of artificial intelligence, focusing on computational algorithms that are designed to learn and improve themselves, without the need to be explicitly programmed [...]
... The blank holder force and the die radius are regarded as input variables. To establish the relationship between the blank holder force, the friction coefficient and the yield strength of the blank material, and the forming quality indices, Palmieri et al. [21] used the numerical results to develop the kriging model. ...
Considering the importance of failure prediction in the sheet metal forming design process, the ability to predict these failures by the four most common surrogate techniques, namely response surface methodology (RSM), radial basis function (RBF), kriging, and artificial neural network (ANN), was investigated. Firstly, a finite element model (FEM), which can substitute for the physical deep drawing and precisely predict thinning and rupture, has been developed. To ensure the accuracy of the FE model, a comparison between simulation results and experimental results is performed. In this study, the construction of training and test data is carried out by the Latin hypercube design (LHD) method via numerical simulation. Secondly, the four surrogate models are developed to predict thinning and fracture as a function of the six most critical parameters namely, blank holder force, punch section radius, die section radius, die fillet radius, blank thickness, and die blank friction coefficient. Finally, the performance and accuracy of these models are demonstrated by a goodness-of-fit test.
... The deformation of the metal is performed at re-crystallization temperature, which is usually equal to the ambient one for many metals [1]. In such a type of forming process, the metal sheet gets deformed plastically while it is pushed down into the die cavity, conforming it to its shape [1], and with no alterations in its thickness [2]. A schematic representation of a typical stamping press used for cold sheet metal forming is shown in Fig. 1. ...
... In fact, springback is due to deformations of the material in the elastic-plastic field. This is a critical aspect of the drawing process, especially in the automotive sector, where high accuracy is often required [2]. ...
... Differently, the monitoring of the punch force is more viable and various contributions model the deep drawing process relating the BHF to the punch force [6], [12], [14], [15]. Nevertheless, it is proven that the draw-in of the metal sheet can better reveal the correct execution of the deep drawing process [2], [7]. In fact, more recent works consider the deep drawing process as a relationship between the BHF as input and the draw-in as output. ...
Deep drawing is a metalworking procedure aimed at getting a cold metal sheet plastically deformed in accordance with a pre-defined mould. Although this procedure is well-established in industry, it is still susceptible to several issues affecting the quality of the stamped metal products. In order to reduce defects of workpieces, process control approaches can be performed. Typically, process control employs simple proportional-integral-derivative (PID) regulators that steer the blank holder force (BHF) based on the error on the punch force. However, a single PID can only control single-input single-output systems and cannot handle constraints on the process variables. Differently from the state of the art, in this paper we propose a process control architecture based on Model Predictive Control (MPC), which considers a multi-variable system model. In particular, we represent the deep drawing process with a single-input multiple-output Hammerstein-Wiener model that relates the BHF with the draw-in of
$n$
different critical points around the die. This allows the avoidance of workpiece defects that are due to the abnormal sliding of the metal sheet during the forming phase. The effectiveness of the proposed process controller is shown on a real case study in a digital twin framework, where the performance achieved by the MPC-based system is analyzed in detail and compared against the results obtained through an ad-hoc defined multiple PID-based control architecture.
Note to Practitioners
—This work is motivated by the emerging need for the effective implementation of the zero-defect manufacturing paradigm in the Industry 4.0 framework. Especially in the deep drawing process, various quality issues in stamped parts can lead to significant product waste and manufacturing inefficiencies. This turns into considerable economic losses for companies, particularly in the automotive sector, where deep drawing is one of the most used cold sheet metal forming techniques. In most applications, only sample inspections are performed on batches of finished-product, with subsequent losses of time and resources. For the sake of improving the workpiece quality, innovative strategies for real-time process control represent a viable and promising solution. In this context, the proposed MPC-based process control approach allows the correct shaping of the metal sheet that is getting deformed during the forming stroke, thanks to the draw-in monitoring at various locations around the die. The draw-in is indeed one of the most effective forming variables to control in order to provide a correct BHF during the forming stroke. A useful and easy-to-implement non-linear metal sheet deep drawing process model is provided by this paper to perform an innovative process control strategy. A comprehensive methodology is applied in detail to an automotive case study, ranging from process modeling (model identification and validation based on experimental data acquisition) to MPC implementation (controller tuning and testing and software-in-the-loop system validation). The presented method can be easily implemented on any real deep drawing press, providing the multivariable constrained process with a suitable control system able to make the stamped parts well formed.
... In the literature, machine learning algorithms have been already applied to various manufacturing topics, such as for the prediction of joint strength of ultrasonic welding processes [22], to estimate the tool wear in milling operations [23], to diagnose the dimensional variation of additive manufactured parts [24], to classify the cutting phase of the natural fiber reinforced plastic composites [25] and to predict the tool life in the micro-milling process [26]. More recently, Wang et al. [27] developed a deep learning-based algorithm for the recognition of the defects in the strip rolling process, Marques et al. [28] investigated the performances of parametric and non-parametric models for the correlation of process and material variables to springback and wall thinning, Palmieri et al. [29] defined a metamodel to correlate the process parameters and key-quality indicators for the optimization of the blank-holding forces in the stamping process, and Winiczenko [30] utilized a hybrid response surface methodology combined with a genetic algorithm to simulate and optimize the friction welding parameters in AISI 1020-ASTM A536 joints. ...
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.
... Owing to the many difficulties that arise from increasing nonlinearity, adjustment direction, and surface description, the classical approach is to optimize the process parameters, such as blank-holding force, the location and shape of the holder, selective lubrication, tool parts, and kinematics. For some examples, please see [25,26], and recently [27][28][29]. Since these problems are often solved through optimization algorithms, a large number of computer simulations of the forming process are needed. ...
This paper presents a numerical method for simultaneous optimization of blank shape and forming tool geometry in three-dimensional sheet metal forming operations. The proposed iterative procedure enables the manufacturing of sheet metal products with geometry fitting within specific tolerances (surface and edge deviations less than 0.5 or 1.0 mm, respectively) that prescribe the maximum allowable deviation between the simulated and desired geometry. Moreover, the edge geometry of the product is affected by the shape of the blank and by an additional trimming phase after the forming process. The influences of sheet metal thinning, edge geometry, and spring-back after forming and trimming are considered throughout the blank and tool optimization process. It is demonstrated that the procedure effectively optimizes the tool and blank shape within seven iterations without unexpected convergence oscillations. Finally, the procedure thus developed is experimentally validated on an automobile product with elaborated design and geometry which prone to large springback amounts owning to complex-phase advanced high strength steel material selection.
