Sheet Metal Processing Research Articles

Multi-Response Optimization of Single Point Incremental Forming of Al 6061 Sheet Through Grey-Based Response Surface Methodology

The single point incremental forming process (SPIF) is materialized to form the desired shapes in low-cost sheet metal processing and well suited for low batch components and customized designs. Many modifications have been attempted in recent years to maximize the formability, geometric accuracy and quality of the SPIF formed parts. In this work, an attempt has been made to analyse the influence of process parameters namely ball tool diameter, step size, spindle speed and sheet thickness on final wall thickness, forming force and surface roughness. The optimized results exhibit the desirable formability when the roller ball tool diameter of the tool is 12 mm. The results also project that formability of the sheet metal is minimized when the spindle speed is increased and the ball diameter maximizes the accuracy and surface roughness. Also minimizing the step size increases the product quality features. Upon conducting this multi-response optimization by grey based response surface methodology (RSM) technique It is identified that 12 mm ball diameter of the tool, 0.25 mm step size, 2445 rpm spindle speed, and 2 mm sheet thickness are the obtained optimized SPIF process parameters as confirmed through confirmation experiments.

Numerical and experimental investigation of extrusion processes from coil

The use of coil material as semi-finished product in sheet-bulk metal forming (SBMF), offers the possibility to use high-speed presses and thus to achieve high output quantities. However, compared to industrially established forming of pre-cut blanks, the reduced geometric accuracy of the parts due to the asymmetrical material flow represents a challenge. Against this background, the aim of this study is to gain an in-depth knowledge on the influence of the process setup on the material flow direction and on the forming of geared functional parts. A combined numerical-experimental approach of each a lateral and a backward extrusion process of a DC04 sheet metal ( t0 = 2 mm) is applied for a fundamental process analysis. For this purpose, process-dependent causes for the asymmetric part forming are analyzed by means of material flow simulations. Subsequently, the numerical results are verified experimentally in terms of the part geometry, the process force and grain structural changes. Based on the numerical and experimental results, cause-effect relations between material flow, part accuracy and tool load are derived. In addition, the transferability of the causes of the asymmetric material flow to processes with different primary material flow directions and part geometries is verified. The findings facilitate the future counteraction of asymmetrical material flow in SBMF from coil, which in turn leads to enhanced component quality.

In-situ synchrotron diffraction analysis of deformation mechanisms in an AA5083 sheet metal processed by modified equal-channel angular pressing

Severe plastic deformation (SPD) is a process to significantly improve the mechanical properties of materials by grain refinement. For forming at room temperature (RT), higher strengths can thus be achieved; for increased temperatures (HT), greater elongations can be achieved. This great potential has been chiefly applied only on laboratory scale due to a lack of industrial process design. Equal-channel angular pressing (ECAP) offers the possibility to integrate these advantages in industrial processes. This paper investigates a modified ECAP process for aluminum AA5083 sheet metal. Two passes of ECAP for sheet metal and an additional heat treatment were used. To study the material deformation on a micro-scale, in-situ synchrotron measurements at DESY (Hamburg) were performed to analyze the evolution of lattice strains and dislocation densities during tensile testing. The mechanical properties of the ECAP-processed sheet material reveal an increase in yield strength and a decrease in elongation at RT. Higher strains can be measured for the processed material at elevated test temperatures. In-situ diffraction results show a changed behavior of the ECAP sheet material compared to the reference material for both temperature regimes. These changes are based on the increased defect density in the material, which leads to increased strength at RT. At HT, dynamic recrystallization occurs during tensile testing, and the prevailing deformation mechanisms reconfigure. Electron backscatter diffraction (EBSD) and micrographs are used to interpret and supplement the observations. The findings provide the basis for the in-depth understanding of the deformation behavior of the material and help to apply the ECAP process on an industrial scale.

Recent development in feeding mechanism of sheet metal shearing machine for productivity improvement: A review

Sheet metal working processes find extensive applications in industries such as automobile and aerospace. However, the sharp edges of sheet metal pose a safety hazard to workers, potentially leading to injuries. In response to this concern, we have conceived and developed an automatic sheet metal cutting machine. Feeding mechanism is one of the important part of sheet metal shearing machine. In this paper, recent research papers are reviewed. This papers also reports recent development in feeding mechanism and its role for productivity improvement. Also, proposed innovative feeding mechanism represents a pivotal advancement in the field of mechanical workshops, aligning with industry demands for increased productivity and safety in sheet metal processing. Paper throws lights on benefits of automation into the sheet metal cutting process, which a significantly reduces in labour costs while ensuring precise and efficient cutting operations. Paper also focuses the various parameter such as cutting speed, material properties.

Study of Anisotropic Behavior in Sheet Metal Forming.

Since sheet metal exhibits significant anisotropy in processing and forming, which has a significant impact on its performance during processing, forming, and use, we explore the anisotropic behavior of materials in the forming process of sheet metal. The ability of the Yld2000-2d criterion to describe anisotropic behavior is analyzed, and its accuracy for characterization of the anisotropic behavior of metal plates is improved, based on which anisotropic behavior is predicted in three-dimensional space. Theoretical and experimental results on the anisotropy of sheet metal are compared, and two materials, 5754O aluminum alloy and DP980 steel plate, are tested and analyzed, and the anisotropic behaviors, such as three-point bending and cylindrical deep-drawing, are well predicted.

Effect of tool profile on micro hardness, forming limit, and final thickness in incremental hole flanging process of DC01 steel and AA1050 sheet metals

Effect of tool profile on micro hardness, forming limit, and final thickness in incremental hole flanging process of DC01 steel and AA1050 sheet metals

Computer Aided Process Planning for Sheet Metal Cutting Operations in the Manufacturing Industry

This paper describes computer-aided process planning for sheet metal cutting operations. The two designs to production stages that is highlighted are sheet metal processing and machining. There are four modules in this object's system. Virtual factory environments, feature-based designs, process planning, and process-based feature mapping. Feature-based design is utilized for the conception, modeling, and representation of the components for manufacturing applications. Whenever it involves sheet metal cutting operations in the manufacturing sector, computer-aided process planning, is extremely important for streamlining manufacturing procedures. The provides a general introduction to computer-aided process planning as it relates to sheet metal cutting, emphasizing its importance in boosting productivity, saving costs, and raising product quality. The main goal of computer-aided process planning for sheet metal cutting is to integrate computer technology, CAD/CAM systems, and sophisticated algorithms to automate and streamline the planning process. Based on design requirements, material characteristics, and production limitations, this method enables manufacturers to produce exact and ideal cutting plans. To create blanking and piercing holes, stamped or punched die are utilized in generative shape design; the generative computer-aided process planning system is created in C++ and used in various case studies presented in the present work. The application of computer-aided process planning for sheet metal cutting has several advantages, including greater output, less material waste, shortened lead times, and improved competitiveness in the industrial sector. The potential of computer-aided process planning to revolutionize sheet metal cutting operations, making them more efficient and cost-effective while ensuring high-quality final products is highlighted in the present research.

Nondestructive material characterization and component identification in sheet metal processing with electromagnetic methods

Electromagnetic methods for non-destructive evaluation (NDE) are presented, with which sheet metal components can be identified and their material properties can be characterized. The latter is possible with 3MA, the Micromagnetic Multiparametric Microstructure and stress Analyser. This is a combination of several micromagnetic NDE methods that make it possible to analyse the microstructure in a ferromagnetic material and to determine quantitative values of the mechanical material properties or the stress state. In the case of cold forming, the 3MA application for pre-process testing of sheet metal is discussed. Based on the 3MA information, the formability of the sheets can be predicted. To apply 3MA in-line, the influence of the relative speed and the relative distance between the 3MA probe head and the sheet was investigated. In a second study, a spatially resolved eddy current (EC) method was used to create an image of the intrinsic material microstructure of a component for its identification and traceability. It turned out, that these intrinsic fingerprint images can still be recognized even after subsequent plastic deformation or coating of the surface. This enabled the development of a marker-free traceability method for sheet metal processing. It is based on a low-cost array sensor and a specimen identification using robust and partly redundant features of the fingerprint images processed by machine learning (ML).

Cut surface characteristics of aluminum alloy sheet in cryogenic shearing process

Die cutting is a well-known process of sheet metal forming for separating sheet metal into the required shape. Compared with other cutting processes such as machining, this process has the advantages of a high production rate and low production cost. Currently, as a necessary process in sheet metal manufacturing, this process has been researched to improve the efficiency of the process and quality of cut components. In this study, the application of cryogenics in the die-cutting process was investigated, and the characteristics of the cut surfaces were examined. The shearing process was investigated using a die-cutting model. An aluminum alloy grade A5083 (JIS standard) was used as the workpiece. After shearing, the physical characteristics of the cut surfaces were examined using a 3-D laser scanner. Shear forces were also reported. The grain evolution in the shearing zone was also investigated. The results revealed that compared with the shearing process at room temperature, the ratio of clean cut to workpiece thickness was slightly increased. However, they showed differences in fracture characteristics. A concave feature in the fracture zone was generated at the cryogenic temperature, particularly for small clearances. These results were clearly explained based on the initial fracture angle and its propagation, and grain evolution. Based on the changes in the material properties at cryogenic temperatures, an elongated grain structure was easily generated, resulting in a larger initial fracture compared with that of the shearing process at room temperature. This is important when using the cut component, as the strength of the cut part decreases owing to the larger concave features. In addition, it provides helpful information on cut components that may require additional operations.

Study on the explainability of deep learning models for time series analysis in sheet metal forming

In recent research, force signals have been utilized to estimate the wear of tool components in sheet metal processing using, e.g., artificial neural networks (ANNs). ANNs learn predictive models from raw time series signals without requiring prior knowledge and manual feature engineering. However, ANNs are black-box models. Existing research on explainable AI provides methods to increase the transparency of ANNs, but mainly focusses on computer vision problems. This publication proposes an approach to learn explainable ANNs for time series data. The presented approach is applied to a tool wear prediction task using force signals from a fine blanking machine.

Study on material-data-driven process parameterization in fine blanking

In industrial sheet-metal processing, processes are parameterized based on the material specifications. However, the manufacturing process of sheet-metal coils determines the material properties and as every manufacturing process is subject to variations, variations in material properties occur in the semi-finished product influencing the subsequent sheet-metal processing. The production of high-quality parts requires considering these varying material properties to adapt the processes accordingly. This paper shows that material data from non-destructive eddy current measurements and process data of the coil production enable decision support for the parameterization of fine blanking processes. A material-data-driven approach using material data recorded in an industrial setting is employed to investigate the influence of parameter settings in the fine blanking process on specific quality characteristics of fine blanked workpieces.

Investigation on a novel in-line incremental die forming process for sheet metals

In this study, an alternative to the conventional cold-roll-forming process was developed. The new in-line incremental die forming process is a continuous manufacturing process that can provide rapid prototyping of automotive components. This new process combines the pressing and drawing operations. The feasibility of using ultra-high-strength steel (UHSS) sheets to manufacture real automotive components usually produced with the conventional cold-roll forming process was evaluated. Finite element simulations incorporating the mechanical properties of plasticity and ductile fracture were conducted to validate the new process. The results demonstrated that the proposed forming process efficiently produced automotive components with consistent shapes and lengths, and the simulation outputs were in good agreement with the experimental data in terms of dimensional accuracy and quality. Additionally, sensitivity analyses of process parameters such as friction and die design provide valuable insights for optimizing the newly proposed formation process of UHSS sheets.

Effect of a strain rate dependent material modeling of a steel on the prediction accuracy of a numerical deep drawing process

In the production of sheet metal components, batch and process fluctuations cause deviations in the resulting component properties, which often lead to production rejects. To counteract this inline, the computing time for predicting the process result and optimizing the process parameters must be very short, which is why analytical models are advantageous. A large database is usually required for modeling, and numerical simulations are well suited for generating it. The stamping velocity is a process parameter possibly varying, but strain rate dependency of the material often is neglected in numerical simulations. The objective of this study is to analyze the effects of strain rate dependent material modeling on the simulation accuracy of a sheet metal forming process. Therefore, uniaxial tensile tests and layer compression tests at different strain rates are conducted on the steel HC340LA. Based on this, the material behavior is captured in a strain rate dependent material card, which is used for the numerical simulation of a deep drawing process of a geometry with complex shape. For the validation of the model, experiments are carried out and being compared with the computational results in terms of force–displacement curves and part geometry. Furthermore, numerical investigations are used to analyze if drawbead height and blankholder force have an influence on the strain rate distribution and whether this affects the process force.

A new method to exploit ultrasonic vibrations in deep drawing process

In the deep drawing process of sheet metals, lack of formability and the defects generated as a consequence of friction are two main limiting factors. Using ultrasonic vibration is an impressive way to decrease these limitations. In this paper, the ultrasonic – aided deep drawing process with a novel way of applying radial ultrasonic vibration is presented. The novelty of the presented method is using the bending mode of vibration in the sheet metal and the die using radial ultrasonic vibration. The presented method, contrary to the other techniques of applying ultrasonic vibration, has shown much fewer resonance frequency variations by changing the sheet metal geometry during the process and likewise with a variation of the clearance between the blank holder and the die. For this purpose, the finite element method was used in designing the equipment for the deep drawing process. The experimental test was done on St12 in the presence and absence of ultrasonic vibration. The effect of ultrasonic vibration in the presented method on the wrinkling and thinning defects and likewise the forming forces were experimentally studied. The results indicated the high efficiency of the presenting method in decreasing both the forming forces and the wrinkling and thinning defects. About a 42.3% decrease in the forming forces and a 4.4% decrease in the thinning effect were achieved by applying ultrasonic vibration.

Multi-objective parametric optimization of driver-based electromagnetic sheet metal forming of SS304 using AA6061-T6 driver

This study uses statistical techniques to optimize the electromagnetic forming process for driver-based sheet metal forming with a flat coil. AA6061T6 sheet was used as a driver to form SS304 sheets. The optimization of maximum elongation, die fitting, maximum Lorentz force, and peak current was carried out. The results were verified through simulation which were in close agreement with the experimental results. The most influential factor was the voltage (C.R of 72.97%), followed by coil gap and SS304 sheet thickness (7.99 and 6.07%, respectively).

Establishing Equal-Channel Angular Pressing (ECAP) for sheet metals by using backpressure: manufacturing of high-strength aluminum AA5083 sheets

Severe plastic deformation (SPD) processes offer the possibility of improving the mechanical properties of metallic materials by grain refinement. However, this great potential has so far mostly been applied on a laboratory scale or on small series. Equal-Channel Angular Pressing (ECAP) also enables to integrate the advantages in industrial processes with large output—so far, mainly for bars or thick plates. In this paper, we investigate the ECAP process for sheet metal. Preliminary investigations have shown that cracks form on the surface when aluminum AA5083 sheets are processed. To solve this problem, we determined the Johnson–Cook fracture criterion for the material and modeled the process numerically. The simulation was carried out with the superposition of a backpressure and subsequently implemented and validated experimentally. The semi-finished sheet metal products from the ECAP investigation were then mechanically characterized with microhardness measurements and tensile tests. In addition, the microstructure was investigated with Electron Back Scatter Diffraction (EBSD). Even comparatively small amounts of backpressure (10 MPa) already result in a significant suppression of the crack formation in the numerical and experimental investigations. The microhardness measurements indicate a more homogeneous strain distribution for a sufficient level of applied backpressure which enables the processing of crack-free sheets in multiple ECAP passes. As with ECAP of bulk materials, tensile tests on the processed sheets show a reduced elongation to failure (− 73%) but a significantly increased yield strength (+ 157%) compared to the initial condition of the material. Distinct substructures are found in the EBSD measurements and explain this behavior. The findings provide the basis for using ECAP on an application-oriented scale and demonstrate an advanced manufacturing method for the production of high-strength aluminum sheets.

Experimental validation of IS2062 deep drawing component

Deep drawing isa leading manufacturing method in which a sheet metal blank is radially stretched into a die by action of a punch to produce various parts of automobiles, computers, electronic items and many other things used in day-to-day life. Optimization of sheet metal forming process parameters was important to reduce the cost of manufacturing a product. Forming a defect-free drawn part is always a challenging task. This work presents analysis and experimental identification of the deep drawing process in order to avoid earring effect and Stretcher strain defect. In our present work, low carbon structural steel (IS2062, Carbon 0.17 to 0.19 wt%) is used for deep drawing application. Factors mainly influencing the friction coefficient, blank holder force, and die nose radius, which are the sheet metal process characteristics that exhibit deformation behaviour. In an experiment, radial clearance between the die and the punch was taken as 0.15 mm on each side. Deep drawing operation was carried out on a 1200 ton hydraulic press with a servo controlled mechanism, a 3 stage forming process was taken and values were compared with the FEA, A cylindrical cup with a LDR of 1.8 has been successfully created experimentally.

Application of a neural network for predicting cutting surface quality of punching processes based on tooling parameters

Punching represents one of the most frequently used manufacturing processes in the sheet metal processing industry. Thereby, one of the most important quality criteria for such punching processes constitutes the geometric shape of the cutting surface. High cutting surface qualities are usually characterized by the lowest possible edge draw in, a high clean cut, as well as a small fracture surface and a low burr height. In this respect, conventional punching processes can only produce clean cut proportions (CCP) up to 20-50% of the sheet thickness. By means of the so called “concave punch nose design” developed at the Institute for Metal Forming Technology, the geometry of conventional punches could be optimized in such a way that the clean-cut proportion along the cutting surface is significantly increased. The findings reported about in this paper show that the quality parameters of the sheared component edges thereby show highly nonlinear relationships to the tooling and sheet metal material parameters used. In order to quantify these effects, the data from several numerical punching simulations is used to pretrain an artificial neural network (ANN). Input data for the neural network included features of the punch geometry, the size of the clearance, the sheet thickness and the material data of the semi-finished product. The output of the neural network is precise predictions of the achievable cutting surface quality parameters. The experimentally validated findings presented in this paper show that the process design for novel punching processes such as cutting with concave punch nose design is possible, when using a modern machine learning approach.

Study on Deformation Force of Hard Aluminum Alloy Incremental Forming

The deformation force is an important factor affecting the forming accuracy of parts in the incremental forming process of sheet metal. This paper proposes an analytical calculation method of the deformation force based on pure shear deformation. After assuming and simplifying the factors affecting the deformation force, a graphical method is used to approximate the contact area between the forming tool and the sheet metal. A forming test is also designed. In addition, the deformation force is measured in the experiment, and its theoretical analysis value is compared with the actual measurement value of the forming test to validate the analytical method of deformation force calculation. The results show that the radial forming deviations are 28.5% and 22.5%, the axial deformation force deviations are 9.8% and 16.1%, and the forming force deviations are 6.3% and 10.3%, which demonstrates the effectiveness of using the analytical method to calculate the deformation force.

Study on learning efficient stroke representations in clocked sheet metal processing: theoretical and practical evaluation

Clocked manufacturing processes such as sheet metal forming and cutting processes pose a challenge for process monitoring approaches due to inaccessibility of tool components and high production rates which make direct measurement of the physical process conditions unfeasible. Auxiliary data such as force signals are acquired and assessed, often still relying on control and run charts or even visual control in order to monitor the process. The data of these signals are high-dimensional and contain a large amount of redundant information. Therefore, the processing of such signals focuses on compressing information into as few variables as possible that still represent the important information for the manufacturing process. Due to repeatability in clocked sheet metal processing, the data generated consist of a series of time series of the same operation with varying physical conditions due to wear and variations in lubrication or material properties. In this paper two major research objectives are identified: (i) the theoretical evaluation of representation learning methods in context of clocked sheet metal processing, and the connection with (ii) the practical evaluation of the learned representations with a given use case to track the wear progression in series of strokes. The contribution of this paper is the comparison of varying time series representation learning techniques and their performance evaluation in a theoretical and practical scenario.