Sustainable Process Design for Special Welded Profiles via Roll Forming Compression

Abstract

This study explores the sustainable design and optimization of processes for creating special cross-section welded profiles using the roll forming compression method. This technique is recognized for its efficiency in producing complex profiles while minimizing material waste, making it a valuable approach to environmentally friendly manufacturing. The research examines key factors such as material selection, process settings, and welding techniques to improve energy efficiency and reduce environmental impact. Finite Element Analysis (FEA) was employed to simulate the forming process, helping to predict the profiles’ behavior and ensure they meet structural and dimensional requirements. Experimental tests were conducted to validate the simulation results and assess the performance of the profiles under practical conditions. The findings highlight the potential for saving both materials and energy while maintaining consistent quality, demonstrating the benefits of sustainable manufacturing in advanced profile production.

1. Introduction

Sheet metal forming is a common method for shaping metals by pressing them between tools (or dies) to create the desired shape. Simple parts are turned into more complex ones as the tools apply pressure to the material. Over time, sheet bending methods have improved, boosting efficiency and allowing for more intricate designs [1]. Sheet bending methods are constantly improving to make processes more efficient and enable the creation of more complex products. These advancements not only enhance productivity but also allow for greater design flexibility, making it easier to produce high-quality, complicated items at a faster rate. Roll forming is a process where a flat strip is gradually shaped into a desired profile by passing through a series of rolls. Known for its high production volume and low labor requirements, roll forming is widely used in the electronics, construction, greenhouse, and automotive industries. The process can handle materials with high strength and limited ductility, making it ideal for manufacturing structural and crash components in the automotive sector. While the process involves complex material and geometric nonlinearities, experienced staff and precise calculations guide roll and flower pattern designs. Roll forming also enables the production of long section lengths, limited only by machine capacity, and closed sections can be created using welding techniques such as electric resistance, laser, or TIG [2,3,4,5,6]. The research compares four sheet metal forming methods, finding the combined bending method best for high-strength plates. Simulations analyze strain and stress patterns during forming, showing that high-strength steel eases welding and stress management. To address defects like strain concentration, the roller design and production settings are optimized [7].

Designing complex-shaped profiles requires careful planning, including creating a suitable flower pattern, tool design, and conducting FEA. FEA helps understand sheet metal behavior during shaping, allowing for tool optimization. When designing welded profiles, the production technique must be chosen first: either the classical method, where the sheet is shaped gradually until welding, or the compression method, which involves forming a tube and compressing it into the desired profile. This thesis focuses on developing process designs for shaping tubes and achieving specific cross-sections using the compression method in a continuous roll forming line, as well as exploring designs for welded tube production.

According to DIN 8582 [8], forming techniques are categorized into five subgroups based on the primary direction of applied stress. DIN 8586 [9] classifies roll forming as a method involving rotary die movement. This fast and efficient process is used to transform flat sheets into functional profiled sections.

A roll forming machine consists of several key components that work together to shape sheet metal into the desired profile. The process begins with the uncoiler, which feeds metal coils into the machine at the correct speed after they are cut into strips by a slitting machine. Any waviness in the sheet is corrected by the straightener to ensure smooth feeding. If the profile requires holes, a prepunch unit creates them before the sheet enters the roll forming stations. These stations use upper and lower shafts to gradually shape the sheet, with the number of stations depending on the profile’s complexity. For more advanced profiles, compression stations are used to transform welded tube sections into specific shapes through precise, multi-directional adjustments. Together, these components enable efficient production of high-quality metal profiles.

The roll forming process relies on carefully configured parameters like line speed, roll spacing, roll gap, and roll diameter, all of which play a key role in shaping product quality and tooling design. Researchers have used simulations and experiments to refine these parameters. Li et al. [10] showed that chain die forming can handle variable cross-sections effectively. Kasaei et al. [11] tackled flange wrinkling with a flexible roll forming system, while Rossi et al. [12] studied the influence of bending angles on strain in high-strength and stainless steels. Lindgren et al. [13] found that stronger steel reduces peak strain but increases deformation length. Paralikas et al. [14,15] emphasized that roll spacing and strain distribution are critical for consistent forming. Murugesan et al. [16] explored issues like bowing, flange height deviations, and spring-back in U-profile sheets. Wang et al. [17] investigated how roll spacing, friction, and velocity affect strain, introducing a floating roll design for complex, asymmetrical sections. Using COPRA software (https://www.copra.info/en/crf), simulations examined factors such as processing speed, roller spacing, and material type. The paper uses COPRA simulations to study cold roll forming of deep, asymmetrical sections, focusing on stress and strain analysis. To address the issue of oversized rolls due to deep sections, a floating roll device is proposed. The study explores how parameters like roll spacing, friction, line speed, and roll diameter affect the process, using statistical methods to find the optimal combination. The result is a stable section design that meets product quality standards while keeping costs low [17].

Industrial tubes or profiles can be made as welded. These are created using various welding methods like laser, TIG, or high-frequency welding, with electric resistance and induction welding being the most common. High-frequency induction welding is especially useful for creating both longitudinal and spiral seams. In this process, the steel strip is shaped into a tube by pressure rolls, and the edges are heated by induction coils before being pressed together to form the weld. The key to this process is the “Vee” shape where the edges meet, as this is where the weld forms. The heat from the induction is carefully controlled, ensuring a strong and efficient weld. Once the profile is formed, it is cut to the desired length using a mechanism that moves with the production line, typically using sawing or shear cutting methods.

2. Profile Selection

Within the scope of this study, the process design parameters of the profile shown in Figure 1 were investigated. High-frequency welding has been selected as the welding method. The profile, designed as the primary structural component of the excavator cabin, has been optimized to facilitate the assembly of the doors and front glass.

The application of the profile is illustrated in Figure 2. Based on the intended use of the profile and the associated installation requirements, ISO 2768 general tolerances standard [18] was specified by the cabin designer. The tolerance dimensions for the profile, in accordance with ISO 2768 tolerance class, are presented in Figure 3.

The profile has a thickness of 2.5 mm, with S355MC chosen as the raw material by the designer. The tolerances for length, twist, and straightness are as follows:

• Length: 6000 mm ± 2 mm
• Twist: Maximum 1° per meter
• Straightness: 0.001 × L (mm)

Since the profile will be used in a visible area within the cabin, it is essential to minimize any surface imperfections, such as scratches or waves. The key inputs for the process design are listed as “technical drawings, sheet thickness, raw material properties, tolerance class, customer-specific requirements, critical dimensions, roll-forming machine parameters (e.g., number of stations, station distance, axis dimensions)”.

The design inputs serve as the foundation for the process design. Optimizations can be incorporated into the process to ensure that the tolerances are met. The first step in the process design is to develop the flow pattern, which will guide the design of the necessary tools.

3. Flow Pattern Development

The flow pattern was developed using the roll-forming-specific design software, Copra RF [19]. During the transition from a tube to the final profile, the centerline length shortens due to material compression. This reduction is quantified by a compression factor, expressed as a percentage, which determines the extent of shortening. The total shortening is distributed across the individual shaping steps.

Initially, the tube diameter is calculated based on the compression ratio, with larger compression ratios corresponding to larger tube diameters. Drawing from experience, the compression ratio for this process was set at 1%.

Each sheet material exhibits unique characteristic strength values, which significantly influence its shaping behavior. Yield strength, the point at which permanent deformation begins, plays a critical role in this process [20]. Errors in the flow pattern design can lead to defects in the final product. While the overall longitudinal strain in roll forming is relatively low, secondary plastic strains can cause common defects like edge ripple [21] and oil canning [22]. To minimize these issues, the material’s true stress–strain curves must be accurately determined, and optimization of the flow pattern should be carried out based on finite element analysis results.

The tube diameter was calculated as Ø64.5 mm, taking the 1% compression factor into account and considering the final section dimensions. The compression factor, which can also be expressed as an absolute value in millimeters, is detailed in

Table 1. Compression Factor.

Tube Diameter	Compression Factor (%)	Compression Factor (mm)
Ø64.5	%1	1.9 mm

. Further optimization of this factor will be performed based on the results of finite element analysis.

When forming a tube into its final profile, the features of the final profile are linearly projected onto the corresponding features of the tube. The shaping steps are approximated as cross-sections derived from a wire model, which is defined by the feature points of both the tube and the final profile. In COPRA, profile sections are represented by a combination of lines and arcs. However, the sections of the wire model, determined by the tube and final profile, differ as they consist of curves based on the geometry of the final profile. These general curves in the shaping steps are approximated using the lines and arcs of a COPRA profile section [19].

For this process, the change angles were distributed equally across all stations. The tube-forming process was completed in four stations. The progression of the formed steps is illustrated in Figure 4.

The roll-forming tools were adapted to align with the flower pattern. Each station comprises four rolls: top, bottom, right, and left. For the purposes of finite element analysis, it is sufficient to define the tool surfaces; therefore, the tools were not sized based on the roll-forming machine parameters at this stage. Tool sizing will be finalized after optimization is carried out based on the simulation results. The tool design is illustrated in Figure 5.

4. Determination of Nonlinear Sheet Curve

The chemical properties of the material play a crucial role in determining its mechanical properties. A chemical test was conducted using a spectrometer, in accordance with EN 10149 standards (EN 10149-2 S355MC Automotive Steel) [23]. The tests were performed with the Foundry-Master HITACHI testing device. The test device and the sheet material sample are shown in Figure 6 and Figure 7. The results of the chemical test are presented in

Table 2. Results of Chemical Testing.

Grade				Thickness
S355MC				2.5 mm
%C	%Mn	%Si	%P	%S	%Cr	%Ni	%Cu
0.06	0.52	0.01	0.014	0.007	0.07	0.08	0.23
%Al	%V	%Mo	%Nb	%Ti	%B	%N	%S
0.032	0.004	0.016	0.015	0.001	0.0001	0.009	0.012

.

The tensile test is a standard method for assessing the mechanical properties of materials, including yield strength (YS), ultimate tensile strength (UTS), and elongation percentage [24]. Material deformation was measured using an optical extensometer with a gauge length of 50 mm. A minimum of three tests were performed for each material and condition, with the results averaged (

Table 3. Results of Tensile Testing.

Material	Tensile Strength (Rm) [N/mm2]	Yield Strength (Re) [N/mm2]	Elongation %A
S355MC	466 MPa	379 MPa	%34

). The tensile testing device is shown in Figure 8. The S355MC stress–strain curve (Figure 9) illustrates the relationship between stress and strain for the material, providing insights into its mechanical behavior. These parameters are essential inputs for FEA software, which is used to model the mechanical properties of deformable materials.

The strain–stress data after the yield point were recorded using the output from the tensile test. A total of 25 data points were selected to be used in Equations (4) and (5). These 25 data points are presented in the table below (

Table 4. 25 Data Points After Yield Point.

MPa	%Strain
379.12	0.0154
390.79	0.0192
395.60	0.0210
407.62	0.0265
417.92	0.0320
422.04	0.0347
426.16	0.0375
433.37	0.0430
438.87	0.0484
443.33	0.0536
447.45	0.0589
450.20	0.064
452.95	0.0696
455.35	0.0749
457.07	0.0804
459.13	0.0860
460.50	0.0916
461.53	0.0973
462.56	0.1031
462.91	0.1060
463.59	0.1090
464.28	0.1151
464.97	0.1206
465.65	0.1269
466	0.1316

).

The results of the tensile test alone are insufficient for generating the material’s true stress–strain curve. The test assumes a constant cross-sectional area of the material, but as force is applied, the material’s cross-sectional area decreases, leading to an increase in stress. Therefore, the curve obtained from the tensile test, known as the engineering curve, must be converted to a true stress–strain curve to account for the changing cross-sectional area during deformation. A more accurate approach is to use a true stress-true strain model. In this model, the true stress (σ_T) is defined as the applied load (F) divided by the instantaneous cross-sectional area that is undergoing deformation, typically in the necked region beyond the tensile point.

$${\sigma }_T={\displaystyle \frac{F}{A_i}}$$

(1)

Moreover, there are situations where it is more practical to express strain as true strain (ε_T), which is defined as follows:

$${\epsilon }_T=\mathrm{l}\mathrm{n}({\displaystyle \frac{l_i}{l_0}})$$

(2)

If no volume change occurs during deformation, i.e., if:

$$A_i{l}_i=A_0{l}_0$$

(3)

Then true and engineering stress and strain can be related using the following formulas:

$${\sigma }_T=\sigma \left(1+\epsilon \right)$$

(4)

$${\epsilon }_T=\ln \left(1+\epsilon \right)$$

(5)

The strain–stress values presented in Table 4 represent the data points used to construct the linear curve of the material, spanning from the yield point to the tensile point. Non-linear data points were calculated using Formulas (4) and (5) and are detailed in

Table 5. Non-Linear Data Points Calculated Beyond Yield Stress.

MPa	%Strain
385.35	0.0154
398.29	0.0190
403.93	0.0208
418.41	0.0261
431.29	0.0315
436.68	0.0341
442.15	0.0398
452.03	0.0421
460.11	0.0472
467.11	0.0522
473.82	0.0572
479.13	0.0623
484.46	0.0672
489.48	0.0723
493.84	0.0774
498.60	0.0825
502.69	0.0876
506.46	0.0929
510.25	0.0981
511.97	0.1007
514.13	0.1035
517.72	0.1089
521.06	0.1139
524.76	0.1195
527.33	0.1236

. The yield point in the data used for creating the linear curve is 379 MPa.

In the non-linear region (Figure 10), this corresponds to a value of 385.35 MPa. The elastic region lies below the yield point, and the strain value corresponding to 385.35 MPa was assumed to be zero to focus on analyzing the plastic region. Consequently, the strain value at 385.35 MPa was subtracted from the other 24 data points. The simulation data points derived from this process are provided in

Table 6. Simulated Material Data Points (Assuming 385.35 MPa Strain = 0).

MPa	%Strain
385.35	0
398.29	0.0036
403.93	0.0054
418.41	0.0107
431.29	0.0161
436.68	0.0187
442.15	0.0244
452.03	0.0267
460.11	0.0318
467.11	0.0368
473.82	0.0418
479.13	0.0469
484.46	0.0518
489.48	0.0569
493.84	0.062
498.60	0.0671
502.69	0.0722
506.46	0.0775
510.25	0.0827
511.97	0.0853
514.13	0.0881
517.72	0.0935
521.06	0.0985
524.76	0.1041
527.33	0.1082

.

During the cold deformation process, the elastic recovery of the material is known to influence the corner deformation of the workpiece. In the study, this effect was accounted for by creating a non-linear (true) stress–strain curve of the material, as explained in Section 4. The curve was generated using the data points provided in Table 6.

The strain at the yield point of the material was identified as 0, with the next strain value defined as 0.0036 mm. All node movements below this strain threshold were simulated to return to their original state, representing the elastic recovery of the material. This recovery was incorporated into the simulation through a constitutive model that considers the elastic region of the material’s behavior.

The results obtained from the simulation included the elastic recovery within this region, ensuring that the effect was reflected in the analysis of corner deformation. By including this consideration, the deformation behavior of the workpiece was evaluated with greater accuracy, particularly at the corners, which are critical areas in cold deformation processes.

5. Finite Element Simulation

The finite element model was created using the roll forming process in Copra FEA, which utilizes an implicit solver. To reduce calculation time, eight-node, iso-perimetric, arbitrary hexahedral elements (Element Type 7 in the MSC Marc Element library [25]) were used to mesh the metal strip with a single element based on its thickness.

The boundary conditions and assumptions for the finite element analysis are as follows:

• Friction between the rolls and the material is ignored.
• The rotational movement of the rolls is not considered.
• The material’s mechanical properties are assumed to be homogeneous throughout the entire strip.
• The strain hardening from the previous process in the tube is ignored.
• Rollers and roll shafts are assumed to have infinite rigidity.

The simulation model is depicted in Figure 11. The formed rolls were designated as ‘rigid surfaces’ and fixed in all five degrees of freedom, while the tube was treated as deformable.

Both the rotational dynamics of the roller and the frictional interaction between the workpiece and the roller were neglected during the finite element analysis. This was carried out to save computational time and make the model more efficient so that a parametric study analysis of the compression ratio’s effect on profile shaping as a function of tube diameter could be conducted. The omission of roller rotation and friction alters the boundary conditions by removing the interactions with tangential forces and the effects of rotational inertia, both of which can affect stress distribution and deformation behavior. These factors were, however, neglected to achieve consistency in all analyses and to guarantee the validity of comparisons among various compression ratios.

Figure 11. Simulation Model Visualization.

Figure 11. Simulation Model Visualization.

Two simulations were run with the same boundary conditions, with the sole difference being the compression ratio. In this way, the influence of the compression ratio on the resulting profile geometry could be decoupled while, at the same time, reducing computational complexity. Although neglecting friction and rotational effects may cause deviations from reality, the main objective of this work was to compare the relative effect of compression ratio changes in a range of controlled and uniform simulation parameters.

The distance between stations was established as 250 mm. As shown in Figure 12, the tube’s mesh structure was designed with a length equivalent to 2.1 times the distance between stations. This structure consists of 40,000 nodes and 19,856 elements. Figure 13 represents the boundary conditions.

The tube was constrained at the first row of nodes along the flow direction and at the rear. Additionally, it was fixed at the back using three different nodes in the x and y directions. A touch contact was established between the deformable tube and the forming tools. The analysis was performed based on these defined boundary conditions, material properties, and contact settings. The stress distribution on the material is illustrated in Figure 14.

The designed section was analyzed by comparing it with the simulation results to evaluate its accuracy. The shape transitions at each station were examined, with the red color representing the simulation output and the blue color corresponding to the flow pattern at the respective stations. Figure 15, Figure 16, Figure 17 and Figure 18 illustrate how the simulation outputs compare with the desired section at each station. Figure 15 focuses on the first station, Figure 16 on the second, and Figure 17 on the third station. Figure 18 continues with the comparison for the fourth station. These comparisons help assess how well the simulation results match the target shape at each stage of the forming process.

The flow pattern was compared with the simulation outputs, and it was observed that no thinning occurred at the bend edges. Initially, the tube diameter was set at Ø64.5 mm based on a 1% compression ratio. However, the simulation results indicated that the final cross-section was smaller than the desired dimensions. As a result, the decision was made to increase the tube diameter and rerun the simulation. The optimized compression ratio was set to 3%, which led to a new tube diameter of Ø65.8 mm (

Table 7. Updated Compression Factor and Tube Diameter.

Tube Diameter	Compression Factor (%)	Compression Factor (mm)
Ø65.8	%3	6 mm

). The flow pattern was then updated to reflect the new diameter and compression ratio, and the forming tools were redesigned accordingly.

The revised tube diameter, determined for the updated compression ratio, is Ø65.8. The flow pattern and roll forming tool design, adjusted for the Ø65.8 tube, are shown in Figure 19 and Figure 20.

The distance between the stations was set to 250 mm based on a 1% compression ratio. The mesh structure of the tube to be formed is shown in Figure 21. The tube length is defined as 2.1 times the distance between stations, resulting in a mesh structure with 52,165 nodes and 22,668 elements.

The simulation boundary conditions were defined in the same way as for the 1% compression ratio. The analysis was then re-run using these boundary conditions, along with the defined material properties and contact conditions. The resulting stress distribution on the material is shown in Figure 22.

Before re-running the simulation, adjustments were made to the tube diameter and compression ratio based on the initial results. The tube diameter was increased to Ø65.8 mm, corresponding to a revised 3% compression ratio, in an effort to address the discrepancies observed in the first round. This change aimed to bring the simulation results closer to the desired final tube shape. After making these adjustments, the flow pattern was recalculated, and the roll forming tools were redesigned to accommodate the updated tube dimensions and compression ratio. These modifications helped ensure that the material behavior was more accurately represented throughout the process, providing a clearer comparison with the desired section at each station in the re-simulation.

The redesigned section was compared with the simulation results, and the shape changes at each station were carefully examined. In comparison, the simulation output is represented in red, while the flow pattern at the stations is shown in blue. Figure 23, Figure 24, Figure 25 and Figure 26 show how the re-simulation outputs compare with the desired section at each station. In Figure 23, the results for Station 1 are presented, illustrating how closely the simulation matches the intended design. Figure 24 shows the comparison at Station 2, while Figure 25 and Figure 26 do the same for Stations 3 and 4, respectively. These figures provide a clear view of how the shape changes at each station, reflecting the impact of the adjustments made to the tube diameter and compression ratio, ensuring the final design aligns with the desired specifications throughout the process.

The flow pattern was compared with the simulation outputs. According to the results, no thinning was observed at the bend edges. In the analysis using the Ø64.5 tube, corresponding to a 1% compression ratio, a difference of 1.4 mm was found at the lower part and 0.4 mm at the upper part between the desired section and the simulation output. However, for the Ø65.8 tube, corresponding to a 3% compression ratio, this difference reduced to 0.2 mm at the lower part and 0.1 mm at the upper part. The 0.2 mm deviation falls within acceptable tolerance as shown in Figure 16. Therefore, the flow pattern and forming tools were considered acceptable.

In the analysis, the outer diameter of the steel pipe was increased from 64.5 mm to 65.8 mm. While this adjustment was deemed acceptable based on the simulation results, it is acknowledged that this change does not constitute optimization. The increase in the pipe diameter led to a corresponding change in the compression ratio, which is a key parameter in the profile shaping process.

Initially, simulations were conducted with a compression ratio of 1%. However, the profiles obtained under these conditions were found to exceed the specified tolerance limits during cross-sectional comparisons. To address this issue, preliminary studies were performed with compression ratios of 1%, 1.5%, 2%, 2.5%, and 3%. It was observed from the simulation results that only the profiles produced with a 3% compression ratio fell within acceptable tolerance limits.

For clarity and relevance, the manuscript focuses on the studies conducted using compression ratios of 1% and 3%. These cases were selected to illustrate the influence of compression ratio adjustments on the profile geometry and to emphasize how this parameter affects the ability to meet design tolerances.

6. Conclusions

The current study focused on establishing a sustainable production process for specialized welded profiles using roll forming compression methods. By optimizing tube diameters and compression ratios, the research aimed at enhancing product quality as well as production efficiency.

In order to conserve valuable simulation time, the finite element model decided to disregard the reality of friction between the workpiece and the roller, as well as the rotation of the roller. This choice effectively lowered the boundary conditions within the simulation. Although this approach enabled a more streamlined and effective parametric study, it should be mentioned that these assumptions could have led to deviations from practical conditions.

The study conducted a thorough analysis of the effects of changes in compression ratio on the profile forming process with regard to the considered tube diameter. FEA simulations were utilized in the present work for producing detailed data related to multiple factors, such as flow regimes, stress patterns, and form alterations at each station during the process. The result of this careful analysis was that a 1% to 3% increase in compression ratio resulted in greater precision in the end cross-section generated, essentially removing any variation that might have arisen from the desired design specifications. Comparing these two analyses, both of which used disparate compression ratios, effectively illustrated the deep positive impact this one parameter can make.

Reductions in tube diameter and shaping tools facilitated a more environmentally friendly process by reducing material wastage while still satisfying severe tolerance demands. Such process advancements enhanced production efficiency and limited the use of excessive material adjustments.

The research emphasizes and stresses the utmost significance of continuous optimization and constant monitoring in roll forming processes. The findings of the research offer and provide significant insights that are critical in creating more sustainable manufacturing processes tailored for special welded profiles and can further be utilized to guide similar efforts and initiatives in other industries as well.

Subsequent research experiments can incorporate the friction and roller dynamics effects to make the model more accurate and correlate with experimental conditions. The assumptions made in the scope of this research, however, enabled a consistent and focused parametric study, thus enabling an accurate determination of the impact of the compression ratio on profile development. The impact of elastic recovery was also successfully modeled and investigated in the scope of this research. Subsequent studies may consider adding other factors, such as thermal effects and strain rate sensitivity, to increase the accuracy of the model under more complex conditions. Subsequent studies may also consider other variables, including types of materials and wall thickness, to develop a more detailed methodology while being compatible with real-world manufacturing constraints. Future studies will involve further process optimization of the roll forming process by investigating other parameters that affect performance, including temperature fluctuation, material composition, and tool wear during extended runs. The use of real-time monitoring systems and machine learning algorithms will improve control of the forming process and allow it to be more flexible and efficient. The work will also be extended further to study the feasibility of applying this method with other metals and different shapes and thus extend its sustainability benefits to a wider application field. Further, automating the designing and redesigning of the forming dies and more sophisticated simulation packages will enhance design iterations and make them faster and economically feasible for the roll forming industry.