In the field of automotive manufacturing, the adoption of advanced robot technology has revolutionized production processes, particularly in areas such as automated welding and stamping. However, robot roller hemming, a critical technique for joining inner and outer panels in vehicle closures like doors and hoods, still faces challenges in achieving consistent quality compared to international standards. My research focuses on addressing the wrinkling defects that commonly occur during this process, which can compromise the aesthetic and structural integrity of automotive components. Through theoretical analysis and finite element simulations, I aim to identify key factors influencing these defects and propose optimization strategies to enhance the application of robot technology in hemming operations.
The wrinkling phenomenon in roller hemming arises from complex stress distributions during the bending and folding of sheet metal. As the roller applies pressure, the material undergoes elongation and compression, leading to localized instabilities. In my analysis, I consider a planar-linear hemming model to simplify the study. The stress concentration in specific regions, such as areas adjacent to the roller contact point, can cause fibers to buckle, resulting in wave-like wrinkles. This is often exacerbated by factors like improper roller geometry or suboptimal process parameters. To understand this, I divide the hemming zone into segments, such as A, B, and C, where each segment experiences varying stress states during roller passage. For instance, when the roller moves from segment B to C, the elongation in C compresses B, leading to bulging and eventual wrinkling. This mechanistic insight underscores the importance of precise control in robot technology applications to minimize defects.
From a mechanical perspective, the force applied by the roller, denoted as F, plays a pivotal role in stress development. Using fundamental equations, the stress σ can be expressed as:
$$ \sigma = \frac{F}{A} $$
where A is the cross-sectional area. Additionally, bending stress is given by:
$$ \sigma = \frac{M}{W_z} $$
with M representing the bending moment and W_z the section modulus. In my analysis, I examine points like a, b, c, and d in the hemming zone. Point b, under roller contact, experiences compressive stress, while points a and d are subjected to tensile stresses. The inequality σ_a < σ_c highlights the stress variations that contribute to wrinkling. According to the linear constitutive relationship for metal deformation, stress and strain are proportional:
$$ \sigma_{ij} = C_{ijkI} \varepsilon_{kI} $$
where C_{ijkI} is the stiffness tensor. This implies that increasing F amplifies stress and strain, potentially worsening wrinkles. In practice, selecting an appropriate F is crucial; too low, and the hem may not seal properly; too high, and surface imperfections like wrinkles become prominent. My research emphasizes that robot technology must integrate real-time feedback to adjust these parameters dynamically, ensuring optimal outcomes in automotive production.
To quantitatively assess wrinkling defects, I employed finite element analysis using ANSYS/LS-DYNA software, which is widely used in robot technology for simulating complex forming processes. The model parameters were defined based on typical automotive applications, as summarized in the table below. This approach allows for a detailed investigation of how various factors influence wrinkling, providing a foundation for improving robot-based hemming systems.
| Parameter | Value |
|---|---|
| Material | Deep Drawing Steel AKDQ |
| Flange Height | 7 mm |
| Roller Diameter | 50 mm |
| Inner and Outer Panel Thickness | 0.7 mm each |
| Distance Between Inner and Outer Panel Flanges | 2 mm |
In the simulation, I analyzed stress distributions across the outer panel, focusing on regions corresponding to theoretical points. For example, region 4 exhibited tensile stress, aligning with point c in the mechanistic model, while regions 2 and 3 showed compressive stresses. The alternating tensile and compressive stresses, as depicted in stress contour plots, confirmed the wrinkling mechanism. The results indicated that parameters like TCP-RTP distance (the distance between the tool center point and the roller touch point) significantly affect wrinkle severity. By varying this distance, I observed that a larger TCP-RTP value, combined with a 60° pre-hemming angle, resulted in smoother surfaces compared to 45° pre-hemming. This highlights the potential of robot technology to achieve precision through parameter tuning.
Further simulations explored the impact of multiple hemming passes and roller diameter. The table below summarizes the effects of different parameters on wrinkling degree, measured in millimeters. This data underscores how advancements in robot technology can leverage multi-pass strategies to mitigate defects.
| Parameter Variation | Wrinkling Degree (mm) | Notes |
|---|---|---|
| Friction (f1: with, f2: without) | ~5.0 (both cases) | Minimal effect |
| Roller Diameter (D1: small, D2: large) | D1: >5.1, D2: <5.0 | Larger diameter reduces wrinkles |
| Springback (H1: with, H2: without) | ~5.0 (both cases) | Negligible impact |
| Hemming Passes (45° two-pass vs. 60° three-pass) | Two-pass: >5.1, Three-pass: <5.0 | Three-pass yields better results |
The data clearly shows that using a larger roller diameter and implementing three-pass hemming at 60° effectively reduces wrinkling. For instance, in three-pass hemming, the wrinkling degree decreased significantly, leading to a more uniform surface. This aligns with the principles of robot technology, where iterative processes can enhance quality. The stress-strain relationship during hemming can be further described by the equation:
$$ \varepsilon = \frac{\sigma}{E} $$
where E is Young’s modulus, illustrating how material properties interact with applied forces. In my simulations, I also considered the effect of roller speed and pressure, but these were secondary to geometric parameters. The integration of such insights into robot technology systems can enable adaptive control, reducing reliance on trial-and-error in production.
In conclusion, my research demonstrates that wrinkling defects in robot roller hemming are primarily driven by stress imbalances, which can be mitigated through optimized parameters. The use of larger rollers, increased TCP-RTP distances, and multi-pass hemming strategies significantly improves forming quality. These findings not only advance the application of robot technology in automotive manufacturing but also provide a framework for future innovations in automated forming processes. As robot technology continues to evolve, incorporating real-time monitoring and AI-driven adjustments could further minimize defects, enhancing efficiency and product quality in industries worldwide.
Moving forward, I plan to explore dynamic modeling approaches that account for material anisotropies and environmental factors. The role of robot technology in achieving sustainable manufacturing is also a key area, as reducing defects directly correlates with lower waste and energy consumption. By continuing to refine these models, I aim to contribute to the global advancement of robot technology, ensuring its pivotal role in next-generation industrial applications.