International Journal of Material Forming最新文献

The present paper aims to introduce a new finite element approach in numerical modeling of the impact tube hydroforming process. For this purpose, the coupled Eulerian-Lagrangian method is used to replicate the formation of the water flow, resulting from an impact, leading to the fabrication of flawless T-shaped copper tubes. One major advantage of such coupled Fluid-Structure Interaction (FSI) modeling is that it eliminates the need for measuring the parameters associated with the process including the internal pressure, and works with the minimum number of inputs such as the impact velocity. Moreover, ductile damage analysis has been performed in FE studies to further investigate the damage evolution in specimens. Experimental tests are also carried out to examine the viability of performing the impact tube hydroforming process in low velocities and also to validate the authenticity of the presented numerical method. Results corroborate the accuracy of the presented numerical approach in predicting the process parameters, the final shape, and the onset and evolution of rupture in fabricated tubes. The feasibility of this approach shows promise in wide application for finite element modeling of the hydroforming process.

Autoclave consolidation is used to manufacture continuous fiber composites in applications that have strict part porosity requirements. The applied positive pressure in this process is attractive for reduction in part porosity. However, some part geometries can cause porosity issues even under positive pressure. One is the concave corner seen in an L-bracket geometry. Higher porosity is seen in areas of high curvature, hindering part quality. Since the autoclave process takes several hours and prepreg material is expensive, trial-and-error methods of resolving issues are not practical. In this work, a unique physics-based viscoelastic model is proposed to describe the mechanical behavior of uncured continuous fiber thermoset prepreg undergoing consolidation under hydrostatic pressure. This model considers stress due to compaction of the fiber network, compression of voids in the resin, and viscous stress from resin flow relative to fibers. The constitutive expressions for these are coupled to important mechanisms that occur during autoclave consolidation. The viscoelastic model is incorporated into the finite element analysis software ABAQUS/Standard using a UMAT subroutine. Numerical results are validated by analytic solutions and experimental comparison for flat and L-bracket geometries. A parametric study identifies important process and material parameters that influence the quality of the manufactured part.

Efforts to enhance sustainability in all areas of life are increasing worldwide. In the field of manufacturing technology, a wide variety of approaches are being used to improve both resource and energy efficiency. Efficiency as well as sustainability can be improved by creating a circular economy or through energy-efficient recycling processes. As part of the interdisciplinary research group "Light—Efficient—Mobile" investigations on the energy-efficient friction-induced recycling process have been carried out at the department of Forming and Machining Technology at Paderborn University. E.g. using the friction-induced recycling process, different formless solid aluminum materials can be direct recycled into semi-finished products in an energy-efficient manner. The results of investigations with regard to the influence of the geometrical shape and filling rate of the aluminum particles to be recycled as well as the rotational speed of the continuously rotating wheel are explained in this paper. In addition to the recycling of aluminum chips, aluminum particles like powders from the field of additive manufacturing are processed. Based on these results, the future potentials of solid-state recycling processes and their contribution to the circular economy are discussed. The main focus here is on future interdisciplinary research projects to achieve circularity in the manufacturing of user-individual semi-finished products as well as the possibility to selectively adjust the product properties with the continuous recycling process.

Incremental sheet metal forming (ISF) is a versatile dieless forming process for manufacturing complex sheet metal components. The toolpath is one of the most critical process parameters, significantly influencing the ISF formability. The conventional toolpath strategies, such as spiral and constant z-slice-based tool paths, do not prove helpful for complex asymmetries in part geometry. The approach to toolpath planning in ISF should consider both material behavior and design complexity. This work compares conventional toolpaths with two strategies, namely feature-based and space-filling fractal tool paths. Material thinning and geometric deviations are critical limitations for successful part development. All toolpath strategies were evaluated for material distribution, geometric accuracy, and fracture depth using four carefully designed components with gradually increasing asymmetry. As evident from the results obtained, the material deformation was sensitive to the choice of toolpath strategies. The feature-based tool path captures the part curvatures more uniformly, leading to homogeneous thickness distribution. At the same time, fractal-based strategies lead to lower overall geometric deviation in the region of curved profiles.

A numerical investigation on uniaxial compression tests is performed to highlight the heterogeneous nature of the deformation process. Resulting errors on the material behaviors are deduced from the obtained force versus displacement data with the assumption of homogeneous deformation. The numerical study considers a range of strain rates (from 0.01 to 0.5 (s^{-1})), Coulomb friction coefficients (up to 0.3), and elasto-viscoplastic power law behaviors. The heterogeneous nature is characterized in terms of sample shape, strain, and strain rate heterogeneities. The results show that the final shape of the sample at a given macroscopic strain is influenced not only by the friction coefficient but also by the material properties. The levels of strain and strain rate heterogeneities in the samples can be significant in some conditions, leading to large errors when exploiting the force versus displacement data with the hypothesis of homogeneous strain. The estimations of the strain rate sensitivity parameters are not significantly affected by the strain heterogeneities, but the errors on the strain hardening parameters can be as large as 40 %. The apparent strain hardening parameter appears to be artificially strain rate sensitive. Being systematically lower than the material strain hardening parameter, when measured at lower strain rates, this underestimation will induce a systematic error in the determination of material properties and should be taken into consideration.

This work presents an interpretability study with XAI tools to explain an XGBoost model for hardness prediction in the simultaneous double-frequency induction hardening. Experiments were carried out on C45 steel spur-gear. In order to explain the model, firstly, the built-in tool of the XGBoost library was used to interpret the feature importance. Then, a more advanced approach with the SHAP library was employed to highlight local and global explanations. Finally, the implementation of an interpretable surrogate model allowed to illustrate rules for prediction, making the explanation, although approximate, clear. This study proposes a relevant approach of AI to explain the results obtained by black box models which is currently a major element for the industry allowing to justify the quality of the results in a clear way. It is concluded that the model is consistent with physical principles.

New structural sheet metal parts are developed in an iterative, time-consuming manner. To improve the reproducibility and speed up the iterative drawability assessment, we propose a novel low-dimensional multi-fidelity inspired machine learning architecture. The approach utilizes the results of low-fidelity and high-fidelity finite element deep drawing simulation schemes. It hereby relies not only on parameters, but also on additional features to improve the generalization ability and applicability of the drawability assessment compared to classical approaches. Using the machine learning approach on a generated data set for a wide range of different cross-die drawing configurations, a classifier is trained to distinguish between drawable and non-drawable setups. Furthermore, two regression models, one for drawable and one for non-drawable designs are developed that rank designs by drawability. At instantaneous evaluation time, classification scores of high accuracy as well as regression scores of high quality for both regressors are achieved. The presented models can substitute low-fidelity finite element models due to their low evaluation times while at the same time, their predictive quality is close to high-fidelity models. This approach may enable fast and efficient assessments of designs in early development phases at the accuracy of a later design phase in the future.

4D printed hydrogels are 3D printed objects whose properties and functions are programmable. In the definition of 4D printing, the fourth dimension arises from the ability of printed structures to change their shape and/or function over time when exposed to given conditions environmental stimuli, during their post-press life. Stimulation-responsive hydrogels produced by the emerging 4D bioprinting technology are currently considered as encouraging tools for various biomedical applications due to their exciting properties such as stretchability, biocompatibility, ultra-flexibility, and printability. Using 3D printing technology, customized functional structures with controllable geometry and trigger ability can be autonomously printed onto desired biological interfaces without considering microfabrication techniques. In this review, by studying the progress in the field of hydrogels for biointerfaces, we summarized the techniques of 4D printing gels, the classification of bioinks, the design strategies of actuators. In addition, we also introduced the applications of 4D hydrogels in tissue repair, vascular grafts, drug delivery, and wearable sensors. Comprehensive insights into the constraints, critical requirements for 4D bioprinting including the biocompatibility of materials, precise designs for meticulous transformations, and individual variability in biological interfaces.

The present work investigates the effect of rotational speed on joint quality during Friction Stir Spot Welding (FSSW) using a consumable pin, where a consumable pin is used with a rigid tool shoulder for welding AA6061-T6 sheets to produce an exit-hole-free FSSW joint. Joint quality was analysed using lap shear test, macrostructure, microstructure and microhardness analysis at five rotational speeds, viz. 360, 462, 557, 900 and 1200 revolutions per minute (RPM). The joint strength increased with increase in rotational speed from 360 RPM to 900 RPM and then decreased with further increase in rotational speed. A 1.7 times increase in joint strength was observed for FSSW at 900 RPM with reference to 360 RPM. As expected, both energy input and temperature increased with rotational speed. The energy input increased by only 27.5% as the rotational speed was increased from 360 to 900 RPM. Finite element (FE) simulations were conducted for validation and study using commercial FE software, DEFORM-3D, to predict temperature distribution, force, torque and material flow. Lap shear test simulations matched with experimental results within reasonable (∼7%) accuracy except for very low rotation cases. However, failure load provided better matching with experimental results when Cockcroft-Latham damage model was used instead of Freudenthal damage model.