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Traditional fatigue fracture theory and practice focus principally on structural design. It is thus too conservative and inappropriate when used to predict the high-cycle fatigue life of dies used for metal forming, especially cold forging. We propose a novel mean stress correction model and diagram to predict the high-cycle fatigue lives of cold forging dies, which focuses on the upper part of the equivalent fatigue strength curve. Considering the features of die materials characterized by high yield strength and low ductility, a straight line is assumed for the tensile yield line. To the contrary, a general curve is used to represent the fatigue strength. They are interpolated, based on the distance ratio, when finding an appropriate equivalent fatigue strength curve at the mean stress and stress amplitude between the line and curve. The approach is applied to a well-defined literature example to verify its validity and shed light on the characteristics of die fatigue life. The approach is also applied to practical forging and useful qualitative results are obtained.

A multi-stage cold forging process was developed and complemented with finite element analysis (FEA) to manufacture a high-strength one-body input shaft with a long length body and no separate parts. FEA showed that the one-body input shaft was manufactured without any defects or fractures. Experiments, such as tensile, hardness, torsion, and fatigue tests, and microstructural characterization, were performed to compare the properties of the input shaft produced by the proposed method with those produced using the machining process. The ultimate tensile strength showed a 50% increase and the torque showed a 100 Nm increase, confirming that the input shaft manufactured using the proposed process is superior to that processed using the machining process. Thus, this study provides a proof-of-concept for the design and development of a multi-stage cold forging process to manufacture a one-body input shaft with improved mechanical properties and material recovery rate.

Driveshafts are used in all vehicles, and their service life is expected to be at least three years or 100.000 km. Many driveshaft manufacturers prefer friction welding due to its relatively cheaper cost and ease of the process. However, they should meet some property-related criteria to achieve the expected lifetime. The forging technique becomes essential to succeed in these mechanical requirements. A comparative study evaluates the performance of constant velocity joints (CVJs) produced by multi-step warm–cold forging and friction welding processes. Medium carbon steels were used in both of the techniques. The microstructures, mechanical properties (i.e. hardness, strength, impact energy and shear strength), low-cycle fatigue (LCF) properties, wear resistance and cost-efficiency (number of operations, material saving, number of produced components and cost) are compared in detail for an industrial production point of view. The experimental results reveal that warm–cold forged specimens exhibit superior mechanical properties such as increased strength, hardness, relatively higher impact energy, improved shear strength, relatively longer LCF life and enhanced wear resistance (lower wear volume loss). In addition, it is also assessed that warm–cold forging is a more cost-effective manufacturing process (reduced weight, decreased number of operations and increased yield) in the production of CVJs compared to the friction welding process.

Mo element was added to cobalt-based alloy L605, and cold forging deformation was performed. The effects of the addition and cold forging deformation on the microstructure and mechanical properties of the alloy were studied by thermodynamic calculation, electron backscatter diffraction, transmission electron microscopy, and X-ray diffraction. The stacking fault energy (SFE) of the alloy decreased after the addition, and the formation of stacking faults and intersections were promoted to improve the strength and hardness. The tensile strength of the alloy with Mo increased from 1190 to 1702 MPa after 24% cold deformation, producing significant work hardening. The strengthening mechanism is strain-induced martensitic transformation (SIMT) and deformation twinning. The alloy, combined with Mo and after 24% deformation, had both high strength and ductility in comparison with the original cobalt-based alloy L605. This is attributed to the lower SFE which caused the increase in stacking fault density. During the tensile process, the ε-hcp phase was easily generated at the stacking fault to reduce the stress concentration and increase the ductility. Controlling SIMT by adjusting the density of stacking faults can improve the mechanical properties of cobalt-based alloys. The ε-hcp phase, the interaction between deformation twins and dislocations, and the interaction between ε-hcp phases during cold forging deformation caused local stress concentration, lowering ductility and toughness.

Cold forging backward extrusion is mainly employed in the manufacturing of axisymmetric cup-like components used extensively in automotive and aerospace assemblies due to the process-induced strength that has a pivotal role in such applications. Although cold forging backward extrusion yields mechanically robust components, it demands high forces, subjecting tooling to immense stress, thereby restricting process capacity. The process encounters hindrances in gaining widespread industrial acceptance due to frequent failures of die elements, necessitating proper die design and control of major influencing factors for process viability and cost-effectiveness. The punches in backward extrusion are often susceptible to failures when processing steel billets. The punch service life is significantly affected by geometrical attributes, the type of steel undergoing deformation, and tool manufacturing aspects. Hence, the present study evaluates punch performance in cold forging backward extrusion using optimized geometrical attributes, manufactured through a design of an experimental approach comprising an L9 orthogonal array. The manufacturing factors considered are punch material, hardness, and advanced surface coating. Punches were designed for two industrial components using powder metallurgy (PM) steels—S600, S290, and S590, heat treated to 60–66 HRC, and coated via physical vapor deposition with TiN, AlTiN, and TiAlCN. Punch performance was analyzed against existing industry practices, and the strategy demonstrated improved productivity. Punch performance was determined based on the number of forgings produced before wear- and fatigue-induced failures. Significant improvements in punch performance were witnessed in both high-speed steel (HSS) and PM punches with optimized geometries. Fractographic investigations were carried out on fractured punches and analyzed, focusing on the coating’s effect on the thermal aspects of the punches. The proposed study will assist the cold-forging industry in determining appropriate variables to minimize forming responses, thereby enhancing tool life. The research also benefits industries by enhancing process robustness and improving process efficiency with respect to cost and time.

Strain hardening, elongation, and shearing speed effects of flow behaviors on coil-rod shearing during automatic multi-stage cold forging (AMSCF) are experimentally investigated. AMSCF machines and an experimental apparatus with a universal testing machine are utilized. Various coil materials are tested, including A6061-T6, SWCH10A, SCM435, and SCM415. The former is used to investigate the elongation and shearing speed effects on the sheared surface features. The latter is used to reveal the dependence of shearing phenomena on strain hardening and elongation. The new findings show the strong dependence of coil-rod shearing phenomena and surface features on flow behaviors and shearing speed. They will lead the engineers to the optimized shearing process design.

This paper studies the grain refinement mechanisms of 5A06 aluminum alloy sheets in cold rotary forging (CRF). The results show that the grains are clearly refined from 25.1 µm to 11.8 µm during the CRF process. The grain refinement mechanism can be divided into two modes: (1) The grains with a small Schmid factor (SF) are activated by multi-slip systems, and dense dislocations are segregated along the boundaries of interior regions with different slip systems, which results in a rapidly increasing strain localization along these boundaries. Since the strain localization restrains the coordinate slip deformation between different interior regions, the grains are directly separated into several finer grains. (2) The grains with a large SF are primarily activated by a single slip system, and the dislocation migrates smoothly along most microband boundaries. Then, a more severe lattice rotation causes a transformation to a hard orientation and multi-slip system activation, which contributes to an increase in the rapid misorientation across microband boundaries and thus promotes significant SF grain refinement.

Cold radial forging (CRF) is recognized as one of the most effective manufacturing processes for the production of hollow components. Nonetheless, even minor alterations in the alloy composition during the cold deformation processing phase can significantly influence the material’s manufacturability. This study addresses the challenges associated with high optimization costs, complex data acquisition, and the prediction of forming quality when integrating multiple processes, such as heat treatment and material composition. We propose the development of an intelligent predictive model aimed at forecasting the forming quality of the 20CrMnTiH alloy during CRF, utilizing a back-propagation neural network optimized by genetic algorithm (GA-BP). The assessment of forming quality is based on parameters such as damage, residual stress, and equivalent strain. The study investigates the impact of alloy composition and spheroidal annealing (SA) process parameters on forming quality. The performance of the three GA-BP prediction models is superior in terms of the coefficient of determination ( R 2 ) and mean square error (MSE), when compared to a conventional BP neural network and four other machine learning techniques, including gradient-boosted decision trees, random forests, support vector regression, and logistic regression. A comprehensive comparative analysis of the evaluation metrics across all three prediction models, alongside multi-objective optimization, indicates that the Pareto solution set generated by NSGA-II exhibits optimal distribution uniformity. The optimized process parameters (0.17%C-0.24%Si-0.81%Mn-0.03%P-0.03%S-1.15%Cr-0.05%Ni-0.06%Ti, T e : 60℃, AT i : 4.57 h) resulted in a reduction of residual stresses by 12%, equivalent strain by 15%, and component damage by 30%. The results demonstrate that the methodology proposed in this paper not only enhances the quality of CRF molding but also significantly improves the accuracy of the predictive model for forming quality.

Dense β-SiC coating with 3C-structure was utilized as a dry cold forging punch and core-die. Pure titanium T328H wires of industrial grade II were employed as a work material. No adhesion or galling of metallic titanium was detected on the contact interface between this β-SiC die and titanium work, even after this continuous forging process, up to a reduction in thickness by 70%. SEM (Scanning Electron Microscopy) and EDX (Electron Dispersive X-ray spectroscopy) were utilized to analyze this contact interface. A very thin titanium oxide layer was in situ formed in the radial direction from the center of the contact interface. Isolated carbon from β-SiC agglomerated and distributed in dots at the center of the initial contact interface. Raman spectroscopy was utilized, yielding the discovery that this carbon is unbound as a free carbon or not bound in SiC or TiC and that intermediate titanium oxides are formed with TiO2 as a tribofilm.

In this paper, non-isothermal analysis of an automatic multi-stage cold forging process of a ball-stud is conducted using a new material model which is a closed form function of strain, temperature and strain rate covering low and warm temperatures for high-strength stainless steel SUS304. An assembled die structural analysis scheme is employed for revealing the detailed die stresses, which is of great importance for process and die design for metal forming of the materials with high strengths. Die elastic deformation is dealt with to predict final geometries of material with higher accuracy. A complete analysis model is proposed to be used for optimal design of process and die designs in automatic multi-stage cold forging of high-strength materials.