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The impact of manufacturing strategies on the development of residual stresses in Dievar steel is presented. Two fabrication methods were investigated: conventional ingot casting and selective laser melting as an additive manufacturing process. Subsequently, plastic deformation in the form of hot rotary swaging at 900 °C was applied. Residual stresses were measured using neutron diffraction. Microstructural and phase analysis, precipitate characterization, and hardness measurement—carried out to complement the investigation—showed the microstructure improvement by rotary swaging. The study reveals that the manufacturing method has a significant effect on the distribution of residual stresses in the bars. The results showed that conventional ingot casting resulted in low levels of residual stresses (up to ±200 MPa), with an increase in hardness after rotary swaging from 172 HV1 to 613 HV1. SLM-manufactured bars developed tensile hoop and axial residual stresses in the vicinity of the surface and large compressive axial stresses (−600 MPa) in the core due to rapid cooling. The subsequent thermomechanical treatment via rotary swaging effectively reduced both the surface tensile (to approximately +200 MPa) and the core compressive residual stresses (to −300 MPa). Moreover, it resulted in a predominantly hydrostatic stress character and a reduction in von Mises stresses, offering relatively favorable residual stress characteristics and, therefore, a reduction in the risk of material failure. In addition to the significantly improved stress profile, rotary swaging contributed to a fine grain (3–5 µm instead of 10–15 µm for the conventional sample) and increased the hardness of the SLM samples from 560 HV1 to 606 HV1. These insights confirm the utility of rotary swaging as a post-processing technique that not only reduces residual stresses but also improves the microstructural and mechanical properties of additively manufactured components.

In this research, samples of the H13 steel, a commonly used hot work tool steel in the die/mould manufacturing industry, were additively manufactured using selective laser melting (SLM). Their as-built microstructures were characterised in detail using transmission electron microscopy (TEM) and compared with that of the conventionally manufactured H13 (as-supplied). SLM resulted in the formation of martensite and also its partial decomposition into fine α-Fe and Fe 3 C precipitates along with retained austenite. TEM analyses further revealed that the lattice of the resulting α-Fe phase is slightly distorted due to enhanced Cr, Mo and V contents. Substantially high residual stresses in the range of 940–1420 MPa were detected in the as-built H13 samples compared with its yield strength of ~1650 MPa. In addition, it was identified that the high residual stress existed from just about two additive layers (100 µm) above the substrate along the build direction. The high residual stresses were mainly attributed to the martensitic transformation that occurred during SLM. The research findings of this study suggest that the substantially high residual stresses can be easily problematic in the AM of intricate H13 dies or moulds by SLM.

The article presents the classification of the wear mechanisms of forging tools. The durability of dies can be enhanced through a variety of methods, including the selection of appropriate hot working tool steel, the application of effective heat treatment, the utilization of advanced surface engineering techniques, and the incorporation of lubricating and cooling agents. Two popular methods of tool regeneration, such as re-profiling and laser regeneration, are presented. The issue of numerical wear prediction based on the Archard model, the correlation of this model with experimental results, low-cycle fatigue (HTLCF), and an alternative method based on artificial neural networks are discussed. The paper aims to present currently known wear mechanisms and the methods of increasing and predicting tool durability.

Among various processes for manufacturing complex-shaped metal parts, additive manufacturing is highlighted as a process capable of reducing the wastage of materials without requiring a post-process, such as machining and finishing. In particular, it is a suitable new manufacturing technology for producing AISI H13 tool steel for hot-worked molds with complex cooling channels. In this study, we manufactured AISI H13 tool steel using the laser power bed fusion (LPBF) process and investigated the effects of tempering temperature and holding time on its microstructure and mechanical properties. The mechanical properties of the sub-grain cell microstructure of the AISI H13 tool steel manufactured using the LPBF process were superior to that of the H13 tool steel manufactured using the conventional method. These sub-grain cells decomposed and disappeared during the austenitizing process; however, the mechanical properties could be restored at a tempering temperature of 500 °C or higher owing to the secondary hardening and distribution of carbides. Furthermore, the mechanical properties deteriorated because of the decomposition of the martensite phase and the accumulation and coarsening of carbides when over-tempering occurred at 500 °C for 5 h and 550 °C for 3 h.

This paper examines the challenges of machining structural alloy steels for carburizing, with a particular focus on gear manufacturing. TiN[sub.0.85]-Ti coatings were applied to cutting tool blades to improve machining quality and tool life. The research, supported by mathematical modeling, demonstrated that these coatings significantly reduce adhesive wear and improve blade life. The Kolmogorov–Arnold Network (KAN) was identified as the most effective model comprehensively describing tool life as a function of cutting speed, coating thickness, and feed rate. The results indicate that gear production efficiency can be significantly increased using TiN[sub.0.85]-Ti coatings.

Additive manufacturing (AM) of hot-work tool steels such as H13 offers unique opportunities for producing complex, conformally cooled tools with reduced production time and material waste. In this study, five metal AM technologies—Fused Deposition Modeling and Sintering (FDMS, Desktop Metal Studio System and Zetamix), Binder Jetting (BJ), Laser Powder Bed Fusion (LPBF), and Directed Energy Deposition (DED)—were compared in terms of microstructure, porosity, and post-processing heat treatment response. The as-printed microstructures revealed distinct differences among the technologies: FDMS and BJ exhibited high porosity (6–9%), whereas LPBF and DED achieved near-full densification (<0.1%). Samples with sufficiently low porosity (BJ, LPBF, DED) were subjected to tempering and quenching treatments to evaluate hardness evolution and microstructural transformations. The satisfactory post-treatment hardness was observed in both tempered and quenched and tempered BJ samples, associated with secondary carbide precipitation, while LPBF and DED samples retained stable martensitic structures with hardness around 600 HV0.5. Microstructural analyses confirmed the dependence of phase morphology and carbide distribution on the thermal history intrinsic to each AM process. The study demonstrates that while FDMS and BJ are more accessible and cost-effective for low-density prototypes, LPBF and DED offer superior density and mechanical integrity suitable for functional tooling applications.

Due to a good combination of high hardness, wear resistance, toughness, resistance to high operating temperatures, and fairly low material cost, AISI H13 tool steel is commonly used in the manufacture of injection molds. Additive manufacturing (AM) such as selective laser melting (SLM), due to the layer-wise nature of the process, offers substantial geometric design freedom in comparison with conventional subtractive manufacturing methods, thereby enabling a construction of complex near-net shape parts with internal cavities like conformal cooling channels. The quality of SLM-manufactured parts mainly depends on the part geometry, build orientation and scanning strategy, and processing parameters. In this study, samples of H13 tool steel with a size of 10 × 10 × 15 mm3 were SLM-manufactured using a laser power of 100, 200, and 300 W; scanning speed of 200, 400, 600, 800, 1000, and 1200 mm/s; and hatch spacing of 80 and 120 µm. A constant layer thickness of 40 µm, 67° scanning rotation between subsequent layers, and a stripe scanning strategy were maintained during the process. The samples were built considering a preheating of 200 °C. The relative density, surface roughness, crack formation, microstructure, and hardness were evaluated. The relative density is shown to increase with increasing the volumetric energy density up to a value of about 60 J/mm3 and then no significant increase can be pointed out; the maximum relative density of 99.7% was obtained. A preheating of 200 °C generally aids to increase the relative density and eliminate the crack formation. The microstructure of built samples shows fine equiaxed cellular-dendritic structure with martensite and some retained austenite. The microhardness of the as-built samples was found to vary from 650 to 689 HV 0.2, which is comparable to a conventionally produced H13 tool steel.

This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical specimens were produced for training and testing machine learning (ML) models using variable process parameters: laser power (250–350 W), scanning speed (1050–1300 mm/s), and hatch spacing (65–90 μm). Eight ML models were investigated: 1. Support Vector Regression (SVR), 2. Kernel Ridge Regression (KRR), 3. Stochastic Gradient Descent Regressor, 4. Random Forest Regressor (RFR), 5. Extreme Gradient Boosting (XGBoost), 6. Extreme Gradient Boosting with limited depth (XGBoost LD), 7. Extra Trees Regressor (ETR) and 8. Light Gradient Boosting Machine (LightGBM). All models were trained using the Fast Library for Automated Machine Learning & Tuning (FLAML) framework to predict the relative density of the fabricated samples. Among these, the XGBoost model achieved the highest predictive accuracy, with a coefficient of determination R2=0.977, mean absolute percentage error MAPE = 0.002, and mean absolute error MAE = 0.017. Experimental validation was conducted on 27 newly fabricated samples using ML predicted process parameters. Relative densities exceeding 99.6% of the theoretical value (7.76 g/cm3) for all models except XGBoost LD and KRR. The lowest MAE = 0.004 and the smallest difference between the ML-predicted and PBF-LB validated density were obtained for samples made with LightGBM-predicted parameters. Those samples exhibited a hardness of 604 ± 13 HV0.5, which increased to approximately 630 HV0.5 after tempering at 550 °C. The LightGBM optimised parameters were further applied to fabricate a part of a forging die incorporating internal through-cooling channels, demonstrating the efficacy of machine learning-guided optimisation in achieving dense, defect-free H13 components suitable for industrial applications.

Samples of H13 tool steel were produced using the LENS® laser additive manufacturing technique. Three variants of samples were produced such that during and 2 h after deposition, both the substrate and sample temperatures were maintained at 80, 180, and 350 °C. After the samples were produced, the effect of the substrate temperature on their metallurgical quality, microstructure, and mechanical properties was determined. No segregation of alloying elements was observed. The test results indicate that, depending on the temperature used, the structure of the H13 alloy is martensitic or martensitic-bainitic with a slight residual austenite content of up to 2.1%. Owing to structural changes, the obtained alloy is characterized by lower impact strength compared with conventionally produced alloys and high brittleness, particularly when using an annealing temperature of 350 °C. Isothermal annealing above the martensite start temperature results in extreme brittleness due to a partial structural transformation of martensite into bainite and probable carbide precipitation processes at the nanoscale.

This study refers to an analysis of the dies used in the first operation of producing a valve forging from chromium-nickel steel NC3015. The analyzed process of manufacturing forgings of exhaust valves is realized in the co-extrusion technology, followed by forging in closed dies. This type of technology is difficult to master, mainly due to the increased adhesion of the charge material to the tool substrate as well as the complex conditions of the tools’ operations, which are caused by the cyclic thermo-mechanical loads and also the hard tribological conditions. The average durability of tools made from tool steel WLV (1.2365), subjected to thermal treatment and nitriding, equals about 1000 forgings. In order to perform an in-depth analysis, a complex analysis of the presently realized technology was conducted in combination with multi-variant numerical simulations. The obtained results showed numerous cracks on the tools, especially in the cross-section reduction area, as well as sticking of the forging material, which, with insufficient control of the tribological conditions, can cause premature wear of the dies. In order to increase the durability of forging dies, alternative materials made of hot work tool steels were used: QRO90 Supreme, W360, and Unimax. The preliminary tests showed that the best results were obtained for QRO90, as the average durability for the tools made of this steel equaled about 1200 forgings (with an increase in both the minimal and maximal values), with reference to the 1000 forgings for the material applied so far.