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Properties of fragmentation from an explosively driven 316L stainless steel spherical shell section fabricated by a laser powder bed additive manufacturing process with minimal surface finishing are investigated. This shell is driven by an insensitive high explosive, resulting in high strain rate deformation (>8 × 103 s−1) and failure of the stainless steel. Photonic Doppler velocimetry measures the expansion rate; dynamic radiography and high-speed imaging capture the fracture behavior of the stainless steel. The fracture response of the additively manufactured stainless steel shell is compared to published experimental results on additively manufactured 316L stainless steel and conventionally manufactured wrought 316L and 304 stainless steel shell fragmentation. Despite preferred crack orientation, suggesting the influence of surface grooves on fracture time, fragment size is identical to that measured in a similar experiment on wrought 304 stainless steel. Further analysis indicates that the 316L additively manufactured stainless steel shell exhibits comparable spall strength and fragmentation toughness to conventionally manufactured stainless steel yet lower failure strain due to surface stress concentrations. [ABSTRACT FROM AUTHOR]

To develop more sustainable purification and recovery processes, it is critical to accurately determine inorganic impurities of natural graphite (NG) and valuable elements present in low concentrations in steelmaking slag. This study applied three microwave-assisted acid digestion methods for basic oxygen furnace (BOF) slag, NG, and NG ash. Nitric acid (HNO3), hydrochloric acid (HCl), and hydrogen fluoride (HF) were used in method 1. In method 2, method 1 was followed by microwave-assisted boric acid (H3BO3) digestion. In method 3, acids (HNO3, HCl, HF, and H3BO3) were added simultaneously. Concentrations were measured using inductively coupled plasma–optical emission spectrometry. Analytical performance was evaluated using spiking recovery tests (NG) and certified reference (slag) material (CRM). Spiking recoveries for Al, Ca, Fe, K, Mg, Na, and Si were between 5% and 116% (method 1) and 94%–116% (methods 2 and 3). CRM recoveries for Al, Ca, Fe, Mg, Mn, P, Si, Ti, and V were between 2% and 102% (method 1) and 92%–103% (methods 2 and 3). The unsuitability of method 1 was evident due to the low recoveries. Methods 2 and 3 digested Ca- and Si-containing BOF slag completely without visible residue. The sufficient recoveries verified the adequacy of the methods. The suitability of method 3 probably depended on the phases of samples, as quartz was not completely digested from the NG. Method 2 completely digested the inorganic material of NG, and the spiking test showed no loss of volatile silicon compounds. These methods have not been published for NG or NG ash and validated for slag before. [ABSTRACT FROM AUTHOR]

In order to study the anisotropy mechanism and the effect of current mode on the anisotropy, the arc additive manufacturing experiment of 316L stainless steel was completed by using direct current and pulse current, respectively. The results of microstructure analysis show that each layer of the additive wall can be divided in the longitudinal direction into a heat-affected zone, a central zone and a remelting zone. The grain growth direction of each zone was basically the same, and the microstructure, alloying element content and hardness of the three zones were obviously different. The crystallographic texture and inhomogeneity of the layer were the main reasons for the anisotropy. Compared with the use of direct current, the difference in tensile strength between horizontal and vertical specimens was reduced by 23.5% when using pulse current. Additionally, the wall has high forming accuracy, small grain size and good plasticity. And the δ-ferrite content inside the layer is lower, the hardness of each zone is higher, and the hardness difference between the three zones is smaller. [ABSTRACT FROM AUTHOR]

Commercially available 316L (1.4404) stainless steel is commonly used for industrial filtration due to its combination of good material properties, particularly its corrosion resistance, which is a critical factor for filters in corrosive (e.g., saltwater) environments. Recently, laser powder bed fusion (LPBF) has enabled new more complex and efficient filtration pieces to be manufactured from this material. However, it is critical to know how the corrosion resistance is affected by this manufacturing strategy. Here, the corrosion resistance of LPBF manufactured 316L stainless steel is compared with wrought 316L sheet. The corrosion of the samples in saltwater was assessed with asymmetric electrochemical noise, potentiodynamic polarisation curve, and electrochemical impedance spectroscopy. The samples before and after corrosion were examined with scanning electron microscopy and energy-dispersive spectroscopy. The LPBF samples had higher corrosion resistance than the sheet samples and were more noble. The corrosion resistance of the LPBF sample increased with time, while the wrought sample corrosion resistance reduced over time. The corrosion mechanism of both samples was stable with time, formed of a passive film process and a bared material process. This paper presents the first study about the temporal evolution of the LPBF 316L stainless steel corrosion mechanism. [ABSTRACT FROM AUTHOR]

To utilize the sensible heat of hot roasted iron ore pellets with no CO2 emission in the production of metallized pellets for direct steelmaking, the pellets were reduced in H2 during their cooling process with variable cooling rates. When the cooling rate decreased from 5.2 °C/min to 2.0 °C/min, the total iron content, reduction degree, and iron metallization degree of the pellets increased continuously from 74.0 wt%, 52%, and 31.1% to 84.9 wt%, 93.4%, and 89.2%, respectively. However, the compressive strength of the pellets increased initially from 2100 N/p to 2436 N/p and then decreased considerably to 841 N/p. As the cooling rate decreased, more Fe2O3 was reduced to Fe with diminishing FeO and Fe2SiO4. The porosity of the pellets increased from 23.9% to 54.3%, with higher distribution uniformity of pores. The morphology of metallic iron particles also transited from a layered form to a spherical form and lastly to a porous reticular form. Meanwhile, the metallic iron particles in the pellets grew evidently with more uniform distributions. When the cooling rate was 3.7 °C/min, the resulting metallized pellets had the reduction degree of 74.2%, iron metallization degree of 66.9%, and the highest compressive strength of 2436 N/p, in association with the spherical morphology and relatively large size of metallic iron particles. [ABSTRACT FROM AUTHOR]

The present work describes the effect of long-term (8 weeks) high-temperature oxidation (500 °C) on the formation of an oxide layer as well as on the microstructure and mechanical properties of the 3D-printed 18Ni-300 maraging steel. For this purpose, samples produced by additive manufacturing in the as-built and the as-built + solution annealed and aging treated states were used. The as-built + solution annealed and aging treated material was found to be more prone to oxide layer formation due to a homogeneously distributed Ni3Mo intermetallic phase in the material matrix compared to the as-built material. The 8 weeks long exposure to a temperature of 500 °C has caused the formation of a thick oxide layer that exhibited a very bad adhesion with the metal matrix/oxide. The X-ray diffraction analysis confirmed the formation of a layer with a complex phase composition: martensite, austenite, Fe2O3, and Fe3O4. Moreover, the presence of CoFe2O4 was determined on the thin outer oxide layer using X-ray photoelectron spectroscopy. The phenomenon of over-aging was found to be the most significant after the first week of high-temperature oxidation. Then, a negligible change in the microhardness was observed throughout the entire experiment. X-ray diffraction analysis and energy dispersive spectroscopy confirmed the phase composition of the alloy corresponding to 75% of martensite + 25% of austenite as well as the change of Ni3Mo precipitate to Ni3(Mo, Ti) type after the long-term oxidation. [ABSTRACT FROM AUTHOR]

This paper focuses on the operation and results of the gas treatment pilot plant for the purification and conditioning of steel mill gases, a key element of the Carbon2Chem® Technical Center in Duisburg, Germany. Using highly sensitive analytics, it is demonstrated that steel mill gas impurities which are detrimental to downstream catalytic conversion are reduced to single‐digit ppb levels. Strategies for adapting the gas purification process to take account of the transformation of steelmaking towards direct reduction are also discussed. [ABSTRACT FROM AUTHOR]

This study proposed a strategy to prepare metalized pellets for direct steelmaking by hydrogen cooling reduction (HCR) of iron ore pellets with a focus on the effect of H2 flow rate on the process. It was demonstrated that increasing H2 flow rate could effectively enhance the reduction performance of iron ore pellets. However, due to the influence of the countercurrent diffusion resistance of gas molecules, too high H2 flow rate no longer promoted the reduction of the pellets when the maximum reduction rate was reached. The reduction swelling index (RSI) of the pellets initially increased and then decreased with increasing H2 flow rate. This change was associated with the decreased content of Fe2SiO4 in the metalized pellets and the changes in porosity and iron particle size. The compressive strength (CS) decreased continuously, showing a sharp decline when the H2 flow rate reached 0.6 L/min. It was attributed to the significant increases in porosity and average pore size of the metalized pellets, with the presence of surface cracks. When the H2 flow rate was 0.8 L/min, the metalized pellets had the optimal performance, namely, reduction degree of 91.45%, metallization degree of 84.07%, total iron content of 80.67 wt%, RSI of 4.66%, and CS of 1265 N/p. The findings demonstrated the importance of controlling the H2 flow rate in the preparation of metallized pellets by HCR. [ABSTRACT FROM AUTHOR]

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The thermal conductivity of components manufactured using Laser Powder Bed Fusion (LPBF), also called Selective Laser Melting (SLM), plays an important role in their processing. Not only does a reduced thermal conductivity cause residual stresses during the process, but it also makes subsequent processes such as the welding of LPBF components more difficult. This article uses 316L stainless steel samples to investigate whether and to what extent the thermal conductivity of specimens can be influenced by different LPBF parameters. To this end, samples are set up using different parameters, orientations, and powder conditions and measured by a heat flow meter using stationary analysis. The heat flow meter set-up used in this study achieves good reproducibility and high measurement accuracy, so that comparative measurements between the various LPBF influencing factors to be tested are possible. In summary, the series of measurements show that the residual porosity of the components has the greatest influence on conductivity. The degradation of the powder due to increased recycling also appears to be detectable. The build-up direction shows no detectable effect in the measurement series. [ABSTRACT FROM AUTHOR]

Accurate prediction of endpoint carbon at the dynamic control stage in the converter is crucial for achieving smelting targets. Currently, there are two main methods for converter endpoint prediction: the data-driven method and the mechanism-based method. Data-driven methods exhibit high accuracy but are vulnerable to data quality variations and lack interpretability. Mechanism-based methods provide great interpretability but face challenges in precisely identifying key parameters in the mechanism formula. Inspired by the design concept of physics-informed neural networks (PINNs), an integrated data-driven and mechanism-based method for endpoint carbon prediction in BOF (dmPINNs, data-driven and mechanism-based physics-informed neural networks) is proposed, which has four parts: feature extraction, mechanism-based calculation, data-driven prediction, and integrated prediction. We identify key parameters of the mechanism formula through the neural network to obtain the specified formula for each heat and supervise the training process of the neural network through the mechanism formula to ensure interpretability. Experimental results show that, within the ±0.012% error range, the hit rate of endpoint carbon content using dmPINNs improved by 5.23% compared with the traditional data-driven method and has greater robustness with the supervision of the mechanism formula. [ABSTRACT FROM AUTHOR]

The recovery of iron contained in mill scale rather than iron ore can be considered a promising valorization pathway for this waste, especially if carried out through reduction using biogenic carbon sources. Nevertheless, the physicochemical properties of the latter may hinder the industrial transferability of such a pathway. In this work, the mechanical and metallurgical behavior of self-reduced briquettes composed of mill scale and four biogenic carbons (with increasing ratios of fixed carbon to volatile matter and ash) was studied. Each sample achieved mechanical performance above the benchmarks established for their application in metallurgical furnaces, although the presence of alkali compounds in the ash negatively affected the water resistance of the briquettes. In terms of metallurgical performance, although agglomeration successfully exploited the reduction by volatiles from 750 °C, full iron recovery and slag separation required an amount of fixed carbon higher than 6.93% and a heat treatment temperature of 1400 °C. Finally, the presence of Ca-, Al-, and Si- compounds in the ash was essential for the creation of a slag compatible with steelmaking processes and capable of retaining both phosphorus and sulfur, hence protecting the recovered iron. [ABSTRACT FROM AUTHOR]

This paper studies the integration of direct reduced iron (DRI) and hot briquetted iron (HBI) into the steelmaking process via an electric arc furnace (EAF). Considering a variety of DRI production techniques distinguished by different reactor types, this paper provides a comparative overview of the current state. It delves into significant challenges, such as the susceptibility of DRI to reoxidation and the necessity of thorough handling to maintain its quality. The effectiveness of several reoxidation mitigation strategies, including the application of thin oxide layers, briquetting, various coatings, and nitride formation in ammonia-based reduction processes, is evaluated. Most existing studies have primarily focused on the reoxidation of DRI rather than on HBI, despite the fact that HBI may undergo reoxidation. The importance of DRI/HBI in offering an alternative to the integrated steelmaking route is highlighted, focusing on how it changes the EAF process compared to those for melting scrap. This paper also identifies several research prospects for further DRI/HBI applications in steel production. [ABSTRACT FROM AUTHOR]

The feasibility of using argon‐atomized QT 17‐4+ stainless steel powder for directed energy deposition (DED) additive manufacturing is studied. The QT 17‐4+ steel is a novel martensitic steel designed based on the compositional modification of the standard 17‐4 precipitation‐hardened (PH) stainless steel. This modification aims to achieve better mechanical properties of as‐deposited components compared to the heat‐treated wrought 17‐4PH steel. In this study, QT 17‐4+ steel powder is used for DED, for the first time. The influence of laser power, laser scan speed, powder feed rate, and hatch overlap on the density is studied. The central composite design is used to determine the experimental matrix of these factors. The response surface methodology is used to obtain the empirical statistical prediction model. Both columnar and equiaxed parent austenite grain structures are observed. X‐ray diffraction analyses reveal a decrease in the percentage of retained austenite from 19% in the powder to 5% after DED. The microhardness of the DED processed sample in the as‐deposited state is slightly higher than that of wrought 17‐4PH steel either solution‐annealed or H900‐aged. A higher 0.2% yield strength, a lower ultimate tensile strength, and lower elongation are observed for the vertically printed test sample, when compared to the horizontal one. [ABSTRACT FROM AUTHOR]

The importance of emulsion zone formation in the basic oxygen steelmaking (BOF) process lies in its crucial role in enhancing refining efficiency. This study provides a comprehensive theoretical analysis of refining in BOF steelmaking, empowering steelmakers to optimize emulsion zone formation. By examining the contributions of decarburization, desiliconization, and dephosphorization from the emulsion zone, the study systematically investigates refining phenomena in distinct sections of the BOF converter, interconnected through recirculation streams. By utilizing FactSageTM and its macro-facilities, transient variations in metal and slag compositions, alongside varying terminal phosphorus levels for diverse emulsion zone dimensions, are quantified. Model findings highlight that emulsion and hotspot zones play roles in decarburization and desiliconization, while dephosphorization exclusively occurs within the emulsion zone. The model's projections for carbon, silicon, and phosphorus removals (wt pct) and metal bath temperature concur with data obtained from plant trials. This comprehensive analysis enhances our understanding of the BOF steelmaking process, enabling steelmakers to fabricate the required emulsion zone strategically for optimal refining efficiency. [ABSTRACT FROM AUTHOR]

The underutilization of steelmaking slags is primarily ascribed to instability, predominantly influenced by their intricate phase structure. The presence of MgO, a key constituent in steelmaking slag, contributes to the challenges faced in steelmaking slag recycling and sustainable steel production practices. There are currently uncertainties surrounding the impact of fluctuations in MgO content on the mineral phase of steelmaking slag, as well as ongoing controversy regarding the influence of the RO phase on slag stability. This study investigates the phase evolution and microstructures of steelmaking slag with varying MgO contents during the cooling process through high-temperature experiments and FactSage thermodynamic equilibrium calculations. The principal phases in steelmaking slag, namely α′-Ca2SiO4 (C2S), RO (MgO–FeO solid solution), Ca2Fe2O5 (C2F) and Ca3SiO5 (C3S), were identified using XRD and SEM-EDS analyses. As the MgO content increases, the MgO content within the RO continued to increase. With a MgO content of 10 mass pct in steelmaking slag, the precipitation of C3S commences during the cooling of the steelmaking slag. The average size of C3S reaches up to 400 μm, markedly higher than the 35 μm size of C2S. This disparity is attributed to the higher nucleation rate of C2S compared to C3S, coupled with a lower growth rate. The augmentation of MgO content in steelmaking slag induces changes in the C3S formation, facilitated by the substitution of Ca2+ in C3S by Fe2+ and Mg2+.The significance of this work lies in unraveling the intricacies of steelmaking slag behavior, providing crucial insights for optimizing recycling processes. This research sheds light on the factors that influence the phase composition and stability of steelmaking slag, contributing to the utilization of steelmaking slags, promoting environmental sustainability, and bolstering the overall efficiency of metallurgical processes in the current industrial landscape. [ABSTRACT FROM AUTHOR]

Vanadis 8 steel is a tool steel manufactured by powder metallurgic processing. Its main alloy elements are V, Cr and Mo. By implementing an experimental design with five factors—all of them are related to the thermal processing of this steel and with ionic nitriding—the effects of said factors on adhesive wear resistance and corrosion resistance were studied. For this purpose, Pin-on-Disc wear tests and lineal polarization resistance tests were carried out using an aqueous solution with 3.5% NaCl by weight. The main aim was to increase this steel use in more aggressive environmental conditions, such as in coastal environments. By means of XRD, the percentage of retained austenite was determined, and by SEM-EDX, the microstructure was revealed. The conclusion is that adhesive wear resistance is improved if thermal processing parameters are at such levels that increase austenite destabilization and reduce retained austenite content. This means to destabilize austenite at 1180 °C during 1 h, with oil quenching, tempering at 520 °C during 2 h and ionic nitriding at 520 °C during 2 h. Corrosion resistance is highly improved with ionic nitriding. At the same time, to compensate for the negative effect on corrosion resistance of a high density of primary and secondary carbides, it is essential to carry out the ionic nitriding treatment. The harmful effect of electrochemical microcells that appear in the carbide/matrix interface is compensated by the passivating effect generated by the nitrided surface. [ABSTRACT FROM AUTHOR]

The slag generated as a by-product of the steelmaking process can be used to manufacture cement, reducing the generated waste and contributing to the circular economy. Currently, steelmaking companies promote long-term bilateral deals with one or few cement companies where the price is fixed, and the slag is a treated as commodity. We propose a new solution, which promotes slag reuse through its differentiation with a composition-based grouping and an auction. This process is carried out in a blockchain network, which increases trust in the system, provides guarantees about the slag composition to cement companies and helps external regulators to reliably extract circularity indicators. [ABSTRACT FROM AUTHOR]

The unusual combination of the fundamentally contradictory properties of high tensile strength and high fracture toughness found in maraging steel makes it well suited for safety-critical applications that require high strength-to-weight materials. In certain instances, additive manufacturing (AM) has produced materials that may be desirable for safety-critical applications where impact toughness is a key property, such as structural parts for the aerospace industry or armor plates for military applications. Understanding the influence of process parameters and defect structure on the properties of maraging steel parts produced via laser powder bed fusion (LPBF) is a fundamental step towards the broader use of AM technologies for more demanding applications. In this research, the impact energy of V-notched specimens made of 1.2709 maraging steel produced by LPBF was determined via Charpy impact testing. Specimens were produced using different processing parameter sets. By combining the process parameters with the porosity values of the parts, we demonstrate that an almost full prediction of the impact properties can be achieved, paving the way for significantly reducing the expenses of destructive testing. [ABSTRACT FROM AUTHOR]

The objective of basic oxygen furnace (BOF) steelmaking is to achieve molten steel with final carbon content, temperature, and phosphorus content meeting the requirements. Accurate prediction of the above properties is crucial for end-point control in BOF steelmaking. Traditional prediction models typically use multi-variable input and single-variable output approaches, neglecting the coupling relationships between different property indicators, making it difficult to predict multiple outputs simultaneously. Consequently, a multi-output prediction model based on the fusion of deep convolution and attention mechanism networks (FDCAN) is proposed. The model inputs include scalar data, such as the properties of raw materials and target molten steel, and time series data, such as lance height, oxygen supply intensity, and bottom air supply intensity during the blowing process. The FDCAN model utilizes a fully connected module to extract nonlinear features from scalar data and a deep convolution module to process time series data, capturing high-dimensional feature representations. The attention mechanism then assigns greater weight to significant features. Finally, multiple multi-layer perceptron modules predict the outputs—final carbon content, temperature, and phosphorus content. This structure allows FDCAN to learn complex relationships within the input data and between input and output variables. The effectiveness of the FDCAN model is validated using actual BOF steelmaking data, achieving hit rates of 95.14% for final carbon content within ±0.015 wt%, 84.72% for final temperature within ±15 °C, and 88.89% for final phosphorus content within ±0.005 wt%. [ABSTRACT FROM AUTHOR]

The dephosphorization behavior of multiphase steel slag in converter steelmaking process is very important for higher steel quality with ultra-low phosphorus content. In this paper, the multiphase steel slag with different BaO content reacted with hot metal at 1873 K temperature. The influence of the reaction on dephosphorization was investigated. The mineralogical phase was observed. The phosphorus partition between phosphorus enriched phase and liquid phase was discussed. The phosphorus partition ratio between steel slag and hot metal was obtained. The main mineralogical phases of steel slag were phosphorus enriched phase mCa2SiO4-Ba1-xCax(PO4)2 and RO (FeO/MgO) phase. The barium existed in phosphorus enriched phase and liquid phase, not in RO phase. The phosphorus content in phosphorus enriched phase increased from 1.12% to 1.36% with the addition of BaO. The phosphorus partition ratio between phosphorus enriched phase and liquid phase increased from 2.60 to 3.77, increased 45.00% by the addition of BaO. The phosphorus partition ratio between steel slag and hot metal increased from 55.35 to 97.50, increased 76.15% by the addition of BaO in steel slag. [ABSTRACT FROM AUTHOR]

The current investigation involves the fabrication of fifteen samples of austenitic stainless steel 316 L (SS316L) utilizing laser powder bed fusion technology to evaluate the residual stresses through the impact of energy density, which was varied between 30 J/mm3 and 111 J/mm3 by varying process parameters, notably scanning speed and laser power. The energy density range suggested by existing literature has been found to have samples with significant mechanical properties, apart from its impact on residual stress, which can affect the service life of the product. The primary focus of this study is to identify the optimal window for achieving residual stress at various locations within the sample. The study discovered differences in residual stress development at all tested locations. When employing lower laser powers of 140 W and 160 W, residual stress formation remained consistent. Furthermore, when the laser power exceeds 140 W to 160 W, a substantial temperature gradient develops, leading to a considerable rise in tensile stress magnitude. The variation in energy density does not affect residual stress, while there is a variation in two process parameters, laser power and scanning speed, simultaneously. A laser power range of 140 W to 160 W, combined with scanning rates ranging from 500 mm/s to 1100 mm/s, results in a consistent range of minimal residual stress formation across multiple locations. The key outcomes of the study show the crucial role of laser power and scanning speed in residual stress development, which has implications for improving mechanical characteristics and product service life by generating minimal residual stresses. [ABSTRACT FROM AUTHOR]

Power hammers are mechanised forging devices that constitute a pivotal part of steel manufacturing. Power hammer‐associated injuries are a rare occurrence. We report a noteworthy case of a 52‐year‐old man who sustained a high‐energy penetrating injury while working with a power (counterblow) hammer. The man used a sizable disc‐shaped metallic object to dislodge the forging wedged in the machine by applying the force of the striking ram on it. On impact, the object ejected and struck the man in the right lateral portion of the chest. The autopsy disclosed extensive damage to the thoracic and abdominal organs. The cause of death was opined to be exsanguination due to penetrating trauma of the heart and transection of the descending aorta. The investigation confirmed a breach of safety regulations. To the best of our knowledge, this is the first power (counterblow) hammer‐related fatality in medico‐legal literature. [ABSTRACT FROM AUTHOR]

The main objective of this work was to propose and evaluate a methodology for shielding-gas selection in additive manufacturing assisted by wire arc additive manufacturing (WAAM) with an austenitic stainless steel as feedstock. To validate the proposed methodology, the impact of multi-component gases was valued using three different Ar-based blends recommended as shielding gas for GMA (gas metal arc) of the target material, using CMT (cold metal transfer) as the process version. This assessment considered features that potentially affect the building of the case study of thin walls, such as metal transfer regularity, deposition time, and geometrical and metallurgical characteristics. Different settings of wire-feed speeds were conceived to maintain a similar mean current (first constraint for comparison's sake) among the three gas blends. This approach implied different mean wire-feed speeds and simultaneously forced a change in the deposition speed to maintain the same amount of material deposited per unit of length (second comparison constraint). The composition of the gases affects the operational performance of the shielding gases. It was concluded that by following this methodology, shielding-gas selection decision-making is possible based on the perceived characteristics of the different commercial blends. [ABSTRACT FROM AUTHOR]

The article addresses one of the most significant topics in the field of weapons science, i.e., the historical Damascus blades of the Indo-Persian region. By combining previously known typological and characteristic features of oriental Damascus blades with variations in the charge and fluxes of crucible melting, observed in the historical period from the 16th to the 19th centuries, a pattern regarding the presence or absence of specific process impurities in patterned steels was established. These steels were found exclusively in the Indo-Persian region. In terms of chemical composition, not all crucible steels with a pattern on the surface of the blade can be considered Damascus when compared with the finest examples of Oriental weapons. The chemical characteristics of Damascus blades in terms of the quantitative content of process impurities are described in detail and confirmed with specific examples. New data is published on the chemical composition of 20 pieces of Damascus steel weapons dated to the 17th–19th centuries, including a shamshir with the marks of Asad (Asadullah) Isfahan. The study presents various types of damascene patterns from private collections of K. Khaydakov and D. Sukhanov, with the appropriate chemical composition. It was concluded that, based on the chemical composition, macro-, and microstructure, Damascus crucible steels of the Indo-Persian region should be classified as a distinct group of historical blade steels manufactured prior to the decline of local technologies and the influence of European metallurgical production in the Indo-Persian region. [ABSTRACT FROM AUTHOR]

Approximately 10–15% of the world's steel production is derived from small industries. While ladle furnaces contribute to maintaining a generally good quality of refined steel, new challenges arise, especially when striving for very low sulfur percentages in steel. To address this, the study explores the impact of process parameters such as aluminum, silicon, and basicity in reducing sulfur content. Three interconnected equilibrium/adiabatic stoichiometric-reactor-based approaches describe the overall ladle steel-making process. The macro-programming facility of FactSage™ software was used to determine the refining behaviors of the ladle steel-making processes. The model's predicted endpoint, manganese, sulfur, oxygen, and aluminum content, agreed with plant data. This efficient model is a valuable guiding tool for ladle steelmaking industries with a 10–15 ton capacity. [ABSTRACT FROM AUTHOR]

The bottom blowing element is critical for ensuring the effectiveness of top and bottom blowing in converter steelmaking process. Investigating the influence of different bottom blowing elements on the stirring properties of molten bath contributes to the optimization of the bottom blowing system. The effects of structural variations in dispersive, circular seam and straight cylinder types of bottom blowing elements on molten bath fluid dynamics, turbulent kinetic energy and multiphase flow properties of gas-slag-metal were investigated through numerical simulations. In addition, physical simulations were used to measure the mixing time of molten bath, observe changes in the flow field, and validate and analyze the results of the numerical simulations. The results show that the dispersive type element has a wider dispersion range of the flow jets, while the straight cylinder type has the smallest dispersion range. When the bottom blowing intensity is below 0.05 Nm3/t·min, the dispersive type has the longest mixing time, while the circular seam type has the shortest mixing time. Conversely, at more than 0.08 Nm3/t·min, the dispersive type shows the shortest mixing time and the straight cylinder type shows the longest. The dispersive type significantly influences the bottom flow field and disperses tracers from the interior of molten bath. The circular seam type mainly affects the middle flow field and directs tracers along the central area. The straight cylinder type, on the other hand, has a significant influence on the surface flow field and directs tracers along the pool surface. [ABSTRACT FROM AUTHOR]

Calcium (Ca) additions during secondary steelmaking are a well-adopted practice to transform solid oxide non-metallic inclusions (NMIs) into globular-shaped liquid oxides. The claimed hypothesis that liquid NMIs reduce SEN clogging has been proven in the past by researchers. However, the exact quantity of Ca needed to transform the physical state of NMIs during steelmaking remains uncertain. Operators in the steel plant use a consistent quantity of Ca additions for specific steel grades, but this approach does not account for the varying physical states and evolving dynamics of NMIs characteristics in each 'heat'. To overcome this, a study was conducted to explore the impact of varying Ca additions on the transformation and behavior of NMIs in low-alloyed Ca-treated steel grades. The aim was to establish a more reliable and responsive approach to Ca treatment, potentially leading to more effective control in preventing submerged entry nozzle (SEN) clogging. The proposed methodology involved online monitoring of NMIs state coupled with controlled variations in Ca addition, deviating from fixed quantity, to observe its effects on NMIs state transformations. Through careful analysis of collected data and the implementation of a data-driven optimizer, this study reports the practical implications of using optimal amounts of Ca during secondary steelmaking. The resulting change due to dynamic calcium silicide (CaSi)-cored wire additions and their impact on SEN clogging were evaluated. The findings reveal the significant role of optimal CaSi wire additions, leading to improved steel castability and a notable 30 pct reduction in SEN clogging tendencies. The results obtained after the implementation of the data-driven optimizer 'ClogCalc' have significant implications for steel manufacturers, offering new insights into enhancing Ca treatment efficiency. [ABSTRACT FROM AUTHOR]

Thin slab casting and rolling (TSCR) is a near-net-shape manufacturing process and a key development technology in China's iron and steel industry. This study uses cross-scale calculations to analyze the complete process of thin slab casting. The focus is on simulating and predicting the final solidification structure by adjusting process parameters. The aim is to enable further investigation into material performance and establish a foundation for researching deformation and phase transformation. To achieve this, a coupled model has been developed to simulate the entire thin slab casting process, using hot stamping steel as the research subject. The model encompasses fluid flow, heat transfer, and solidification. The study identifies the optimal combination for flow field, temperature distribution, and equiaxed grain ratio within the specified parameter range at a casting speed of 4.0 m/min and a superheat of 40 °C. The aim of the study is to establish an integrated computational materials engineering (ICME) research system for near-net-shape automotive steel casting processes. [ABSTRACT FROM AUTHOR]

Precise selective cooling control of work roll can significantly improve the cold rolled strip flatness in steel manufacturing industry. To improve the control accuracy of the coolant output of selective work roll cooling control system, a machine learning (ML) algorithm with differential evolution-gray wolf algorithm optimization support vector machine regression (DE-GWO-SVR) model has been proposed for the first time in this study. This model combines the differential evolution (DE) with grey wolf optimization algorithm (GWO) to improve the optimization performance of the algorithm. Then, the SVR model parameters are optimized with differential evolutionary gray wolf hybrid algorithm (DE-GWO) to improve the regression accuracy. Finally, the influences of data normalization methods and the selection of SVR kernel functions were systematically investigated. Compared with the test results of other regression models, the evaluation index R2 based on the DE-GWO-SVR model is greater and the RMSE, MAE, and MAPE are smaller. The DE-GWO-SVR model performs the best, with a higher regression accuracy than the other regression models. Besides, it has been successfully applied to a 1450 mm five-stand industrial cold rolling mill. The model has higher control accuracy for the thermal crown of the work roll and better control effect for the flatness deviation of the strip steel. This study provides a novel strategy with a help of ML algorithm to effectively improve the flatness quality of cold rolled strips by optimizing the selective cooling control of work roll, which exhibits a great practical application potential in steel manufacturing. [ABSTRACT FROM AUTHOR]

We consider an integrated batching problem for steel-making and continuous casting process of steel plate manufacturing. For steel-making, slab batching problem is defined to make charges from slabs, and for continuous casting, charge batching problem is defined to make casts from charges. Our batching problem is regarded as a combination of slab batching and charge batching problems. Since steel-making and continuous casting are processes that must be synchronised, two batching problems must be integratedly handled. However, because of their difficulties, most papers solved each batching problem sequentially. We deal with a batching problem under bi-strand casting environment where slabs are cast with two strands. We also consider practical aspects that were barely considered before such as slab length difference constraints between strands and casts having few charges. We propose two mixed integer programming models. The first one is to create frames to define the overall structure of the solution and the second one is to fill the created frames with slabs to concretise the solution. The proposed matheuristic algorithm solves the problem in an integrated manner and finds a near-optimal solution in a reasonable time. It outperforms the company's current practice, as shown in experiments with real and randomly generated data. [ABSTRACT FROM AUTHOR]

Deploying renewable generation to replace conventional fossil-fuel-based energy supplies provides an important pathway for the decarbonization of steelmaking plants. Meanwhile, it is also crucial to improve the flexibility of blast-furnace-gas-fired combined-cycle power plants (BFGCCs) to ease the accommodation of uncertain renewable generation. To this end, this paper proposes the deployment of a molten salt heat storage (MSHS) system in BFGCCs to store the heat of gas turbine flue gas so that the power–heat coupling of these BFGCCs can be unlocked to enhance the flexibility of the energy supply. A stochastic capacity optimization of an integrated BFGCC–MSHS–wind–solar (BMWS) energy system is presented to determine the optimal installed capacities of a BFG holder, MSHS, wind turbine, and PV panel, aiming to achieve an economic and safe energy supply for the entire system. Multiple scenarios considering uncertain fluctuations in load demands and renewable generation are generated with the Monte Carlo method based on a typical scenario. These scenarios are then reduced to representative scenarios using the synchronous substitution and reduction method for stochastic capacity optimization to enhance the reliability of the results. The case study results demonstrate that configuring MSHS reduces the total annualized cost of the BMWS system by 2.28%. Furthermore, considering the uncertainties of the power/heating load and wind/PV generation can reduce the expected annualized total cost of the BMWS system and the corresponding standard deviation by 5.66% and 81.45%, respectively. The BMWS system can achieve 730.68 tons of equivalent CO2 reduction in 24 h due to the successful utilization of renewable energy. This paper provides an effective approach for the decarbonization of energy generation systems in steelmaking plants. [ABSTRACT FROM AUTHOR]

The presented manuscript focuses on the influence of process parameters of abrasive water jet technology on the creation of non-transient erosive grooves. The processed stainless steel SS 316L is additively manufactured using the selective laser melting (SLM) method. Due to the distinct mechanical properties of this material resulting from the production process, the material was machined in two planes according to the direction of the printing layers. The experimental part employed a planned experiment utilizing the DoE (Design of Experiment) method. Experiments aimed at varying process parameters (traverse speed, standoff distance, abrasive mass flow) were conducted at a water pressure of 50 MPa, assessing the parameters' impact on the removed material and the properties of the resulting non-transient erosion groove. The properties of the erosion groove, such as shape and the material removal (area of erosion groove), were evaluated. The influences of process parameters on the observed parameters were assessed using the analysis of variance (ANOVA) method. Experiment preparation and setup were based on a thorough theoretical analysis of the machining process with the abrasive water jet (AWJ) method. The experiment also highlights the diverse properties of the SS 316L material prepared using the SLM method when machined with AWJ technology. [ABSTRACT FROM AUTHOR]

The material extrusion (MEX) method utilizing highly filled metal filament presents an alternative to advanced additive metal manufacturing technologies. This process enables the production of metal objects through deposition and sintering, which is particularly attractive compared to powder bed fusion (PBF) technologies employing lasers or high-power electron beams. PBF requires costly maintenance, skilled operators, and controlled process conditions, whereas MEX does not impose such requirements. This study compares research on 17-4 PH steel manufactured using two different commercially available techniques: MEX and powder bed fusion with laser beam melting (PBF-LB/M). This research included assessing the density of printed samples, analyzing surface roughness in two printing planes, examining microstructure including porosity and density determination, and measuring hardness. The conducted research aimed to determine the durability and quality of the obtained samples and to evaluate their strength. The research results indicated that samples produced using the PBF-LB/M technology exhibited better density and a more homogeneous structure. However, MEX samples exhibited better strength properties (hardness). [ABSTRACT FROM AUTHOR]

Aiming at ensuring the quality of the product and reducing the cost of steel manufacturing, an increasing number of studies have been developing nonlinear regression models for the prediction of the mechanical properties of steel rebars using machine learning techniques. Bearing this in mind, we revisit this problem by developing a design methodology that amalgamates two powerful concepts in parsimonious model building: (i) sparsity, in the sense that few support vectors are required for building the predictive model, and ( i i ) locality, in the sense that simpler models can be fitted to smaller data partitions. In this regard, two regression models based on the Least Squares Support Vector Regression (LSSVR) model are developed. The first one is an improved sparse version of the one introduced in a previous work. The second one is a novel local LSSVR-based regression model. The task of interest is the prediction of four output variables (the mechanical properties YS, UTS, UTS/YS, and PE) based on information about its chemical composition (12 variables) and the parameters of the heat treatment rolling (6 variables). The proposed LSSVR-based regression models are evaluated using real-world data collected from steel rebar manufacturing and compared with the global LSSVR model. The local sparse LSSVR approach was able to consistently outperform the standard single regression model approach in the task of interest, achieving improvements in the average  R 2  from previous studies: 5.04% for UTS, 5.19% for YS, 1.96% for UTS/YS, and 3.41% for PE. Furthermore, the sparsification of the dataset and the local modeling approach significantly reduce the number of SV operations on average, utilizing 34.0% of the total SVs available for UTS estimation, 44.0% for YS, 31.3% for UTS/YS, and 32.8% for PE. [ABSTRACT FROM AUTHOR]

The temperature of hot metal (HM) is crucial for the energy input and smelting in the electric arc furnace (EAF) steelmaking process with HM and scrap as the charge structure. However, due to the influence of many factors in the heat dissipation in HM transportation before the EAF steelmaking process, the temperature drop of HM before charged is usually fluctuating and uncertain. This situation is not conducive to the input energy control and energy optimization of the EAF steelmaking process. In this paper, a three-dimensional numerical model of a 90-ton hot metal ladle is established to simulate the heat transfer characteristic of HM transportation through ANSYS Fluent 2023 and verified by on-the-spot testing and sample analysis. The effects of ambient temperature, air velocity, slag thickness and furnace cover thickness on the temperature drop of HM are investigated and quantitatively analyzed in 30 numerical schemes. The results indicate that slag thickness is the most influential factor, followed by furnace cover thickness, air velocity and ambient temperature. In the case of 50 min transport time, the temperature drop of HM is 55.2, 15.06, 12.08, 10.38, 10.29 and 10.26 °C when the slag thickness is 0, 50, 100, 150, 200 and 250 mm, respectively. While HM is not covered by slag, the furnace cover can also greatly reduce the temperature drop. Based on the simulated data, a prediction model of HM temperature drop is obtained through the multi-factor coupling analysis and mathematical fitting. This study can help develop targeted insulation measures and determine the temperature of HM, which is expected to control the input energy for deep energy-saving optimization in the EAF steelmaking process. [ABSTRACT FROM AUTHOR]

To achieve low‐cost clean production in steelmaking plants, a low‐carbon double slag converter steelmaking process (LCDSP) with high dephosphorization efficiency, low lime consumption, and low CO2 emissions is focused on. The average lime consumption in LCDSP has decreased about 25.8%, which can reduce at least about 10.3 million m3 CO2 emissions per converter per year in the current steelmaking plant. The phosphorus enrichment properties of decarbonization slag with different basicities are comprehensively evaluated through LCDSP industrial experiments, mineralogy, and ion–molecule coexistence theory (IMCT). The increase of decarburization slag basicity (DSB) significantly improves the dephosphorization efficiency of molten steel. With increasing the DSB from 2.93 to 3.52, the typical morphology of the P‐rich phase extends from small pieces to long strips and then changes to block shapes with smooth edges. Finally, it forms a massive P‐rich phase with an area of more than 10 000 μm2 with a DSB of 3.80. The phosphorus enrichment properties of decarbonization slag with different DSBs evaluated using multiple perspectives show a consistent change trend. IMCT provides an accurate thermodynamic method for quantitatively predicting the phosphorus enrichment capacity in slag in two stages of LCDSP. [ABSTRACT FROM AUTHOR]

With the concept of Industry 4.0 production processes are moving towards autonomy and intelligence. Technologies equipped with artificial intelligence (AI) are involved into processes that are more and more digitized. Collaborative technologies are a feature of discrete processes. The automotive industry has achieved many successes in the process innovation towards smart factories. Other plants, such as smelters or coal mining are also striving to develop smart manufacturing with integrated computer systems to support processes. A continuous production is different from a discrete or batch production. Industry 4.0 concept is focused on discrete production (with high level of automation and robotization of manufacturing) meanwhile there is a gap in implementation of these approach in the continuous production. The objective of the publication is to prepare and design the integrated computer management system based on processes realized in coal and steel manufacturing. Coal and steel production are key elements in a chain of any industrial manufacturing e.g. automotive or machinery engineering. These processes are crucial in building of smart value chain. In our paper we present the structure of processes for the continuous production. Based the processes model we proposed the next steps to build the smart manufacturing for continuous production. [ABSTRACT FROM AUTHOR]

Previous research suggested that friction-based processing is a promising method for fabricating oxide dispersion-strengthened (ODS) steel. In this study, we combined friction consolidation and friction extrusion to successfully manufacture ODS steel rods using precursor powder made with gas atomization reaction synthesis. We examined the microstructure evolution from the initial powder to the final extruded rod, which revealed the dispersion process of Y. Additionally, by comparing the microstructures of three rods extruded at different temperatures, we showed that low-temperature friction extrusion effectively enhanced microstructure uniformity and prevented grain coarsening, leading to improved mechanical properties. Our findings provide practical guidelines for adjusting processing parameters in the production of ODS steel using friction-based processing. [ABSTRACT FROM AUTHOR]

The distribution of oxygen between the gaseous and liquid oxidation products at the hot spot is modeled. The simplified model accurately describes the interaction of the gas jet exiting the lance nozzles with the gaseous surrounding of the jet. This interaction changes the chemical composition of the gas jet influencing the subsequent interaction with the hot spot. The iterative procedure then calculates the chemical composition of the gas exiting the cavity. The characterization of the exiting gas flowing past the lance head as well as the generation rate of FeO is attained by combining the modeled gas composition exiting the cavity with an oxygen mass balance based on a quasi-stationary BOF operation. [ABSTRACT FROM AUTHOR]

The converter steelmaking process is an important part of metallurgical production, and the flame characteristics at the furnace mouth indirectly reflect the smelting conditions inside the furnace. Effectively recognizing and predicting the smelting conditions of converter steelmaking is a challenging and critical issue in industrial production. However, traditional image-based methods using a single static flame image as input have low recognition accuracy and cannot accurately reflect changes in smelting conditions. To address this problem, a new recognition model is proposed in this study, which first preprocesses the flame video sequences at the furnace opening, and then applies a convolutional recurrent neural network (CRNN) to further learn the spatio-temporal features and obtain recognition results. In addition, in order to further improve the accuracy of the model, we introduced the channel attention mechanism and verified the effectiveness of the model through the feature layer visualization technique. In addition we quantitatively evaluate the model performance by accuracy, precision, recall, and F1-score, and plot the confusion matrix with AUC–ROC curves. The experimental results show that the method is not only effective but also robust and has a large potential for industrial applications. [ABSTRACT FROM AUTHOR]

The O2–CO2 mixing injection method was proposed to mitigate CO2 emissions and enhance the CO content in flue gas during the BOF steelmaking process. Despite the reported advantages of the O2–CO2 mixing injection method, limited research existed regarding the impact of Laval nozzle structures designed by various CO2 mixing rates on the flow characteristics of supersonic mixing jets. In this research, we investigated four types of Laval nozzle structures designed for varying CO2 mixing rates through a series of experimental tests and numerical simulations. The results indicated that when the CO2 additional flow rate increased from 0 to 15 mass pct, the impaction ability of the mixing jet enhanced. However, this increasing rate was gradually suppressed due to the thermo-physical property variations between O2 and CO2. Under room and high ambient temperature conditions, the average axial velocity variation within the velocity potential core was 1.58 and 1.68 m/(s·mass pct), respectively. To further validate the metallurgical effects, a pilot test was conducted using a 1.0 ton converter with O2 and CO2 blowing rates of 210 and 31.5 Nm3/h, respectively, demonstrating that the Laval nozzle structure designed based on the O2–CO2 mixing gas thermo-physical property could achieve superior metallurgical outcomes. [ABSTRACT FROM AUTHOR]

Replacement of cement with electric arc furnace (EAF) slag at higher volumes causes volumetric expansion; therefore, such blends are not recommended in concrete production. In this study, the effect of this slag on the performance and microstructure of mortar samples based on wollastonite (CaSiO3) was examined. The samples were cured in a CO2-rich environment, resulting in the formation of non-expansive products, including aragonite, calcite, and traces of tobermorite in the microstructure. The addition of slag above 20% affected the workability and strength developments. However, the formation of pores above 100 nm reduced with increasing slag content to 60%, highlighting the beneficial effect of slag when used in higher volumes. EAF slag contains a higher amount of Fe2O3 which limits its disposal at landfills, but its increased use in the production of CO2 gas-cured wollastonite concrete can reduce the environmental burdens caused by the Portland cement and steel manufacturing industries. [ABSTRACT FROM AUTHOR]

The continuous improvement of the steelmaking process is a critical issue for steelmakers. In the production of Ca-treated Al-killed steel, the Ca and S contents are controlled for successful inclusion modification treatment. In this study, a machine learning technique was used to build a decision tree classifier and thus identify the process variables that most influence the desired Ca and S contents at the end of ladle furnace refining. The attribute of the root node of the decision tree was correlated with process variables via the Pearson formalism. Thus, the attribute of the root node corresponded to the sulfur distribution coefficient at the end of the refining process, and its value allowed for the discrimination of satisfactory heats from unsatisfactory heats. The variables with higher correlation with the sulfur distribution coefficient were the content of sulfur in both steel and slag at the end of the refining process, as well as the Si content at that stage of the process. As secondary variables, the Si content and the basicity of the slag at the end of the refining process were correlated with the S content in the steel and slag, respectively, at that stage. The analysis showed that the conditions of steel and slag at the beginning of the refining process and the efficient S removal during the refining process are crucial for reaching desired Ca and S contents. [ABSTRACT FROM AUTHOR]

Laser metal deposition (LMD) is an advanced manufacturing technology which has become increasingly popular in recent years. The cyclic tests of 304 austenitic stainless steel (SS304) manufactured by LMD are conducted under symmetrical and asymmetrical cyclic stress. In the results, it is shown that SS304 manufactured by LMD exhibits cyclic softening/hardening and ratcheting effect under symmetrical cyclic stress. The ratcheting strain and ratcheting strain rate of the smooth specimen both increase as the cyclic stress amplitude under the asymmetric cyclic stress. The ratcheting strain and ratcheting strain rate of the smooth specimen change non‐monotonously as the mean stress increases. As the stress amplitude increases, the area of fatigue fracture becomes smaller until no fatigue crack initiation zone can be observed. In this case, only the characteristic of plastic fracture is significant. Park et al.'s model has the worst predicted accuracy. Modified Goodman equation and Smith–Watson–Topper model have the similar predicted accuracy. [ABSTRACT FROM AUTHOR]

This study focuses on utilizing ground granulated blast furnace slag (GGBS) from a steel plant as the main raw material, with water and calcium‐based montmorillonite as binders, with manganese and alkali metal nitrates (Na, K, Mg, Ca). A series of supported denitration catalysts are prepared using the equal volume impregnation method. These catalysts are then employed in the selective catalytic reduction process to investigate their impact on denitration effectiveness. Furthermore, the mechanism of low‐temperature denitration in selective catalytic reduction is analyzed by combining catalyst activity tests and characterization techniques. The activity of catalysts is assessed through Fourier transform infrared spectrometer (FT‐IR), X‐Ray diffraction (XRD), and search engine marketing (SEM) characterization tests, along with examining its denitration and sulfur resistance capabilities to determine the denitration mechanism. Experimental results indicate that Mn–Mg/GGBS exhibit the most efficient low‐temperature denitrification effect, and it demonstrates certain sulfur resistance with a 5% Mn loading and a 3% second active component. Introducing Ce as the third active component further enhances the sulfur resistance of Mn–Mg–Ce/GGBS when loaded at 1%. [ABSTRACT FROM AUTHOR]

Currently, converter steelmaking is the main method for steelmaking, the performance of bottom blowing and bath stirring would obviously affect the efficient and stable smelting of high‐quality clean steel. At present, the uniform bottom gas distribution mode is mostly applied in converter steelmaking, which would probably lead to unstable stirring performance and inadequate mixing at the later stage of furnace campaign. Therefore, a nonuniform bottom gas distribution mode is proposed herein, which intends to improve the mixing efficiency. The physical and numerical simulations are carried out to investigate the influence of gas flowrate, nozzle configurations, and bottom gas distribution mode on bath stirring and mixing. In the results, it is shown that the large and asymmetric circulation flow is formed due to the nonuniform gas distribution for each nozzle, which enhances stirring in the horizontal direction and ultimately improves mixing efficiency. Compared with the uniform gas distribution mode, when gas distribution ratio of nonuniform bottom blowing is 3:1, the mixing time is reduced by 29%, the average velocity of liquid steel is increased from 0.0342 to 0.0404 m s−1, the ratio of the dead zone is decreased by 26.23%, and the proportion of active flow region is added by 10.49%. [ABSTRACT FROM AUTHOR]

In this study the mechanical anisotropy of laser powder bed fusion (LPBF) 316L stainless steel under tensile, compressive, and shear loading in different orientations with respect to the build direction is investigated. Experimental analysis revealed a moderate degree of anisotropy, which is mainly attributed to the build direction. In addition, the mechanical properties were seen to be dependent on the stress state, as evidenced by the tension-compression asymmetry. The anisotropy and asymmetry can be explained by various microstructural factors with texture orientation being one of the most significant. To incorporate such material behavior in structural analysis, a phenomenological model for anisotropic plasticity and tension-compression asymmetry was proposed and calibrated for additive 316L steel. The flexibility of the model allows it to be applied using a mechanical test set on uniaxial loading, and further enhancements may rely on data for combined stress state. The model was calibrated based on FEA of samples loading and fit well all experimental curves, though factors such as residual stresses and test imperfections could introduce some discrepancies. [ABSTRACT FROM AUTHOR]