The effect of tempering and auto-tempering on the microstructure–property relationship of two ultra-high strength press hardening steels (PHS1500 and PHS2000) was studied. Both steels were austenitized, oil quenched, and subsequently tempered at four different temperatures ranging from 180 °C to 300 °C. For auto-tempering, the steels underwent austenitization and quenching using a press equipped with planar tools and were subsequently ejected at varying cooling durations. The tensile properties, hardness, microstructure, and dislocation densities after heat treatment were characterized. The results showed that the effect of tempering temperature on tensile properties and microstructure features was more pronounced than the effect of tempering time for both steels. Tensile strength and hardness decreased slightly with increasing tempering temperature up to 200 °C. Above that temperature, there was a further decrease in tensile strength and hardness, which is suggested to be due to the formation and coarsening of carbides in the highly dislocated martensitic matrix. In contrast to the tensile strength and hardness, the yield strength increased with increasing tempering temperatures, which is most probably due to internal stress relaxation. Total elongation was increased with increasing tempering temperatures, except for the samples tempered at 250 °C and 300 °C. These samples experienced a reduction in elongation at fracture, which was more pronounced after tempering at 300 °C than at 250 °C. This was most likely attributed to the so-called tempered martensite embrittlement effect. Calculation of dislocation densities before and after tempering treatments confirmed dislocation annihilation and recovery of martensitic microstructure. 

This work investigates the quantitative effect of the hydrogen concentration of 100Cr6 bearing steel on the surface-initiated damage induced during lubricated rolling/sliding tribotesting. Hydrogen was introduced to the samples prior to tribotesting by electrochemical pre-charging, and hydrogen concentration was measured using thermal desorption analysis. The surface-initiated damage was quantified by optical profilometry and scanning electron microscopy. An upper limit for the critical hydrogen concentration was determined to be 1.4–2 wppm diffusible hydrogen. At this concentration, the area fraction covered by damage features was found to double compared to uncharged samples. As both charged and uncharged samples exhibited the same type of surface damage (early-stage micropitting), it was concluded that hydrogen did not change the wear mechanism but decreased the number of contact cycles necessary for the initiation of surface defects.

As hydrogen reduces the fatigue life of 100Cr6 bearing steel significantly, extensive research on the interaction of hydrogen with 100Cr6 is necessary. This study investigated the influence of rolling/sliding tribotesting performed on a micro-pitting-rig on the hydrogen absorption and trapping behaviour of 100Cr6 bearing steel. Thermal desorption mass spectrometry was used to compare the hydrogen desorption spectra of 100Cr6 samples after tribological tests and static heated oil-immersion tests to untested reference samples. The approach was chosen to further understand the influence of both microstructural deformation as well as steel-oil contact on the hydrogen absorption and trapping behaviour of 100Cr6. The tribological test showed a stable friction behaviour and mild wear which was dominated by local plastic deformation of surface asperities. Despite the mild wear, a change in de-trapping temperatures was found for tribotested samples compared to oil-immersed and untested reference samples. This finding indicates that even mild tribotesting conditions alter the hydrogen trapping behaviour of 100Cr6 bearing steel.

Additive manufacturing is known for producing complex metal components, particularly with materials like 316L stainless steel. However, ensuring the quality and microstructural consistency of such components remains a challenge, as traditional testing methods are often destructive and time-intensive. Data driven models that are used for non-destructive evaluation are often difficult to interpret. This study explores the use ultrasound measurements combined with a multivariate statistical technique (partial least squares), to estimate the material properties of steel samples and examining the relationships between ultrasound signals at various frequencies and material properties such as porosity, grain size, and hardness. This aims to enhance the interpretability of ultrasound testing for additive manufacturing. Our findings indicate that ultrasound backscatter can be effectively linked to key material properties.

Additive manufacturing (AM) using powder bed fusion is becoming a mature technology that offers great possibilities and design freedom for manufacturing of near net shape components. However, for many gas turbine and aerospace applications, machining is still required, which motivates further research on the machinability and work piece integrity of additive-manufactured superalloys. In this work, turning tests have been performed on components made with both Powder Bed Fusion for Laser Beam (PBF-LB) and Electron Beam (PBF-EB) in as-built and heat-treated conditions. The two AM processes and the respective heat-treatments have generated different microstructural features that have a great impact on both the tool wear and the work piece surface integrity. The results show that the PBF-EB components have relatively lower geometrical accuracy, a rough surface topography, a coarse microstructure with hard precipitates and low residual stresses after printing. Turning of the PBF-EB material results in high cutting tool wear, which induces moderate tensile surface stresses that are balanced by deep compressive stresses and a superficial deformed surface that is greater for the heat-treated material. In comparison, the PBF-LB components have a higher geometrical accuracy, a relatively smooth topography and a fine microstructure, but with high tensile stresses after printing. Machining of PBF-LB material resulted in higher tool wear for the heat-treated material, increase of 49%, and significantly higher tensile surface stresses followed by shallower compressive stresses below the surface compared to the PBF-EB materials, but with no superficially deformed surface. It is further observed an 87% higher tool wear for PBF-EB in as-built condition and 43% in the heat-treated condition compared to the PBF-LB material. These results show that the selection of cutting tools and cutting settings are critical, which requires the development of suitable machining parameters that are designed for the microstructure of the material.

A 316 austenitic stainless-steel alloy, with modified alloy composition, manufactured by laser powder bed fusion (L-PBF) has been investigated. The modification of the alloy composition included addition of niobium (Nb), tungsten (W) and copper (Cu), together with a reduction in the amount of molybdenum (Mo) and an increased amount of carbon (C). To find suitable process parameters, a parameter study by varying laser power, hatch distance and scan speed was performed, centered on typical parameters used for normal 316 L. As-built material from a selected parameter configuration was then subjected to different stress relief annealing heat treatments and ageing heat treatments. The effectiveness of the stress annealing was ranked using a deformation-based method. Microstructural characterization, hardness and room temperature tensile testing were done to evaluate the effect of stress relief and aging heat treatments.

It was found that a higher volumetric energy was needed to build dense material, about ∼50 % higher compared to the volumetric energy input for normal 316 L. A subsequent aging heat treatment at 725 °C for 3 h increased the strength and hardness of the material. A reinforcement of the cellular microstructure by precipitation of carbides in between the cells is believed to be the main reason for this. To completely alleviate the residual stresses it was necessary to carry out a stress relief annealing process at 950 °C, which resulted in a removal of the cellular structure and a lower strength material.

Additive Manufacturing is used for printing parts with high precision and complex geometries, but achieving consistent material properties and avoiding defects is a challenge. This paper presents the use of ultrasound technology as a non-destructive method to optimize the additive manufacturing process. A factorial design is used to print 18 samples using the key process parameters such as Power, Speed, and Hatch Distance. The ultrasound measurements are carried out using a 7.5 MHz focused transducer to capture within-sample variation. The manufacturing parameters and ultrasound variation metric is converted to a response surface model which is then used to identify optimal manufacturing conditions that can help minimize process induced variation and get a consistent microstructure and achieve consistent mechanical properties.

Metal based additive manufacturing techniques such as laser powder bed fusion can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel samples. The steel samples are manufactured with varying process parameters (speed, hatch distance and power) in two batches that are placed at different locations on the build plate. These samples are examined with ultrasound using a focused transducer. The ultrasound scans are performed in a dense grid in the build and transverse direction, respectively. Part of the ultrasound data are used to train a partial least squares regression algorithm by labelling the data with the corresponding manufacturing parameters (speed, hatch distance and power, and build plate location). The remaining data are used for testing of the resulting model. To assess the uncertainty of the method, a Monte-Carlo simulation approach is adopted, providing a confidence interval for the predicted manufacturing parameters. The analysis is performed in both the build and transverse direction. Since the material is anisotropic, results show that there are differences, but that the manufacturing parameters has an effect of the material microstructure in both directions.

Metal based additive manufacturing techniques such as laser powder bed fusion (LPBF) can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel cubes. Nine cubes with varying manufacturing parameters (speed, hatch distance and power) are examined with ultrasound using focused transducers. The volumetric energy density (VED) is calculated from the process parameters for each cube. The ultrasound scans are performed in a dense grid in the built and transverse direction. The ultrasound data is used in partial least square regression algorithm by labelling the data with speed, hatch distance and power and then by labelling the same data with the VED. These models are computed for both measurement directions and as the samples are anisotropic, we see different behaviours of estimation in each direction. The model is then validated with an unknown set from the same 9 cubes. The manufacturing parameters are estimated and validated with a good accuracy making way for online process control.