In the context of the use of AM, particularly in the L-PBF technique, the printability characterization of material occurs through the identification of its printability map as a function of printing process parameters. The printability map identifies the region where the powder melting is optimal and ensures a dense and defect-free material. Identifying the zones affected by physical phenomena that occur during the printing process which lead to material defects such as keyhole, lack of fusion and balling mode is also possible. Classical methods for the characterization of material and the identification of its printability map require the printing of a large number of specimens. The analysis of the specimens is currently time-consuming and costly. This paper proposed a methodology to identify optimal process parameters in L-PBF using an integrated single and multi-tracks analyses embedded in an overall algorithm with detailed metrics and specific factors. The main scope is to speed up the identification of printability window and, consequently, material characterization, reducing the number of micrographic analyses. The method is validated through an experimental campaign assessing the material microstructure in terms of porosity and melt pool evaluation. The case study on IN718 superalloy shows how the application of the proposed method allows an important reduction of micrographic analysis. The results obtained in the case study are a reduction of 25% for the complete definition of the printability map and more than 90% for identifying the zone with a high productivity rate.

In the additive manufacturing laser powder bed fusion (L-PBF) process, the optimization of the print process parameters and the development of conduction zones in the laser power (P) and scanning speed (V) parameter spaces are critical to meeting production quality, productivity, and volume goals. In this paper, we propose the use of a machine learning approach during the process parameter development to predict the melt pool dimensions as a function of the P/V combination. This approach turns out to be useful in speeding up the identification of the printability map of the material and defining the conduction zone during the development phase. Moreover, a machine learning method allows for an accurate investigation of the most promising configurations in the P-V space, facilitating the optimization and identification of the P-V set with the highest productivity. This approach is validated by an experimental campaign carried out on samples of Inconel 718, and the effects of some additional parameters, such as the layer thickness (in the range of 30 to 90 microns) and the preheating temperature of the building platform, are evaluated. More specifically, the experimental data have been used to train supervised machine learning models for regression using the KNIME Analytics Platform (version 4.7.7). An AutoML (node for regression) tool is used to identify the most appropriate model based on the evaluation of R² and MAE scores. The gradient boosted tree model also performs best compared to Rosenthal’s analytical model.

This paper investigates the effects on the material microstructure of varying the Inter-Layer Cooling Time (ILCT) during the printing process in laser powder bed fusion (L-PBF) multi-laser machines. Despite these machines allowing higher productivity rates compared to single laser machines, they are affected by lower ILCT values, which could be critical for material printability and microstructure. The ILCT values depend both on the process parameter sets and design choices for the parts and play an important role in the Design for Additive Manufacturing approach in L-PBF process. In order to identify the critical range of ILCT for this working condition, an experimental campaign is presented on the nickel-based superalloy Inconel 718, which is widely used for the printing of turbomachinery components. The effect of ILCT on the microstructure of the material is evaluated in terms of porosity and melt pool analysis on printed cylinder specimens, considering ILCT decreasing and increasing in the range of 22 to 2 s. The experimental campaign shows that an ILCT of less than 6 s introduces criticality in the material microstructure. In particular, at an ILCT value of 2 s, widespread keyhole porosity (close to 1‰) and critical and deeper melt pool (about 200 microns depth) are measured. This variation in melt pool shape indicates a change in the powder melting regime and, consequently, modifications of the printability window promoting the expansion of the keyhole region. In addition, specimens with geometry obstructing the heat flow have been studied using the critical ILCT value (2 s) to evaluate the effect of the surface-to-volume ratio. The results show an enhancement of the porosity value (about 3‰), while this effect is limited for the depth of the melt pool.

Laser Powder Bed Fusion (L-PBF) is one of the most widespread, versatile, and promising metal Additive Manufacturing (AM) techniques. L-PBF allows for the manufacturing of geometrically complex parts with good surface characteristics. In this process, in order to minimize the heat loss in the first layers of printing, the building platform is preheated to a temperature ranging between 80 and 250 °C. This aspect turns out to be very critical, and further investigation is needed for situations where the part to be printed is only a few layers high, as is the case in sensor printing. This work aims to investigate the melt pool stability under a variation in the preheating temperatures. We investigate the distance from the building platform, considering the number of layers printed. This is where the melt pool reaches its stability in terms of depth and width. This aspect turns out to be of remarkable importance for ensuring the structural integrity of parts with a few layers of height that are processed through L-PBF, such as sensors, which are proliferating in different industries. Thus, two case studies were carried out on IN718 superalloys at 40 and 60 microns of layer thickness and a preheating temperature of 170 °C on the machine. The results obtained show that after 1.2 mm of distance from the building platform, the melt pool reached its stability in terms of width and depth dimensions and consequently for the melting regime.

The primary aim was to evaluate the association of patient-related factors, biomaterials, and implant characteristics on complications' rate of sinus graft surgery and on implant survival rate in grafted sinus. Secondary aims were to measure bone remodeling around implants and patient satisfaction. A retrospective cohort study was designed. Patients who had computerized tomography (CT) before sinus surgery (T0), orthopantomography after implant surgery (T1) and at follow-up (T2), were included. Specific forms were used to collect clinical data. Radiographic measures were: height of residual bone before sinus surgery measured on CT (T0) and apical and marginal bone levels around implants measured on orthopantomography at T1 and T2. Forty-three lateral sinus lifts were performed. Three grafts failed before implant insertion. Out of 83 implants inserted in 29 patients, a total of 19 failed. Mean follow-up (T2) was 6 ± 1.8 years [4; 11.2 years]. The multilevel models analysis showed no association between complications rate and patient-related factors, biomaterials, and implant characteristics. Smoking (odds ratio [OR]: 8.3; 95% CI 1.46–48.05, P = .0173) and height of residual bone (OR: 0.32 for each mm; 95% CI 0.15–0.68, P = .0034) were associated with implant failure. Bone remodeling between T1 and T2 was −0.8 ± 0.2 mm for apical bone and −0.6 ± 0.3 mm for marginal bone. General therapy satisfaction measured in a visual analogue scale was 8.4 ±1.4. In conclusion, lower height of residual bone before sinus surgery and smoking habits had a negative prognostic effect on survival rate of dental implants placed in grafted sinuses.