Performance of High-Layer-Thickness Ti6Al4V Fabricated by Electron Beam Powder Bed Fusion under Different Accelerating Voltage Values

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.

Effect of Layer Thickness and Heat Treatment on Microstructure and Mechanical Properties of Alloy 625 Manufactured by Electron Beam Powder Bed Fusion

This research program investigated the effects of layer thickness (50 µm and 100 µm) on the microstructure and mechanical properties of electron beam powder bed fusion (EBPBF) additive manufacturing of Inconel 625 alloy. The as-built 50 µm and 100 µm layer thickness components were also heat treated at temperatures above 1100 °C which produced a recrystallized grain structure containing annealing twins in the 50 µm layer thickness components, and a duplex grain structure consisting of islands of very small equiaxed grains dispersed in a recrystallized, large-grain structure containing annealing twins. The heat-treated components of the microstructures and mechanical properties were compared with the as-built components in both the build direction (vertical) and perpendicular (horizontal) to the build direction. Vickers microindentation hardness (HV) values for the vertical and horizontal geometries averaged 227 and 220 for the as-built 50 µm and 100 µm layer components, respectively, and 185 and 282 for the corresponding heat-treated components. The yield stress values were 387 MPa and 365 MPa for the as-built horizontal and vertical 50 µm layer geometries, and 330 MPa and 340 MPa for the as-built 100 µm layer components. For the heat-treated 50 µm components, the yield stress values were 340 and 321 MPa for the horizontal and vertical geometries, and 581 and 489 MPa for the 100 µm layer components, respectively. The elongation for the 100 µm layer as-built horizontal components was 28% in contrast with 65% for the corresponding 100 µm heat-treated layer components, an increase of 132% for the duplex grain structure.

In Silico Contact Pressure of Metal-on-Metal Total Hip Implant with Different Materials Subjected to Gait Loading

The use of material for implant bearing has a vital role in minimizing failures that endanger implant recipients. Evaluation of contact pressure of bearing material can be the basis for material selection and have correlations with wear that contribute to the need of revision operations. The current paper aims to investigate three different metallic materials, namely cobalt chromium molybdenum (CoCrMo), stainless steel 316L (SS 316L), and titanium alloy (Ti6Al4V) for application in metal-on-metal bearing of total hip implant in terms of contact pressure. In silico model based on finite element simulation has been considered to predict contact pressure of metal-on-metal bearings under normal walking conditions. It is found that the use of Ti6Al-4V-on-Ti6Al4V is superior in its ability to reduce contact pressure by more than 35% compared to the other studied metal-on-metal couple bearings.