Error Detection in Laser Beam Melting Systems by High Resolution Imaging

Laser Beam Melting as a member of Additive Manufacturing processes allows the fabrication of three-dimensional metallic parts with almost unlimited geometrical complexity and very good mechanical properties. However, its potential in areas of application such as aerospace or medicine has not yet been exploited due to the lack of process stability and quality management. For that reason samples with pre-defined process irregularities are built and the resulting errors are detected using high-resolution imaging. This paper presents an overview of typical process errors and proposes a catalog of measures to reduce process breakdowns. Based on this systematical summary a future contribution to quality assurance and process documentation is aspired.Mechanical Engineerin

Position Dependency of Surface Roughness in Parts from Laser Beam Melting Systems

Laser Beam Melting is a promising Additive Manufacturing technology for the production of complex metal components. During batch production of multiple identical parts in a single build job, we observed parts with deviating surface roughness in certain areas, which all faced away from the laser. This suggests a dependency of surface roughness on the part position in the build chamber. In this work we systematically reproduce and analyze this effect. We place hollow pyramids with twelve faces and two different overhanging angles at nine positions on the substrate plate and build this setup twice, using an imaging setup for process documentation. Surface roughness is measured by contact profilometry on three lines for each pyramid face. Our experiments reproduce the effect. Based on these findings we present a hypothesis for the cause and show metallographic images to support our theory. As a consequence, the position relative to the laser should be considered in the design phase for parts with high surface quality requirements.Mechanical Engineerin

Alternative Approach on an In-Situ Analysis of the Thermal Progression During the LPBF-M Process Using Welded Thermocouples Embedded into the Substrate Plate

Laser powder bed fusion (LPBF-M) is a very potent technology for creating highly individualized, complex, and functional metal parts. One of the major influencing factors is the thermal progression. It significantly determines size accuracy, microstructure and process stability. Therefore, creating an enhanced understanding of thermal phenomena through measurements and simulations is crucial to increase the reliability of the technology. Current research is mainly based on temperature measurements of the upper layer, leaving major scope for the conditions at the substrate-part-interface. This area is of utmost technical importance because it serves as the main heat sink. Insufficient heat dissipation leads to accumulations of heat, deformations, and process breakdowns. This contribution presents a simple and flexible method to analyze the thermal progression close to the part inside the substrate plate. The acquired data shows very high consistency. Additionally, the results are compared to a model created using an ISEMP developed FEM-Software which shows promising results for validation studies.Mechanical Engineerin

Mechanical Properties of Honeycomb Structured Zr-based Bulk Metallic Glass Specimens Fabricated by Laser Powder Bed Fusion

Laser powder bed fusion of bulk metallic glasses offers great potential to overcome the existing restrictions of the geometrical size and complexity of bulk metallic glasses in conventional manufacturing routes due to high cooling rates during laser powder bed fusion. Bulk metallic glasses exhibit extraordinary strength, paired with high elasticity. Yet insights into additive manufactured bulk metallic glasses, especially of complex structures, are limited. The present article investigates the mechanical behaviour of Zr-based bulk metallic glasses, fabricated into honeycomb structures through laser powder bed fusion, by performing three-point bending tests. The results reveal a significant increase in specific strength, quasi-plasticity, and high elastic elongation. These structures thus offer great potential for light-weight applications and compliant mechanisms

Processability of Soda-Lime Glass in Laser-Based Powder Bed Fusion

Processing of electrically insulating materials with high temperature resistance is a major challenge in laser-based powder bed fusion (PBF-LB). Glasses form a promising material class, which also offers the potential for manufacturing optical or electronic components while having high chemical resistance. Therefore, this paper investigates the processability of soda-lime glass in conventional PBF-LB machines using Yb:YAG and CO2 lasers. Firstly, the flow properties and particle shape of the glass powder were inspected. Secondly, the influence of laser power, scan velocity, layer height and hatch distance as well as exposure pattern on the manufacturing of single tracks, single layers, and finally 3- D-parts was investigated. Furthermore, an increase of the temperature of the platform range between 250 to 600 °C resulted in increasing relative density. Despite the higher absorptivity of soda lime glass in the wavelength range of the CO2 laser, manufacturing of 3-D-parts was only possible using a Yb:YAG laser due to insufficient laser power of the former beam source.Mechanical Engineerin

Inline drift detection using monitoring systems and machine learning in selective laser melting

Direct metal laser sintering, an additive manufacturing technique, has a huge growing demand in industries like aerospace, biomedical, and tooling sector due to its capability to manufacture complex parts with ease. Despite many technological advancements, the reliability and repeatability of the process are still an issue. Therefore, there is a demand for inline automatic fault detection and postprocessing tools to analyze the acquired in situ monitoring data aiming to provide better-quality assurance to the user. Herein, the treatment of the data obtained using the EOSTATE optical tomography monitoring system is focused. A balanced dataset is obtained with the help of computer tomography of the certified part (Stainless Steel CX cylindrical samples), through which a feature matrix is prepared, and the layers of the part are classified either having "Drift" or "No-drift." The model is trained with the feature matrix and tested on benchmark parts (Maraging Steel) and on an industrial part (knuckle, automotive part) manufactured in AlSi10Mg. The proposed semisupervised approach shows promising results for presented case studies. Thus, the semisupervised machine learning approach, if adopted, could prove to be a cost effective and fast approach to postprocess the in situ monitoring data with much ease

Multi-biomarker responses in green mussels exposed to PFCs: Effects at molecular, cellular, and physiological levels

10.1007/s11356-013-2216-6Environmental Science and Pollution Research2142785-2794ESPL