Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion

Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection

Investigation of temperature gradient and solidification rate in laser-based powder bed fusion using a high-speed camera to evaluate local microstructure characteristics

Laser-based powder bed fusion of metallic materials is widely used in industrial application with major challenges for safety-critical components. However, the layerwise build-up and process parallel monitoring systems offer the potential of direct quality evaluation. This work proposes a novel process monitoring concept to enable the local evaluation of the solidification and consequently an estimation of the microstructure using an on-axis high-speed camera. Based on characteristic intensity values of the phase transitions, the solidification rate and temperature gradient in scan vector direction are determined. A linear regression model estimates the mean grain diameter along the melt path

Examination of Discretised Mini-Channel Elements for the Transport of Air Manufactured by Selective Laser Melting

Additive manufacturing (AM) processes, in particular selective laser melting (SLM), are predestined for the implementation of innovative cutting tool functions and tool geometries. Using SLM, structures such as complex internal cooling channels for minimum quantity lubrication systems (MQL) and cryogenic carbon dioxide cooling , which were previously difficult to produce using conventional manufacturing processes, can now be produced. Mini-channels are used in a wide variety of applications in mechanical engineering. While they are used in plastic injection moulding and die casting to cool the tool mould, cooling channels are used in machining both for cooling and for the direct supply of lubricant to the cutting zon

Predicting and Controlling the Thermal Part History in Powder Bed Fusion Using Neural Networks

Laser-based powder bed fusion of metallic parts is used widely in different branches of industry. Although there have been many investigations to improve the process stability, thermal history is rarely taken into account. The thermal history describes the parts’ thermal situation throughout the build process as a result of successive heating and cooling with each layer. This could lead to different microstructures due to different thermal boundary conditions. In this paper, a methodology based on neural networks is developed to predict and control the parts’ temperature by adjusting the laser power. A thermal imaging system is used to monitor the thermal history and to generate a training data set for the neural network. The trained network is then used to predict and control the parts temperature. Finally, tensile testing is conducted to investigate the influence of the adjusted process on the mechanical properties of the parts.Mechanical Engineerin

Predicting and Controlling the Thermal Part History in Powder Bed Fusion Using Neutral Networks

Laser-based powder bed fusion of metallic parts is used widely in different branches of industry. Although there have been many investigations to improve the process stability, thermal history is rarely taken into account. The thermal history describes the parts' thermal situation throughout the build process as a result of successive heating and cooling with each layer. This could lead to different microstructures due to different thermal boundary conditions. In this paper, a methodology based on neural networks is developed to predict and control the parts' temperature by adjusting the laser power. A thermal imaging system is used to monitor the thermal history and to generate a training data set for the neural network. The trained network is then used to predict and control the parts temperature. Finally, tensile testing is conducted to investigate the influence of the adjusted process on the mechanical properties of the parts

Konstruktive Potentiale einer Mikrostrukturgradierung von topologieoptimierten L-PBF-Bauteilen

The design space of topology optimizations is often limited by installation space limitations and interfaces to other components, which can result in local stress concentrations in the resulting notches. In this paper, the potential of microstructure grading was investigated by means of FE analyses on the basis of two components. In experimental investigations of L-PBF manufactured tensile specimens made of AlSi10Mg, a producible variation range of the Young's modulus from 46 to 62 GPa could be determined. By grading the Young's modulus, a local stress reduction of 18.6% and 25% could be achieved by means of FE analysis, as well as a slight displacement of the stresses around the critical area

Evaluation of Solidification in Powder Bed Fusion using a High Speed Camera

Powder bed fusion using a laser beam (PBF-LB) [1] enables geometrical design freedom to build parts for optimized functionality. Furthermore, PBF-LB allows microstructural design freedom. By controlling the solidification behavior microstructural adaptions can be made to obtain the full potential of the material. As the solidification rates and the thermal gradient depend on the local part geometry, new data-driven approaches, e.g. machine learning (ML), seem to be suitable for local microstructural adaptions. In this work an evaluation concept to analyze the thermal melt pool characteristics based on a high-speed camera is developed. The thermal radiation intensity of the melt pool is used to derive the thermal gradient and combined with an image rate of 41,000 fps the solidification rate is derived. The developed approach provides local data of the solidification for ML-based process adaptions but also serves for part individual quality assurance tasks

Process-controlled Grading of the Young's Modulus of AlSi10Mg Components Using L-PBF

Laser Powder Bed Fusion (L-PBF) increases freedom in the design of components and is therefore well suited for the manufacturing of complex geometries tailored to their function. In addition, it is possible to influence the microstructural characteristics of the components by varying the process parameters during the L-PBF process. This allows shifting the load from areas with high stresses to less heavily loaded areas in order to exploit the full potential of the material. For this purpose, the process window in which the Young‘s modulus of the material AlSi10Mg can be varied was investigated. Subsequently, test geometries were analyzed by finite element method with respect to their critical component areas and a design for grading the Young‘s modulus to distribute stress more uniformly was developed. These specimens were then manufactured and compared with components manufactured using homogeneous parameters

Dimensionless Process Development for Lattice Structure Design in Laser Powder Bed Fusion

Laser powder bed fusion enables the fabrication of complex components such as thin-walled cellular structures including lattice or honeycomb structures. Numerous manufacturing parameters are involved in the resulting properties of the fabricated component and a material and machine-dependent process window development is necessary to determine a suitable process map. For cellular structures the thickness, which correlates with the process parameters, directly influences the mechanical properties of the component. Thus, dimensionless scaling laws describing the correlation between strut thickness, process parameters, and material properties enable predictive lattice structure design for laser powder bed fusion. This contribution develops material independent dimensionless allometric scaling laws for both single track and contour exposure to enable process-driven design of lattice structures in laser powder bed fusion. The theory derived with dimensional analysis is validated for the powder alloys stainless steel alloy 1.4404, nickel alloy 2.4856, aluminum alloy AlSi10Mg and Scalmalloy AlMgSc. The results can be used for the process-driven design of lattice structures and dense material obtaining high precision in the micrometer range or economic production with high melt pool width