The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited despite the significant progress made in the development of the process chain. One of the key obstacles i...

Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing

A rotary electric machine includes: a stator core fixed to a casing; a coil end formed on the stator core; a bracket fixed to the casing; an arm that extends from the bracket and that is made of metal and that has elasticity; a holder that is attached to the arm and that is made of resin; and a sensor that is attached to the holder and that contacts the coil end to detect temperature of the coil end.

Rotary electric machine

Despite the recent progress in additive manufacturing (AM) process and technology, challenges in the repeatability and reproducibility of AM parts still hinders the adoption of this technique in many industries. This is particularly difficult when a part is qualified on a particular part on a certain machine using optimised parameters. If a manufacturer wishes to expand production to multiple machines, the ability to translate these optimised parameters to different machines much be understood. In this study, four different metal L-PBF printers were used to produce 316L tensile testing samples using the same processing parameters and metal powder supplied from a single batch from the same supplier. In addition to the analysis of the correlation between the input parameters and the output measures, this study reports that despite the same set process parameters, there is significant variations were found in the mechanical performance and properties of the AM samples produced on the different L-PBF metal additive manufacturing machines. For the range of the input processing parameters and the resulting input volumetric energy density applied of 21–37 J/mm3, values of (4–42)%, (200–716) MPa, and (52–214) GPa were obtained for the elongation, ultimate tensile strength and elastic modulus on additively manufactured 316L samples respectively.

Comparison of the porosity and mechanical performance of 316L stainless steel manufactured on different laser powder bed fusion metal additive manufacturing machines

Mutual comparison of acoustic, pyrometric and thermographic laser powder bed fusion monitoring

Powder bed fusion (PBF)
process is a metal additive manufacturing process, which can build parts with
any complexity from a wide range of metallic materials. Research in the PBF
process predominantly focuses on the impact of a few parameters on the ultimate
properties of the printed part. The lack of a systematic approach to optimizing
the process parameters for a better performance of given material results in a
sub-optimal process limiting the potentialof the application. This process
needs a comprehensive study of all the influential parameters and their impact
on the mechanical and microstructural properties of a fabricated part.
Furthermore, there is a need to develop a quantitative system for mapping the
material properties and process parameters with the ultimate quality of the
fabricated part to achieve improvement in the manufacturing cycle as well as
the quality of the final part produced by the PBF process. To address the
aforementioned challenges, this research proposes a framework to optimize the process
for 316L stainless steel material. This framework characterizes the influence
of process parameters on the microstructure and mechanical properties of the
fabricated part using a series of experiments. These experiments study the
significance of process parameters and their variance as well as study the microstructure
and mechanical properties of fabricated parts by conducting tensile, impact,
hardness, surface roughness, and densification tests, and ultimately obtain the
optimum range of parameters. This would result in a more complete understanding
of the correlation between process parameters and part quality. Furthermore,
the data acquired from the experimentsare employed to develop an intelligent
parameter suggestion multi-layer feedforward (FF) backpropagation (BP)
artificial neural network (ANN). This network estimates the fabrication time
and suggests the parameter setting accordingly to the user/manufacturers
desired characteristics of the end-product. Further, research is in progress to
evaluate the framework for assemblies and complex part designs and incorporate
the results in the network for achieving process repeatability and consistency.

A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)

The grounding quality of an electric machine should be better monitored. Therefore, an electric machine is proposed, comprising a stator (1), a rotor (2), a shaft (3), to which the rotor (2) is fastened, and a grounding device (5) for grounding the shaft. The electric machine also has a measuring device (6) for measuring a ground current of the grounding device (5) and for providing a corresponding measured value. The electric machine also has a monitoring device (7) for monitoring the grounding function of the grounding device (5) on the basis of the measured value. In this way, the state of a grounding brush in regard to soiling and wear, for example, can be continuously monitored.

Electric machine for which the grounding function is monitored and method

The Laser Powder Bed Fusion (L-PBF) process is being adopted in different industrial fields. However, L-PBF currently lacks process reproducibility and quality. Hence, quality monitoring techniques need to be adopted in order to reduce the process variability and to ensure a high-quality process. Accordingly, this work proposes a quality monitoring approach based on machine learning which links the optical signal of a layer to the density of the final part. The approach consists of selecting relevant statistical features from optical data and validating these features by assessing their ability in predicting the different density classes of different products. Afterwards, the approach is compared to a new deep learning framework that allows predicting a part density from the corresponding raw optical signals. This comparison allows assessing the relevance of the identified statistical features. The proposed monitoring approach is applied on cubical specimens produced with different process parameters, and the results are then discussed and analyzed.

Optical process monitoring for Laser-Powder Bed Fusion (L-PBF)

A method and device for assessing damage to a rolling bearing caused by bearing currents, in particular high-frequency bearing currents, in an electric machine which is electrically connected to an inverter, in particular to an inverter having a DC voltage link circuit, is described. The rolling bearing has a lubrication gap between an inner bearing ring and a rolling body and between an outer bearing ring and the rolling body. The method includes detecting the energy of an electric discharge event in the lubrication gaps, searching for a coincident event having a frequency above one GHz indicative of a damaging bearing current event, detecting a frequency of occurrence of discharge events, and evaluating the discharge events by correlating the frequency of occurrence and the energy of the discharge events.

Method and device for assessing the damage to rolling bearings, in particular in inverter-fed electric machines

Die Erfindung bezieht sich u. a. auf ein Verfahren zur Lagerstromuberwachung einer elektrischen Maschine (10). Es wird mit einer in einem Abstand (d) zu einer Welle angeordneten Elektrode (100) und mit der Welle - aufgrund des Spalts (S) zwischen Elektrode und Welle - eine Messkapazitat (C) gebildet und ein elektrischer Verschiebestrom (i) gemessen, der im Falle einer zeitlichen Anderung der zwischen der Welle und dem Gehause anliegenden Spannung (Ug) durch die Messkapazitat fliest. Ein einen Lagerstromfluss anzeigendes Messsignal (Ms) wird erzeugt, wenn der Verschiebestrom oder eine mit dem Verschiebestrom gebildete Messgrose ein vorgegebenes Auslosekriterium erfullt. Vorzugsweise weist die Elektrode eine kreisformige Innenkontur auf, so dass der Spalt ringformig ist. Durch eine ringformige Innenkontur wird eine Fehlerkompensation im Falle einer Unwucht der Welle erreicht, weil der Faktor dC/dt zumindest weitgehend konstant bleibt. Aufgrund der beruhrungslosen Messung des Verschiebestroms kann auf Kontaktbursten zur Kontaktierung der Welle verzichtet werden. Das Verfahren ist unabhangig davon einsetzbar, ob die Lager gegenuber dem Maschinengehause isoliert sind oder nicht.

Verfahren und Anordnung zur Lagerstromüberwachung einer elektrischen Maschine

It should be possible to measure the bearing current, in particular in electric machines or load machines, with the least possible installation effort. Therefore, a sensor device for installing against or onto a shaft (3) of an electric machine is provided. Said sensor device has a detection device (7, 11) for detecting a bearing current of the electric machine. The sensor device also has an energy converting device (7, 8), which is assembled with the detection device to form an exchangeable module, for converting mechanical energy of the shaft (3) into electrical energy for the detection device.

Oliver Theile

Papers

Electric machine for which the grounding function is monitored and method

Optical process monitoring for Laser-Powder Bed Fusion (L-PBF)

Method and device for assessing the damage to rolling bearings, in particular in inverter-fed electric machines

Verfahren und Anordnung zur Lagerstromüberwachung einer elektrischen Maschine

Bearing current sensor device having an energy converter