This paper contributes to a better understanding of processing the nickel-based superalloy IN738LC using selective laser melting (SLM). Initially, the basic workability of IN738LC using SLM is ...demonstrated. Subsequently, a comparison between a Gaussian and a doughnut profile is carried out, and clear correlations between the choice of process parameters and the resulting imperfections depending on the chosen laser beam profile are shown. Electron backscatter diffraction (EBSD) measurements show a significant influence of scan strategy and build direction on the texture of the sample, independent of the used laser beam profile. Regardless of the laser beam profile, two contradicting trends complicate the defect-free processing of IN738LC, i.e. a reduction in the crack density can only be realized with increasing porosity. Through microstructural investigations, observed hot cracks are identified to be solidification cracks. Based on a broad experimental study in combination with a numerical solidification study, a theory about the crack initiation mechanism is presented. Finally, by using atom probe tomography (APT), the element zirconium is confirmed as a possible reason for the occurrence of solidification cracks.
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•Porosity and crack density exhibit an inverse relationship.•An analysis about the chemical composition of a grain boundary is shown.•The metallurgical analysis identifies zirconium as a possible reason for hot cracking.•Cross-hatch scan strategy and directional solidification cause a cubic texture.
Human–robot collaboration is enabled by the digitization of production and has become a key technology for the factory of the future. It combines the strengths of both the human worker and the ...assistant robot and allows the implementation of an varying degree of automation in workplaces in order to meet the increasing demand of flexibility of manufacturing systems. Intelligent planning and control algorithms are needed for the organization of the work in hybrid teams of humans and robots. This paper introduces an approach to use standardized work description for automated procedure generation of mobile assistant robots. A simulation tool is developed that implements the procedure model and is therefore capable of calculating different objective parameters like production time or ergonomics during a production cycle as a function of the human–robot task allocation. The simulation is validated with an existing workplace in an assembly line at the Volkswagen plant in Wolfsburg, Germany. Furthermore, a new method is presented to optimize the task allocation in human–robot teams for a given workplace, using the simulation as fitness function in a genetic algorithm. The advantage of this new approach is the possibility to evaluate different distributions of the tasks, while considering the dynamics of the interaction between the worker and the robot in their shared workplace. Using the presented approach for a given workplace, an optimized human–robot task allocation is found, in which the tasks are allocated in an intelligent and comprehensible way.
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•A microphone, a camera and a pyrometer are compared by synchronous application.•The microphone exhibits the highest sensitivity but is also the most susceptible.•Position-independent ...sensitivity has been demonstrated for the optical sensors.•Microphone placement and compensation recommendations are given.•Scan direction dependent shield gas interaction was uncovered by all sensors.
This paper compares three distinctive sensors for laser powder bed fusion metal additive manufacturing process monitoring. A microphone for airborne acoustic emissions, an on-axis two-colour pyrometer for melt pool temperature measurement and an off-axis thermographic camera are simultaneously applied. They are challenged with a large build area to investigate their robustness and sensitivity. This paper does not assess the sensors’ ability to detect specific process flaws, but instead gives a common ground comparison of general sensor characteristics. The camera provides a descriptive result in form of a heat-map, while it exhibits a lack of sensitivity. In contrast, the microphone presents a sensitivity up to 40 times higher than the camera and is still 15 times more sensitive than the pyrometer. However, with this comes increased susceptibility; its signal strength is strongly dependent on the distance to the melt pool as a result of frequency dependent dissipation. The pyrometer’s signal is sensitive enough for relevant process deviations to be uncovered, while being robust towards different sensing distances. Recommendations are given for successful implementation of the sensors. Additionally, novel process phenomena were uncovered: an interaction of the scanning direction with the shielding gas is discussed, plus insights regarding overhang scanning are acquired.
Selective laser melting of high γ′ strengthened superalloys such as IN738LC is of huge interest for stationary gas turbine applications. In order to identify the influence of the powder ...characteristics on SLM processing, several batches of commercially available IN738LC powder made by gas and water atomization were characterized with respect to particle size distribution, morphology and chemical composition. The different powders were processed under equal conditions by selective laser melting (SLM) and the build quality was assessed by quantitative image analysis. Samples made out of the water atomized (WA) powder showed a higher porosity after SLM compared to the spherical gas atomized (GA) powders due to the greater irregular morphology of the powder. The porosity of the gas atomized powder batches was found to be influenced by the flow behavior of the powder. Large differences were observed for the hot cracking susceptibility of the different powder batches during SLM processing. The cracking density was correlated to the chemical composition and it was found that the minor element Si has a large influence on the SLM processability of the IN738LC powder. A strict control of this element is important to decrease the hot cracking tendency and extends the SLM processing window of this alloy.
Additive Manufacturing (AM) is close to become a production technique changing the way of part fabrication in future. Enhanced complexity and personalized features are aimed. The expectations in AM ...for the future are enormous and betimes it is considered as kind of the next industrial revolution. Laser Sintering (LS) of polymer powders is one component of the AM production techniques. However materials successfully applicable to Laser Sintering (LS) are very limited today. The presentation picks up this topic and gives a short introduction on the material available today. Important factors of polymer powders, their significance for effective LS processing and analytical approaches to access those values are presented in the main part. Concurrently the exceptional position of polyamide 12 powders is this connection is outlined.
Grinding burn is a common problem in high-performance industrial manufacturing. Usually destructive (e.g., nital etching) or non-destructive (e.g., Barkhausen noise analysis) methods are used to ...detect these unwanted changes of the workpiece properties. In recent years, different investigations for the in-process monitoring of grinding burn are conducted in a research environment. One main drawback of most of these detection methods is the lack of robustness and transferability. Therefore, this study provides a new feature-based approach to detect thermal damages in external cylindrical rough grinding using machine learning. To evaluate the robustness properties of the learning algorithm, a large series of experiments is conducted comprising different process parameters and system variables such as workpiece materials, grain sizes and bonding types. Using the burn threshold diagram, a linear separation boundary for parts with and without thermal damage is identified for one process setup. Due to the missing generalization property of the burn threshold analysis, multiple machine learning models are trained and optimized according to three levels of generalization. After achieving an accuracy of more than
98
%
for a constant process setup, the model is expanded to make predictions independently from the values of the system variables showing only a slightly reduced accuracy. In addition, the obtained model is also able to generalize to new values of the system variables by maintaining the high recall of the classification model.
In metal additive manufacturing, moving heat sources cause spatial and time-dependent variations of temperature and strain that can lead to part distortions. Distortion prediction and optimized ...deposition parameters can increase the dimensional accuracy of the generated components. In this study, an analytical approach for modeling the effect of clad height and substrate thickness is experimentally validated. Additionally, the influence of the scanning pattern as a function of clad height and substrate thickness is determined experimentally. The analytical model is based on the cool-down phase mechanism and assumes the formation of constant thermal shrinking forces for each deposited layer. The model accurately predicts longitudinal cantilever distortion after experimental calibration when compared with similar experimental conditions. For multi-layer deposition, the scanning pattern has the largest influence on distortion for thin-walled substrates. An optimized deposition strategy with longitudinal scanning vectors leads to a distortion reduction of up to 86%. The results highlight the potential of mechanical modeling and scanning strategy optimizations to increase the shape accuracy for industrial applications in the field of additive manufacturing.
With increasing industrial interest and significance of the selective laser melting the importance for profound process knowledge increases so that new materials can be qualified faster. Also it is ...the basis for an educated evaluation of possible process innovations. Therefore a 3D numerical model for the selective laser melting process is presented that allows a detailed look into the process dynamics at comparably low calculation effort. It combines a finite difference method with a combined level set volume of fluid method for the simulation of the process and starts with a homogenized powder bed in its initial configuration. The model uses a comprehensive representation of various physical effects like dynamic laser power absorption, buoyancy effect, Marangoni effect, capillary effect, evaporation, recoil pressure and temperature dependent material properties. It is validated for different process parameters using cubic samples of stainless steel 316L and nickel-based superalloy IN738LC. The results show the significance of evaporation and its related recoil pressure for a feasible prediction of the melt pool dynamics. Furthermore a possible way to reduce the times and costs for material qualification by using the simulation model to predict possible process parameters and therefore to reduce the necessary experimental effort for material qualification to a minimum is shown.
The improvement of industrial grinding processes is driven by the objective to reduce process time and costs while maintaining required workpiece quality characteristics. One of several limiting ...factors is grinding burn. Usually applied techniques for workpiece burn are conducted often only for selected parts and can be time consuming. This study presents a new approach for grinding burn detection realized for each ground part under near-production conditions. Based on the in-process measurement of acoustic emission, spindle electric current, and power signals, time-frequency transforms are conducted to derive almost 900 statistical features as an input for machine learning algorithms. Using genetic programming, an optimized combination between feature selector and classifier is determined to detect grinding burn. The application of the approach results in a high classification accuracy of about 99% for the binary problem and more than 98% for the multi-classdetection case, respectively.
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