F11
Additive Manufacturing 2

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10:40
conference time (CEST, Berlin)
Simulation of Metal Binder Jetting Sintering for Design and Manufacture of High Volume AM
27/10/2021 10:40 conference time (CEST, Berlin)
Room: F
K. Abburi Venkata, D. Paff (Simufact Engineering GmbH - Part of Hexagon, DEU)
K. Abburi Venkata, D. Paff (Simufact Engineering GmbH - Part of Hexagon, DEU)
Metal Binder Jetting (MBJ) stands out among other Additive Manufacturing (AM) technologies for metals due to its high speed, material flexibility and high volumetric output. It is also an attractive alternative to fusion based AM processes as it does not result in large residual stresses in the part from the build process. In MBJ, a binder is selectively deposited onto the powder bed, bonding these areas together to form a solid part one layer at a time. The printed part from MBJ process, referred to as the green part, is extremely fragile due to low relative density (~50-60%). Therefore, post-processing of MBJ components is essential to achieve the required density in the final part such that it is suitable for service. Sintering is considered the most critical step in the MBJ process-chain to assure acceptable final density and dimensional accuracy in the final component. Sintering often leads to anisotropic shrinkage and distortion in parts which can be detrimental to quality. Therefore, a reliable numerical simulation tool that can accurately predict the warpage and anisotropic shrinkage in the final part considering the essential material and process characteristics is invaluable for the commercial success and industrialisation of MBJ process. In this paper, a finite element (FE) simulation of sintering of an industrial component is presented. Anisotropic initial relative density distribution arising from the build process and anisothermal heat transfer in the oven are considered in the simulation and the final shrinkage and deformation of the part during sintering process is predicted. The predicted shrinkage and deformation are validated with experimental measurements on actual components fabricated using MBJ process. Good agreement is observed between the predicted shrinkage and distortion with that of experimental results. Finally, based on the predicted shrinkage/deformation, a shape-compensation algorithm has been applied to pre-compensate the part such that after sintering the component adheres to the required quality standards.
Metal Binder Jetting, Sintering, Finite Element Simulation, Anisotropic shrinkage, Experimental validation, Shape compensation
11:00
conference time (CEST, Berlin)
Validation of Metal Additive Manufacturing Simulation Focusing on Printing Failures and Optimization
27/10/2021 11:00 conference time (CEST, Berlin)
Room: F
B. Dóczi (Knorr-Bremse Vasúti Jármü Rendszerek Hungária Kft., HUN); N. Keller (Additive Works, DEU)
B. Dóczi (Knorr-Bremse Vasúti Jármü Rendszerek Hungária Kft., HUN); N. Keller (Additive Works, DEU)
Possible applications of metal additive manufacturing (AM) in the railway industry have been continuously assessed at the Knorr-Bremse (KB) Group in recent years, as metal AM could deliver significant benefits compared to traditional manufacturing processes when it comes to lower volume production, generative design and design simplification. Although the potential is huge, there are still obstacles to be overcome to realize AM applications on an industrial level. As the process is basically a welding process, FEM-based process simulations created using the inherent strain method became state-of-the-art. However, material knowledge for accurate prediction of stresses and distortion, for example, to compensate deformation, predict process failures, or optimize supports, is crucial. Thus, there is the need to consider further material-related effects that occur on preheated processes and real geometries. Metal additive manufacturing has been performed at Knorr-Bremse Rail Systems Budapest since 2018 with an EOS M290 machine. By applying laser powder bed fusion (LPBF), several different parts have been manufactured from the aluminium alloy, AlSi10Mg, since then. Although extensive metal AM experience has been built, printing failures have occasionally been experienced. The goal of avoiding printing failures and enabling first-time-right metal AM process was formulated. To support this goal, an AM simulation validation project was initiated to assess the capabilities of the Amphyon software to predict printing failures and optimize print jobs. An advanced calibration method for the inherent strain approach was developed, including the efficient modelling of the creep effect of AlSi10Mg. Print jobs with printing failures encountered in practice were collected along with high-resolution images created during the printing processes. The validation of simulation on printing failure prediction was performed by comparing simulation results of these print jobs with the high-resolution images, focusing on the primary printing failures of re-coater interference and support failure. The print job optimization capabilities (i.e., orientation assessment, support structure optimization, pre-deformation of geometry) were validated via the actual 3D printing of jobs optimized by simulation.
metal AM, LPBF, inherent strain approach, printing failures, optimization, validation
11:20
conference time (CEST, Berlin)
Smart “Additive Manufacturing Using Metal Pilot Line”
27/10/2021 11:20 conference time (CEST, Berlin)
Room: F
O. Tabaste (MSC Software France - groupe Hexagon, FRA); E. Onillon (CSEM, CHE); S. Bigot (School of Engineering, Cardiff University, GBR)
O. Tabaste (MSC Software France - groupe Hexagon, FRA); E. Onillon (CSEM, CHE); S. Bigot (School of Engineering, Cardiff University, GBR)
Metal Additive Manufacturing (AM) has continuously attracted increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, it appears that technology readiness level remains low enough to prevent its mainstream adoption throughout the industry. This can be explained by few limiting factors: • Design benefits have been negatively impacted by a common trend to enhance manufacturing of a conventional design, rather that design for an optimized additive approach. This reflects a shortage of user experience in respect of innovative fabricability while designers and product engineers’ know-how do not necessarily match the expertise of fabrication engineers tracing all machine providers evolutions. • Lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance. To overcome these limitations, MANULEA project aims at introducing a Smart Manufacturing capability empowering all stakeholders of the product design to manufacture workflow with a mean to share and capitalize on all digital and physical expertise available hanks to: • A common dashboard associated to domain databases to share knowledge and expertise, across the various silos from design and simulation, to the shopfloor • A pilot line, encompassing a digital twin coupling traditional simulation methods with Data analytics elaborated on top of real-time and in-line machine monitoring Combination of these capabilities aims at offering a documented decision driven platform improving the productivity and quality while decreasing the lead time, all mandatory for automotive, aeronautic, energy and medical sectors: the use cases chosen to execute the project. This paper intends to illustrate the project methodology and its development and implementation progresses from the software and machine feedback loop perspective, in respect of initial objectives. MANUELA is funded. under H2020 framework by Grant # 820774.
Metal Additive Manufacturing; Digital Twin; machine learning ;Industry4.0
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