Atomic-scale grain boundary engineering to overcome hot-cracking in additively-manufactured superalloys

There are still debates regarding the mechanisms that lead to hot cracking in parts build by additive manufacturing (AM) of non-weldable Ni-based superalloys. This lack of in-depth understanding of the root causes of hot cracking is an impediment to designing engineering parts for safety-critical applications. Here, we deploy a near-atomic-scale approach to investigate the details of the compositional decoration of grain boundaries in the coarse-grained, columnar microstructure in parts built from a non-weldable Ni-based superalloy by selective electron-beam melting. The progressive enrichment in Cr, Mo and B at grain boundaries over the course of the AM-typical successive solidification and remelting events, accompanied by solid-state diffusion, causes grain boundary segregation induced liquation. This observation is consistent with thermodynamic calculations. We demonstrate that by adjusting build parameters to obtain a fine-grained equiaxed or a columnar microstructure with grain width smaller than 100 $\mu$m enables to avoid cracking, despite strong grain boundary segregation. We find that the spread of critical solutes to a higher total interfacial area, combined with lower thermal stresses, helps to suppress interfacial liquation.Comment: Accepted version at Acta Materiali

Parts internal structure definition using non-uniform patterned lattice optimization for mass reduction in additive manufacturing

Today, being able to generate and produce shapes that fit mechanical and functional requirements and having as low as possible mass is crucial for aerospace and automotive applications. Besides, the rise of new additive manufacturing technologies has widened the possibilities for designing and producing complex shapes and internal structures. However, current models, methods and tools still represent a limitation to that new horizon of printable shapes. This paper addresses the way internal lattice structures can be generated and optimized to reduce the mass of a product. A new framework is introduced that allows the modeling and optimization of non-uniform patterned lattice structures. Using non-uniform structures, additional degrees of freedom are introduced and allow the definition of a wide variety of shapes which can better fit the requirements. First, a non-uniform patterned lattice structure is generated using the results of an initial finite element analysis. This initial structure is then optimized while iteratively removing the beams considered as useless with respect to a user-specified mechanical criteria. At each iteration, the lattice structure is sent to a finite element solver that returns the von Mises stress map used to drive the simplification process. Here, the simulations are performed on the wireframe lattice structures to speed up the optimization loops. Once this process is completed, the final structure is no longer fully patterned, but it is re-organized to reduce the mass while satisfying the mechanical criteria. This approach is illustrated with examples coming from our prototype software

Parts internal structure definition using lattice patterns optimization for mass reduction in additive manufacturing

With the rise of additive manufacturing, complex internal structure optimization is now a relevant topic. Additive manufacturing allows designers and engineers to go further in their modeling, designing and optimization process, allowing new complex shapes to be produced, including the optimization of their internal structure. However modeling, design and optimization tools still represent a limitation to that new horizon of printable shapes. In this article, we define the framework in term of new designs, 3D modeling and optimization approach dedicated to the shape definition of patterned (or organized) lattice structures1 produced using additive manufacturing processes. The goal being to generate shapes that fit the mechanical requirements with an “as reduced as possible” mass, this issue is still today a niche market for Aerospace and Automotive, but could soon lead to a wider range of applications. Optimizing topology can be slow, so we will show a way of reducing computation time by using relative criteria for removing material. This new approach is based on the use of organized lattice structures to allow a wide range of shapes, thus opening the field for finding better optimized shapes. Once the patterned lattice structure is defined, it is send to a Finite Element solver software that returns the constraints and/or displacements map. This is then used as a basis for a statistical calculus that determines the elements that can or cannot be removed from the lattice. After a few iterations, the general structure is no longer patterned, but organized in a way that suits its mechanical environment, allowing lighter general structure and ensuring its rigidity. This approach is illustrated with examples coming from a prototype software

Towards additive manufacturing of intermediate objects (AMIO) for concepts generation

According to an analysis of existing Design For Additive Manufacturing (DFAM)methods,we first highlight that they present limits regarding product innovation. This paper then presents a creative approach to be integrated in the early stages of DFAMmethods. Two case studiesAand B are presented as the experimental application of the first stage of our creative approach. The results of these case studies highlight that designers need a newkind of IntermediateRepresentation (IR), especially to represent dynamic features. To address this need, we introduce the concept of AMIO Additive Manufacturing of Intermediate Objects. This new kind of IR is an expected output of the ideas generation stage. These intermediate objects are meant to be manipulated by all the design stakeholders, as an input for the concept generation stage, to enhance the generation of creative concepts for additive manufacturing

Lattice structure lightweight triangulation for additive manufacturing

Additive manufacturing offers new available categories of geometries to be built. Among those categories, one can find the well developing field of lattice structures. Attention has been paid on lattice structures for their lightweight and mechanical efficiency ratio, thus leading to more optimized mechanical parts for systems. However this lightness only holds true from a mass related point of view. The files sent to additive manufacturing machines are quite large and can go up to such sizes that machines can freeze and get into malfunction. This is directly related to the lattice structures tendency to be of a high geometric complexity. a large amount of vertices and triangles is necessary to describe them geometrically, thus leading to larger file sizes. With the increasing use of lattice structures, the need for their files to be lighter is also rising. This paper aims at proposing a method for tessellating a certain category of such structures, using topologic and geometric criteria to generate as few as possible triangles, thus leading to lightweight files. The triangulation technique is driven by a chordal error that control the deviation between the exact and tessellated structures. It uses interpolation, boolean as well as triangulation operators. The method is illustrated and discussed through examples from our prototype software

Design for Additive Manufacturing : Supporting Intrinsic-Motivated Creativity

Emotional aspects and designers’ motivations in Design For Additive Manufacturing are rarely studied. Still, as they can influence creative behaviors, it is worth of interest to draw some bases for a relation between designers’ motivations and the field of Additive Manufacturing. This paper aims at identifying the motivations that push designers to deal with AM in their practice. We have highlighted that they experience some extrinsic motivations: technical improvements, economics and social environments pressures. We also notice that creative designers, apart from AM, usually experience some intrinsic motivations and, moreover, that it exists an ideal state to generate creative concepts: the Flow. To support creative designers in DFAM in reaching the Flow, we then identified 4 key levers through the potential of AM: the newness of AM processes, the needed skill of 3D modelling, the investigation of new shape grammars and finally the opportunity of embodying concepts into physical objects. To benefit from this potential, we assume that designers’ intrinsic motivations should be supported: we identified three required conditions. The first one is the use of a proper vocabulary i.e the expression Additive Manufacturing instead of 3D Printing. The second one is the development of a design process which integrates a creative approach. The third condition is the use of AM objects as experience triggers during creative sessions to arise positive emotions

An optimal sensor placement algorithm taking into account diagnosability specifications

International audienceSuitable installed sensors in a industrial process is a necessary condition for fault diagnosis. Sensor placement for diagnosis purposes is to study which process variables have to be measured to satisfy diagnosis specifications (detectability, discriminability and diagnosability). This paper presents a method based on the study of the structural model properties and the Dulmage-Mendelsohn decomposition. Due to the use of structural models, the proposed approach can be applied to a wide variety of system (linear, algebraic, dynamics, etc.). Assuming that the cost of placing a sensor for each possible variable is defined, this method finds the minimal cost sensor configuration according to the diagnosability criteria. This method does not require the computation of testable subsystems

An Optimal Sensor Placement Algorithm taking into account Diagnosability Specifications

Suitable installed sensors in a industrial process is a necessary condition for fault diagnosis. Sensor placement for diagnosis purposes is to study which process variables have to be measured to satisfy diagnosis specifications (detectability, dis-criminability and diagnosability). This paper presents a method based on the study of the structural model properties and the Dulmage-Mendelsohn decomposition. Due to the use of structural models, the proposed approach can be applied to a wide variety of system (linear, algebraic, dynamics, etc.). Assuming that the cost of placing a sensor for each possible variable is defined, this method finds the minimal cost sensor configuration according to the diagnosability criteria. This method does not require the computation of testable subsystem