Background
Additive manufacturing is defined as a material joining process, whereby a product can be directly fabricated from its 3D model, usually layer upon layer. Comparing to traditional manufacturing technologies such as CNC machining or casting, AM processes have several unique capabilities. It enables the fabrication of parts with a complex shape as well as complex material distribution. These unique capabilities significantly enlarge the design freedom for designers. However, they also bring a big challenge. Traditional Design for manufacturing (DFM) rules or guidelines deeply rooted in designers’ mind and severely restrict designers to further improve product functional performance by taking advantages of these unique capabilities brought by AM processes. Moreover, traditional feature-based CAD tools are also difficult to deal with irregular geometry for the improvement of functional performance. To solve these issues, design methods or tools are needed to help designers to take full advantages of design freedom provide by AM processes. These design methods or tools can be categorized as Design for Additive Manufacturing.Methods
Topology optimization
Topology optimization is a type of structural optimization technique which can optimize material layout within a given design space. Compared to other typical structural optimization techniques, such as size optimization or shape optimization, topology optimization can update both shape and topology of a part. However, the complex optimized shapes obtained from topology optimization are always difficult to handle for traditional manufacturing processes such as CNC machining. To solve this issue, additive manufacturing processes can be applied to fabricate topology optimization result. However, it should be noticed, some manufacturing constraints such as minimal feature size also need to be considered during the topology optimization process. Since the topology optimization can help designers to get an optimal complex geometry for additive manufacturing, this technique can be considered one of DfAM methods.Multiscale structure design
Due to the unique capabilities of AM processes, parts with multiscale complexities can be realized. This provides a great design freedom for designers to use cellular structures or lattice structures on micro or meso-scales for the preferred properties. For example, in the aerospace field, lattice structures fabricated by AM process can be used for weight reduction. In the bio-medical field, bio-implant made of lattice or cellular structures can enhance osseointegration.Multi-material design
Parts with multi-material or complex material distribution can be achieved by additive manufacturing processes. To help designers to take use of this advantage, several design and simulation methods has been proposed to support design a part with multiple materials or Functionally Graded Materials . These design methods also bring a challenge to traditional CAD system. Most of them can only deal with homogeneous materials now.Design for mass customization
Since additive manufacturing can directly fabricate parts from products’ digital model, it significantly reduces the cost and leading time of producing customized products. Thus, how to rapidly generate customized parts becomes a central issue for mass customization. Several design methods have been proposed to help designers or users to obtain the customized product in an easy way. These methods or tools can also be considered as the DfAM methods.Parts consolidation
Due to the constraints of traditional manufacturing methods, some complex components are usually separated into several parts for the ease of manufacturing as well as assembly. This situation has been changed by the using of additive manufacturing technologies. Some case studies have been done to shows some parts in the original design can be consolidated into one complex part and fabricated by additive manufacturing processes. This redesigning process can be called as parts consolidation. The research shows parts consolidation will not only reduce part count, it can also improve the product functional performance. The design methods which can guide designers to do part consolidation can also be regarded as a type of DfAM methods.Lattice structures
Lattice structures is a type of cellular structures (i.e. open). These structures were previously difficult to manufacture, hence was not widely used. Thanks to the free-form manufacturing capability of additive manufacturing technology, it is now possible to design and manufacture complex forms. Lattice structures have high strength and low mass mechanical properties and multifunctionality. These structures can be found in parts in the aerospace and biomedical industries. It has been observed that these lattice structures mimic atomic crystal lattice, where the nodes and struts represent atoms and atomic bonds, respectively, and termed as meta-crystals. They obey the metallurgical hardening principles (grain boundary strengthening, precipitate hardening etc.) when undergoing deformation. It has been further reported that the yield strength and ductility of the struts (meta-atomic bonds) can be increased drastically by taking advantage of the non-equilibrium solidification phenomenon in Additive Manufacturing, thus increasing the performance of the bulk structures.Thermal issues in design
For AM processes that use heat to fuse powder or feedstock, process consistency and part quality are strongly influenced by the temperature history inside the part during manufacture, especially for metal AM. Thermal modelling can be used to inform part design and the choice of process parameters for manufacture, in place of expensive empirical testing.Optimal design for additive manufacturing
Additively manufactured metallic structures with the same (macroscopic) shape and size but fabricated by different process parameters have strikingly different microstructures and hence mechanical properties. The abundant and highly flexible AM process parameters substantially influence the AM microstructures. Therefore, in principle, one could simultaneously 3D-print the (macro-)structure as well as the desirable microstructure depending on the expected performance of the specialized AM component under the known service load. In this context, multi-scale and multi-physics integrated computational materials engineering (ICME) for computational linkage of process-(micro)structure-properties-performance (PSPP) chain can be used to efficiently search an AM design subspace for the optimum point with respect to the performance of the AM structure under the known service load. The comprehensive design space of metal AM is boundless and high dimensional, which includes all the possible combinations of alloy compositions, process parameters and structural geometries. However, always a constrained subset of the design space (design subspace) is under consideration. The performance, as the design objective, depending on the thermo-chemo-mechanical service load, may include multiple functional aspects, such as specific energy absorption capacity, fatigue life/strength, high temperature strength, creep resistance, erosion/wear resistance and/or corrosion resistance. It is hypothesized that the optimal design approach is essential for unraveling the full potential of metal AM technologies and thus their widespread adoption for production of structurally critical load-bearing components.References
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