Functionally graded materials (FGMs) represent a class of novel materials in which compositions/constituents and/or microstructures gradually change along single or multiple spatial directions, resulting in a gradual change in properties and functions which can be tailored for enhanced performance. FGMs can be fabricated using a variety of well-established processing methods; however, it is also known that there are inherent drawbacks to existing synthesis methods. As an emerging technology that provides a high degree of control over spatial resolution, additive manufacturing (AM) provides an intriguing pathway to circumvent the drawbacks of currently available methods. AM involves the selective deposition of individual layers of single or multiple materials, and as such it offers the potential of local control of composition and microstructure in multiple dimensions; such process conditions, in principle, can be tailored to construct complex FGMs with multi-dimensional and directional gradient structures. In this review paper, our current understanding of important issues, such as modeling, processing, microstructures and mechanical properties, as related to FGMs produced via AM, are described and discussed in an effort to assess the state of the art in this field as well as to provide insight into future research directions.

Additive manufacturing of functionally graded materials: A review

A simulation-based research on carbon emission mitigation strategies for green container terminals

The port is an important node in logistics, and its energy consumption constitutes a considerable proportion of the transportation industry. In port logistics, not only does the energy consumption generate carbon emissions, but other business activities do as well. This paper firstly characterizes the sources of carbon emissions and the basic elements in the port system, and proposes the concept of a port-integrated logistics system. Secondly, a case study of The Port of Shenzhen is conducted and a method is provided to measure the carbon emissions in the port-integrated logistics system. This paper then suggests two approaches to reducing carbon emissions, and their economic and environmental benefits are compared. Finally, some policies are put forward to reduce carbon emissions, such as improving the efficiency of loading and unloading, and replacing the heavy fuel oil by low sulfur fuel oil and shore power. The proposed method of carbon emission reduction for port-integrated logistics systems can be generalized for the analysis of various types of ports.

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A Carbon Emission Evaluation for an Integrated Logistics System—A Case Study of the Port of Shenzhen

Tool-path planning has a considerable impact on the quality of components printed by fused filament fabrication (FFF). This research proposes a path generation strategy based on the orientations of the maximum principal stresses. According to stress calculations from finite element analysis (FEA) of the components, tool-paths, which are programmed as parallel to the maximum principal stress directions, are constructed with the depth-first search (DFS) method and a connection criterion. The breakpoints in the tool-paths are then eliminated by connecting adjacent tool-paths. The Dijkstra algorithm is engaged to reduce the nozzle jump distance and shorten the production time. Stretching tests of different specimens printed with the developed path generation algorithms demonstrate that the model with the stress-based path has better mechanical performance. The digital image correlation (DIC) method and scanning electron microscopy (SEM) are employed to observe the fracture processes and fracture surfaces, respectively. Corresponding results of DIC and SEM reveal that different path filling forms exhibit variable failure patterns because of filament anisotropy. The filling fraction is calculated and indicates that the deposition quality of the advanced path is not compromised. This work provides a synthesis methodology for improving the mechanical performance of 3D printing products.

Stress-based tool-path planning methodology for fused filament fabrication

Build orientation determination is one of the essential process planning tasks in additive manufacturing since it has crucial effects on the part quality, post-processing, build time and cost, etc. This paper introduces a method based on fuzzy multi-attribute decision making to determine the optimal build orientation from a finite set of alternatives. The determination process includes two major steps. In the first step, attributes that are considered in the determination and heterogeneous relationships of which are firstly identified. A fuzzy decision matrix is then constructed and normalised based on the values of the identified attributes, which are quantified by a set of fuzzy numbers. In the second step, two fuzzy number aggregation operators are developed to aggregate the fuzzy information in the normalised matrix. By comparing the aggregation results, a ranking of all alternative build orientations can then be generated. Two determination examples are used to demonstrate the working process of the proposed method. Qualitative and quantitative comparisons between the proposed method and other methods are carried out to demonstrate its feasibility, effectiveness, and advantages.

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Determination of optimal build orientation for additive manufacturing using Muirhead mean and prioritised average operators

Traditionally Additive Manufacturing (AM) is a two-dimensional layer-by-layer material deposition process which requires building the support structures along with the build of the desired model. Removal of the support structures is costly and time-consuming, especially for metal parts. Using a five-axis deposition machine has the potential to build structures without the need for supports. However, there is a lack of automated process planning software to support the full use of five-axis machines. This paper introduces an automated method that allows reorienting the part during the build using a five-axis machine. The reorientations still allow the part to be built using traditional planar deposition but without the use of supports. This requires that the part be decomposed into sub volumes, such that each sub volume has its build direction and can be built with planar layers without support structures. This paper presents algorithms to determine the sub-volumes, their orientations, and sequence, which form the major components of the process plan for manufacturing. The process plan is generated by sequencing the decomposed volumes while ensuring a lack of local collision with previously deposited volumes. An added benefit of this automated process is the ability to evaluate the feasibility of building the part in a support free manner. This can provide feedback to the designer on the support free manufacturability of the part. Examples illustrating the methodology and establishing the viability of the decomposition strategy are presented to verify the effectiveness of the algorithms.

Process planning for five-axis support free additive manufacturing

The fused filament fabrication (FFF) process deposits thermoplastic material in a layer-by-layer manner, expanding the design space and manufacturing capability compared with traditional manufacturing. However, the FFF process is inherently directional as the material is deposited in a layer-wise manner. Therefore, the in-plane material cannot reach the isotropy character when performing the tensile test. This would cause the strength of the print components to vary based on the different process planning selections (building orientation, toolpath pattern). The existing toolpaths, primarily used in the FFF process, are linear, zigzag, and contour toolpaths, which always accumulate long filaments and are unidirectional. Thus, this would create difficulties in improving the mechanical strength from the existing toolpath strategies due to the material in-plane anisotropy. In this paper, an in-plane isotropy toolpath pattern is generated to enhance the mechanical strength in the FFF process. The in-plane isotropy can be achieved through continuous deposition while maintaining randomized distribution within a layer. By analyzing the tensile strength on the specimens made by traditional in-plane anisotropy toolpath and the proposed in-plane isotropy toolpath, our results suggest that the mechanical strength can be reinforced by at least 20% using our proposed toolpath strategy in extrusion-based additive manufacturing.

Strength Enhancement in Fused Filament Fabrication via the Isotropy Toolpath

Automatic Toolpath Generation for Heterogeneous Objects Manufactured by Directed Energy Deposition Additive Manufacturing Process

Laser Powder bed fusion (L-PBF) has gained much attention for its ability to manufacture high-precision and high-complexity metal components for the aircraft and automobile industries. However, to a certain degree, the print quality (shape, GD&T, mechanical property) is difficult to predict and control due to the multi-variant processability. Recent research predominantly focuses on building a semantic/qualitative process relationship from the single/a few process parameters on the ultimate print qualities, such as thermal distortion failure/defect probabilities. However, such semantic/qualitative process relationships cannot reflect specific governing effects from process variables and provide an optimal selection of the parameters to ensure the build quality. Therefore, there is a strong need to develop a quantitative model within a process network and achieve a constant quality level by controlling the process parameter set. To address the aforementioned challenges, this research focuses on developing a multi-dimensional process-quality interaction model to predict and control the porosity of Titanium 6Al-4V in L-PBF. The process network is first derived from existing literature composing the potential, influential process parameters that can deviate the porosity level, such as laser power and scanning speed. Then the quantitative model is built based on a multi-dimensional quantitative modeling technique from a collection of experimental data. The multi-dimensional modeling provides an architecture that replaces the artificial neural network (ANN) or regression model to depict the mathematical relationship. The established quantitative process models analyze the intercorrelated effects from process parameters to the porosity. The printable zone can also be derived from such a quantitative model and visualized through a multi-dimension variable-control response graph to optimize the selection of process parameters. This would result in a comprehensive understanding of selecting the process parameters according to the desired porosity level and pave the path to fully control the metal L-PBF print qualities by adjusting the process variables. The experimental data also validate the proposed quantitative model to demonstrate its effectiveness and correctness. 

Porosity management and control in powder bed fusion process through process-quality interactions

In today’s design — manufacturing context, designers often modify existing 3D shapes (or design models) instead of creating a new design from scratch. This requires the ability to search an existing database of designs/3D models to identify and extract similar designs. Shape Retrieval Tools (SRTs) have been developed to provide an essential role in saving time and effort to retrieve and generate new designs. The capabilities of commercially available SRTs vary based on the form of the input design model, the search technique or algorithm used, the search/retrieval time, ease of use, and the quality of results. The focus of this paper is to study of their capabilities, performances, and differences and develop criteria to compare the effectiveness and performance of such Shape Retrieval Tools. Current search evaluation methods, such as precision and recall, are based on human interpretation of the results. This paper presents a holistic set of metrics for comparing the performance and effectiveness of SRTs, including data input options (to search), effectiveness of the search process, the associated retrieval time, overall ease of use, and additional data retrieval details. An algorithm is proposed to objectively analyze the search results based on the proposed Model Match Ratio (MMR), computed by the variance between the input and retrieved geometries. The search results are usually presented in a rank order list. A Precision Sequence Metric (PSM) is developed to evaluate the retrieved list by ranking the retrieved results based on the MMR for evaluating the quality of the search. The proposed evaluation algorithm was tested on several design models (and their subsequent retrieval results) involving three SRTs (Vizseek, Geolus, and CADENAS); the results of the comparison of the performance of these SRTs are discussed in this paper.

Xinyi Xiao

Papers

Process planning for five-axis support free additive manufacturing

Strength Enhancement in Fused Filament Fabrication via the Isotropy Toolpath

Automatic Toolpath Generation for Heterogeneous Objects Manufactured by Directed Energy Deposition Additive Manufacturing Process

Porosity management and control in powder bed fusion process through process-quality interactions

Critical assessment of Shape Retrieval Tools (SRTs)