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The purpose of this paper is to find a theoretical reference to adjust the unsymmetrical arc shape and plasma flow of overlapping deposition in wire arc additive manufacturing (WAAM) and ensure the effect of the gas shielding and stable heat and mass transfer in deposition process. The multiphysical numerical simulation and physical experiment are used for validation.

In this study, welding torch tilt deposition and external parallel magnetic field–assisted deposition are presented to adjust the unsymmetrical arc shape and plasma flow of overlapping deposition, and a three-dimensional numerical model is developed to simulate the arc of torch tilt overlapping deposition and external parallel magnetic field–assisted overlapping deposition.

The comparison of simulated results indicate that the angle of welding torch tilt equal to 20° and the magnetic flux density of external transverse magnetic field equal to 0.001 Tesla are capable of balancing the electric arc and shielding gas effectively, respectively. The arc profiles captured by a high-speed camera match well with simulated results.

These studies of this paper can provide a theoretical basis and reference for the calibration and optimization of WAAM process parameters.

For part models with complex shape features or freeform shapes, the existing build orientation determination methods may have issues, such as difficulty in defining features and costly computation. To deal with these issues, this paper aims to introduce a new statistical method to develop fast automatic decision support tools for additive manufacturing build orientation determination.

The proposed method applies a non-supervised machine learning method, K-Means Clustering with Davies–Bouldin Criterion cluster measuring, to rapidly decompose a surface model into facet clusters and efficiently generate a set of meaningful alternative build orientations. To evaluate alternative build orientations at a generic level, a statistical approach is defined.

A group of illustrative examples and comparative case studies are presented in the paper for method validation. The proposed method can help production engineers solve decision problems related to identifying an optimal build orientation for complex and freeform CAD models, especially models from the medical and aerospace application domains with much efficiency.

The proposed method avoids the limitations of traditional feature-based methods and pure computation-based methods. It provides engineers a new efficient decision-making tool to rapidly determine the optimal build orientation for complex and freeform CAD models.

The purpose of this study is to test the concept of a relatively low cost but biocompatible customized surgical guide printing method using a new composite material for the FDM process to support accurate virtual model reconstruction in CT.

Current additive manufacturing printed surgical guides have problems of scanning artifacts or low computed tomography (CT) values for virtual model reconstruction in CT-assisted surgical operations. These tools always face difficulties in precise positioning due to the effect of human soft tissues and manually made unstable landmarks. To solve this problem, this paper proposes a modified material, polyetheretherketone powder mixed with barium sulfate powder, for printing customized surgical guides with relatively low cost to support a synchronized scanning strategy, for the accurate reconstruction of human tissues and in vitro models.

A set of benchmarking experiments and clinical simulation cases were conducted. The results showed that the proposed solution can be used to print surgical guides to form stable and clear CT graphs for three-dimensional digital model reconstruction. Human tissues and in vitro models can be accurately reconstructed using clear CT graphs without any scanning artifacts or difficulties in image segmentation for virtual model reconstruction, thus facilitating accurate operation guidance and positioning.

This method has wide application potential for printing modular or customized surgical guides with low cost and reusability, especially for surgical operations using CT-assisted navigation systems in underdeveloped regions where medical device costs are a critical issue.

The objective of this study is to investigate the feasibility of using selective laser melting (SLM) process to print fine capillary wick porous structures for heat pipe applications and clarify the interrelations between the printing parameters and the structure functional performance to form guidelines for design and printing preparation.

A new toolpath-based construction method is adopted to prepare the printing of capillary wick with fine pores in SLM process. This method uses physical melting toolpath profile with associated printing parameters to directly define slices and assemble them into a printing data model to ensure manufacturability and reduce precision loss of data model transformation in the printing preparation stage. The performance of the sample was characterised by a set of standard experiments and the relationship between the printing parameters and the structure performance is modeled.

The results show that SLM-printed capillary wick porous structures exhibit better performance in terms of pore diameter and related permeability than that of structures formed using traditional sintering methods, generally 15 times greater. The print hatching space and infilling pattern have a critical impact on functional porosity and permeability. An empirical formula was obtained to describe this impact and can serve as a reference for the design and printing of capillary wicks in future applications.

This research proves the feasibility of using SLM process to printing functional capillary wicks in extremely fine pores with improved functional performance. It is the first time to reveal the relations among the pore shapes, printing parameters and functional performance. The research results can be used as a reference for heat pipe design and printing in future industrial applications.

The manufacturability of extremely fine porous structures in the SLM process has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. The research objective is to find out the process limitation and key processing parameters for printing fine porous structures so as to give reference for design and manufacturing planning.

In metallic AM processes, the difficulty of geometric modeling and manufacturing of structures with pore sizes less than 350 μm exists. The manufacturability of porous structures in selective laser melting (SLM) has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. To solve this problem, a comprehensive experimental study was conducted to benchmark the manufacturability of the SLM process for extremely fine porous structures (less than 350 um and near a limitation of 100 um) and propose a manufacturing result evaluation method. Numerous porous structure samples were printed to help collect critical datasets for manufacturability analysis.

The results show that the SLM process can achieve an extreme fine feature with a diameter of 90 μm in stable process control, and the process parameters with their control strategies as well as the printing process planning have an important impact on the printing results. A statistical analysis reveals the implicit complex relations between the porous structure geometries and the SLM process parameter settings.

It is the first time to investigate the manufacturability of extremely fine porous structures of SLM. The method for manufacturability analysis and printing parameter control of fine porous structure are discussed.

The use of continuous fiber-reinforced filaments improves the mechanical properties obtained with the fused filament fabrication (FFF) process. Yet, there is a lack of simulation tailored tools to assist in the design for additive manufacturing of continuous fiber composites. To build such models, a precise elastic model is required. As the porosity caused by interbead voids remains an important flaw of the process, this paper aims to build an elastic model integrating this aspect.

To study the amount of porosity, which could be a failure initiator, this study proposes a two step periodic homogenization method. The first step concerns the microscopic scale with a unit cell made of fiber and matrix. The second step is at the mesoscopic scale and combines the elastic material of the first step with the interbead voids. The void content has been set as a parameter of the model. The material models predicted with the periodic homogenization were compared with experimental results.

The comparison between periodic homogenization results and tensile test results shows a fair agreement between the experimental results and that of the numerical simulation, whatever the fibers’ orientations are. Moreover, a void content reduction has been observed by increasing the crossing angle from one layer to another. An empiric law giving the porosity according to this crossing angle was created. The model and the law can be further used for design evaluation and optimization of continuous fiber-reinforced FFF.

A new elastic model considering interbead voids and its variation with the crossing angle of the fibers has been built. It can be used in simulation tools to design high performance fused filament fabricated composite parts.

This paper presents the application of grey modeling for thermal spray processing parameter analysis in less data environment.

Based on processing knowledge, key processing parameters of thermal spray process are analyzed and preselected. Then, linear and non-linear grey modeling models are integrated to mine the relationships between different processing parameters.

Model A reveals the linear correlation between the HVOF process parameters and the characterization of particle in-flight with average relative errors of 9.230 percent and 5.483 percent for velocity and temperature.

The prediction accuracies of coatings properties vary, which means that there exists more complex non-linear relationship between the identified input parameters and coating results, or more unexpected factors (e.g. factors from material side) should be further investigated.

According to the modeling case in this paper, method has potential to deal with other diverse modeling problems in different industrial applications where challenge to collecting large quantity of data sets exists.

It is the first time to apply grey modeling for thermal spray processing where complicated relationships among processing parameters exist. The modeling results show reasonable results to experiment and existing processing knowledge.

This paper aims to introduce a new nesting scheme to better describe and solve the single-layer-part packing problem in additive manufacturing (AM).

Parallel nesting scheme using two-dimensional (2D) changeable projection profiles is developed. At first, a feature-based orientation optimization method is used to identify a set of practical alternative build orientations for each part to ensure the part quality. Then, 2D polygons are used to represent each part’s projection profiles under its alternative build orientations. Finally, a parallel layout searching algorithm is developed to identify the optimal part layout by using 2D changeable projection profiles.

The proposed nesting scheme can both guarantee the production quality for each part and search the optimal part layout with larger probability but less computational time.

With the use of changeable 2D projection profiles, this method conducts 2D computation to solve the single-layer-part packing problem with five degrees of freedom, which saves much computation cost and, at the same time, guarantees the production quality of each part. By adding specific nesting objectives or constraints and heuristic searching knowledge to the proposed nesting scheme, practical nesting software can be developed to meet the specific nesting or packing requirements for industrial AM machines.

The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning.

To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations.

The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers.

The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations.

AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.

The purpose of this paper is to propose an integrated decision-making model for multi-attributes decision-making (MADM) problems in additive manufacturing (AM) process planning and for related MADM problems in other research areas.

This research analyzed the drawbacks of former methods and then proposed two sub-decision-making models, “deviation model” and “similarity model”. The former sub-model aimed to measure the deviation extent of each alternative to the aspired goal based on analyzing Euclidean distance between them, whereas the latter sub-model applying grey incidence analysis was used to measure the similarity between alternatives and the expected goal by investigating the curve shape of each alternative. Afterwards, an integrated model based on the aggregation of the two sub-models was proposed and verified by a numerical example and simple case studies.

The calculating results of the cited numerical example and the comparison to former related research showed that this proposed model is more practical and reasonable than former methods applied in MADM problems of AM. In addition, the proposed model can be applied in other fields where MADM problems exist.

This proposed integrated model not only considered the deviation extent of alternatives to the aspired goal but also investigated the similarity between alternatives and the expected goal. The similarity analysis compensates the drawbacks of traditional “distance-based” models or methods that cannot distinguish alternatives which have the same distance-based index value.