A comprehensive review on laser powder bed fusion of steels : processing, microstructure, defects and control methods, mechanical properties, current challenges and future trends

Laser Powder Bed Fusion process is regarded as the most versatile metal additive manufacturing process, which has been proven to manufacture near net shape up to 99.9% relative density, with geometrically complex and high-performance metallic parts at reduced time. Steels and iron-based alloys are the most predominant engi-neering materials used for structural and sub-structural applications. Availability of steels in more than 3500 grades with their wide range of properties including high strength, corrosion resistance, good ductility, low cost, recyclability etc., have put them in forefront of other metallic materials. However, LPBF process of steels and iron-based alloys have not been completely established in industrial applications due to: (i) limited insight available in regards to the processing conditions, (ii) lack of specific materials standards, and (iii) inadequate knowledge to correlate the process parameters and other technical obstacles such as dimensional accuracy from a design model to actual component, part variability, limited feedstock materials, manual post-processing and etc. Continued efforts have been made to address these issues. This review aims to provide an overview of steels and iron-based alloys used in LPBF process by summarizing their key process parameters, describing thermophysical phenomena that is strongly linked to the phase transformation and microstructure evolution during solidifica-tion, highlighting metallurgical defects and their potential control methods, along with the impact of various post-process treatments; all of this have a direct impact on the mechanical performance. Finally, a summary of LPBF processed steels and iron-based alloys with functional properties and their application perspectives are presented. This review can provide a foundation of knowledge on LPBF process of steels by identifying missing information from the existing literature

Characterisation of freeform, structured surfaces in T-spline spaces and its applications

In advanced manufacturing, surface topographical designs with deterministic freeform and embedded structures have proven to contain effective, additive functionalities. These surfaces need to be geometrically characterised regarding the designed form and structures. However, this is problematic since existing characterisation techniques such as polynomial form removal, Gaussian/spline/wavelet filtration, field-based statistical parameterisation, spectral and fractal analysis do not provide satisfying results. In this paper, we, therefore, propose to characterise the complex surfaces in T-spline spaces, i.e. basis spline spaces along with T-junctions, using an efficient T-spline fitting algorithm. Several case studies show that the proposed method is compatible and has notable potentials for the challenging characterisation tasks, including non-Euclidean freeform removal, edge-reserving filtration with multiscale analysis, scattered data interpolation and smoothing, and smart large-data downsampling or compression

Freeform surface filtering using the lifting wavelet transform

Texture measurement for simple geometric surfaces is well established. Many surface filtration techniques using Fourier, Gaussian, wavelets, etc., have been proposed over the past decades. These filtration techniques cannot be applied to today's complex freeform surfaces, which have non-Euclidean geometries in nature, without distortion of the results. Introducing the lifting scheme opens the opportunity to extend the wavelet analysis to include irregular complex surface geometries. In this paper, a method of filtering those complex freeform surfaces presented by triangular meshes based on the lifting wavelet has been proposed. The proposed algorithm generalises the traditional lifting scheme to any freeform surface represented by any type of triangular mesh; regular, semi-regular or irregular mesh. This technique consists of five major stages; split, predict, update, simplify (down-sampling) and merge (up-sampling). All of these techniques are discussed and explained in the paper. Results and discussion of the application of this method to simulated and measured data are presente

Multi-scale freeform surface texture filtering using a mesh relaxation scheme

Surface filtering algorithms using Fourier, Gaussian, wavelets, etc, are well-established for simple Euclidean geometries. However, these filtration techniques cannot be applied to today's complex freeform surfaces, which have non-Euclidean geometries, without distortion of the results. This paper proposes a new multi-scale filtering algorithm for freeform surfaces that are represented by triangular meshes based on a mesh relaxation scheme. The proposed algorithm is capable of decomposing a freeform surface into different scales and separating surface roughness, waviness and form from each other, as will be demonstrated throughout the paper. Results of applying the proposed algorithm to computer-generated as well as real surfaces are represented and compared with a lifting wavelet filtering algorithm