Additive manufacturing of metallic components – Process, structure and properties

Additive manufacturing (AM), widely known as 3D printing, is a method of manufacturing that forms parts from powder, wire or sheets in a process that proceeds layer by layer. Many techniques (using many different names) have been developed to accomplish this via melting or solid-state joining. In this review, these techniques for producing metal parts are explored, with a focus on the science of metal AM: processing defects, heat transfer, solidification, solid-state precipitation, mechanical properties and post-processing metallurgy. The various metal AM techniques are compared, with analysis of the strengths and limitations of each. Only a few alloys have been developed for commercial production, but recent efforts are presented as a path for the ongoing development of new materials for AM processes.

The metallurgy and processing science of metal additive manufacturing

Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones

Many traditional approaches for strengthening steels typically come at the expense of useful ductility, a dilemma known as strength-ductility trade-off. New metallurgical processing might offer the possibility of overcoming this. Here we report that austenitic 316L stainless steels additively manufactured via a laser powder-bed-fusion technique exhibit a combination of yield strength and tensile ductility that surpasses that of conventional 316L steels. High strength is attributed to solidification-enabled cellular structures, low-angle grain boundaries, and dislocations formed during manufacturing, while high uniform elongation correlates to a steady and progressive work-hardening mechanism regulated by a hierarchically heterogeneous microstructure, with length scales spanning nearly six orders of magnitude. In addition, solute segregation along cellular walls and low-angle grain boundaries can enhance dislocation pinning and promote twinning. This work demonstrates the potential of additive manufacturing to create alloys with unique microstructures and high performance for structural applications.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1443&context=ameslab_manuscripts

Additively manufactured hierarchical stainless steels with high strength and ductility

A review on selective laser sintering/melting (SLS/SLM) of aluminium alloy powders: Processing, microstructure, and properties

Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing

SimPy is a Python-based, interpreted simulation tool that offers the power and convenience of Python. It is able to launch processes and sub-processes using generators, which act autonomously and may interact using interrupts. SimPy offers other advantages over competing commercial codes in that it allows for modular development, use of a version control system such as CVS, can be made self-documenting with PyDoc, and is completely extensible. The convenience of an interpreted language, however, is offset for large models by slower than desired run times. This disadvantage can be compensated for by parallelizing the system using PyMPI, from the Lawrence Livermore National Laboratory. This work was performed under the auspices of the U.S. Department of Energy by University of California, Lawrence Livermore National Laboratory under Contract W-7405-Eng-48.

Parallel simulations of manufacturing processing using simpy, a python-based discrete event simulation tool

. Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and most consistent detection separation scores on average. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, includ-ing its DOI. [DOI: 10.1117/1.JRS.16.026505] bar values are produced by taking the mean value of the mean separation scores for the four material types for each algorithm. The error bars are produced by taking the mean value of the standard deviation of the separation scores. The dual annealing and differential evolution algorithms produced the highest and most consistent results for the three-band case. For the four- and five-band cases, all algorithms are similar. It is worth noting that the particle swarm algorithm has higher variance than the other two algorithms, which is a good thing if the optimization can be run many times — as a higher maximum score could be more attainable with this algorithm.

/pdf/optimal-band-selection-for-target-detection-with-a-lwir-2ugeefoz.pdf

Optimal band selection for target detection with a LWIR multispectral imager

identification. However, when the information contained in both sensing modalities was combined in a sensor fusion strategy, the error rates of the top performing models were significantly reduced. 

Victor Castillo

Papers

Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing

Parallel simulations of manufacturing processing using simpy, a python-based discrete event simulation tool

Optimal band selection for target detection with a LWIR multispectral imager

Keyhole-mode Microscopy Dataset for Laser Powder-bed Fusion Modeling