Additive manufacturing of metallic components – Process, structure and properties

IEEE Transactions on Pattern Analysis and Machine Intelligence

This article reviews published data on the mechanical properties of additively manufactured metallic materials. The additive manufacturing techniques utilized to generate samples covered in this review include powder bed fusion (e.g., EBM, SLM, DMLS) and directed energy deposition (e.g., LENS, EBF3). Although only a limited number of metallic alloy systems are currently available for additive manufacturing (e.g., Ti-6Al-4V, TiAl, stainless steel, Inconel 625/718, and Al-Si-10Mg), the bulk of the published mechanical properties information has been generated on Ti-6Al-4V. However, summary tables for published mechanical properties and/or key figures are included for each of the alloys listed above, grouped by the additive technique used to generate the data. Published values for mechanical properties obtained from hardness, tension/compression, fracture toughness, fatigue crack growth, and high cycle fatigue are included for as-built, heat-treated, and/or HIP conditions, when available. The effects of test...

Metal Additive Manufacturing: A Review of Mechanical Properties

Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

https://dr.ntu.edu.sg/bitstream/10356/88603/1/Anisotropy%20and%20heterogeneity%20of%20microstructure%20and%20mechanical%20properties%20in%20metal%20additive%20manufacturing-%20A%20critical%20review.pdf

Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: A critical review

A thermo-mechanical model of directed energy deposition additive manufacturing of Ti–6Al–4V is developed using measurements of the surface convection generated by gasses flowing during the deposition. In directed energy deposition, material is injected into a melt pool that is traversed to fill in a cross-section of a part, building it layer-by-layer. This creates large thermal gradients that generate plastic deformation and residual stresses. Finite element analysis (FEA) is often used to study these phenomena using simple assumptions of the surface convection. This work proposes that a detailed knowledge of the surface heat transfer is required to produce more accurate FEA results. The surface convection generated by the deposition process is measured and implemented in the thermo-mechanical model. Three depositions with different geometries and dwell times are used to validate the model using in situ measurements of the temperature and deflection as well as post-process measurements of the residual stress. An additional model is developed using the assumption of free convection on all surfaces. The results show that a measurement-based convection model is required to produce accurate simulation results.

Thermo-mechanical model development and validation of directed energy deposition additive manufacturing of Ti–6Al–4V

Process monitoring in additive manufacturing (AM) is a crucial component in the mission of broadening AM industrialization. However, conventional part evaluation and qualification techniques, such as computed tomography (CT), can only be utilized after the build is complete, and thus eliminate any potential to correct defects during the build process. In contrast to post-build CT, in situ defect detection based on in situ sensing, such as layerwise visual inspection, enables the potential for in-process re-melting and correction of detected defects and thus facilitates in-process part qualification. This paper describes the development and implementation of such an in situ defect detection strategy for powder bed fusion (PBF) AM using supervised machine learning. During the build process, multiple images were collected at each build layer using a high resolution digital single-lens reflex (DSLR) camera. For each neighborhood in the resulting layerwise image stack, multi-dimensional visual features were extracted and evaluated using binary classification techniques, i.e. a linear support vector machine (SVM). Through binary classification, neighborhoods are then categorized as either a flaw, i.e. an undesirable interruption in the typical structure of the material, or a nominal build condition. Ground truth labels, i.e. the true location of flaws and nominal build areas, which are needed to train the binary classifiers, were obtained from post-build high-resolution 3D CT scan data. In CT scans, discontinuities, e.g. incomplete fusion, porosity, cracks, or inclusions, were identified using automated analysis tools or manual inspection. The xyz locations of the CT data were transferred into the layerwise image domain using an affine transformation, which was estimated using reference points embedded in the part. After the classifier had been properly trained, in situ defect detection accuracies greater than 80% were demonstrated during cross-validation experiments.

Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

(Re)Designing for Part Consolidation: Understanding the Challenges of Metal Additive Manufacturing

Apparatus for determining a quality of a weld produced by a welding device according to the present invention includes a sensor operatively associated with the welding device. The sensor is responsive to at least one welding process parameter during a welding process and produces a welding process parameter signal that relates to the at least one welding process parameter. A computer connected to the sensor is responsive to the welding process parameter signal produced by the sensor. A user interface operatively associated with the computer allows a user to select a desired welding process. The computer processes the welding process parameter signal produced by the sensor in accordance with one of a constant voltage algorithm, a short duration weld algorithm or a pulsed current analysis module depending on the desired welding process selected by the user. The computer produces output data indicative of the quality of the weld.

Method and apparatus for assessing weld quality

Effect of processing conditions on the microstructure, porosity, and mechanical properties of Ti-6Al-4V repair fabricated by directed energy deposition

Edward W. Reutzel

Papers

Thermo-mechanical model development and validation of directed energy deposition additive manufacturing of Ti–6Al–4V

Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging.

(Re)Designing for Part Consolidation: Understanding the Challenges of Metal Additive Manufacturing

Method and apparatus for assessing weld quality

Effect of processing conditions on the microstructure, porosity, and mechanical properties of Ti-6Al-4V repair fabricated by directed energy deposition