Although additive manufacturing (AM) has gained significant attention due to the advantages it offers and is currently a focus of much research, design of critical load carrying components utilizing such processes is still at its infancy. This is due to the fact that most of the load carrying components made by AM processes are subjected to cyclic loads, and fatigue behaviour of AM metals is far less understood as compared with those made by conventional methods, such as wrought and cast metals. To better understand the fatigue behaviour of AM metals, a wide range of issues that affect the behaviour in a synergistic manner must be considered. These include the effects of defects, residual stresses, surface finish, geometry and size, layer orientation, and heat treatment. Additionally, due to the multiaxial nature of the loading and/or complex geometries typically manufactured by AM processes, the stress state is often multiaxial including both normal and shear stresses. In this paper, the aforementioned effects influencing the fatigue resistance of AM parts, including torsion and multiaxial fatigue behaviour, are briefly discussed using some recently generated experimental data on Ti‐6Al‐4V by the authors.
The advent of additive manufacturing (AM) processes brought with it intense research into various materials and manufacturing processes. At the same time, the need for validation of material properties, as well as study and forecasting of aging, has arisen. Modern imaging techniques, like X‐ray computed tomography (XCT), are a convenient vehicle for such studies; however, the large datasets they produce require novel analysis techniques to efficiently extract critical information. In this paper, we present our work on developing a 3D extension of the ResNet architecture to distinguish between two build orientations of tensile bars produced by AM. Using only information from XCT, our method achieves a 99.3% correct classification at a misidentification of 1%.
more » « less- Award ID(s):
- 1633216
- NSF-PAR ID:
- 10360129
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Statistical Analysis and Data Mining: The ASA Data Science Journal
- Volume:
- 14
- Issue:
- 6
- ISSN:
- 1932-1864
- Page Range / eLocation ID:
- p. 575-582
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Additive manufacturing (AM) processes present designers with unique capabilities while imposing several process limitations. Designers must leverage the capabilities of AM — through opportunistic design for AM (DfAM) — and accommodate AM limitations — through restrictive DfAM — to successfully employ AM in engineering design. These opportunistic and restrictive DfAM techniques starkly contrast the traditional, limitation-based design for manufacturing techniques — the current standard for design for manufacturing (DfM). Therefore, designers must transition from a restrictive DfM mindset towards a ‘dual’ design mindset — using opportunistic and restrictive DfAM concepts. Designers’ prior experience, especially with a partial set of DfM and DfAM techniques could inhibit their ability to transition towards a dual DfAM approach. On the other hand, experienced designers’ auxiliary skills (e.g., with computer-aided design) could help them successfully use DfAM in their solutions. Researchers have investigated the influence of prior experience on designers’ use of DfAM tools in design; however, a majority of this work focuses on early-stage ideation. Little research has studied the influence of prior experience on designers’ DfAM use in the later design stages, especially in formal DfAM educational interventions, and we aim to explore this research gap. From our results, we see that experienced designers report higher baseline self-efficacy with restrictive DfAM but not with opportunistic DfAM. We also see that experienced designers demonstrate a greater use of certain DfAM concepts (e.g., part and assembly complexity) in their designs. These findings suggest that introducing designers to opportunistic DfAM early could help develop a dual design mindset; however, having more engineering experience might be necessary for them to implement this knowledge into their designs.
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This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on various
in-situ andex-situ imaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM).In-situ imaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered fromin-situ imaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand,ex-situ imaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components. -
The goal of this work to mitigate flaws in metal parts produced from laser powder bed fusion (LPBF) additive manufacturing (AM) process. As a step towards this goal, the objective of this work is to predict the build quality of a part as it is being printed via deep learning of in-situ layer-wise images obtained from an optical camera instrumented in the LPBF machine. To realize this objective, we designed a set of thin-wall features (fins) from Titanium alloy (Ti-6Al-4V) material with varying length-to-thickness ratio. These thin-wall test parts were printed under three different build orientations and in-situ images of their top surface were acquired during the process. The parts were examined offline using X-ray computed tomography (XCT), and their build quality was quantified in terms of statistical features, such as the thickness and consistency of its edges. Subsequently, a deep learning convolutional neural network (CNN) was trained to predict the XCT-derived statistical quality features using the layer-wise optical images of the thin-wall part as inputs. The statistical correlation between CNN-based predictions and XCT-observed quality measurements exceeds 85%. This work has two outcomes consequential to the sustainability of additive manufacturing: (1) It provides practitioners with a guideline for building thin-wall features with minimal defects, and (2) the high correlation between the offline XCT measurements and in-situ sensor-based quality metrics substantiates the potential for applying deep learning approaches for the real-time prediction of build flaws in LPBF.more » « less
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