On Geometric Design Rules and In-Process Build Quality Monitoring of Thin-Wall Features Made Using Laser Powder Bed Fusion Additive Manufacturing Process

The goal of this thesis is to quantify the link between the design features (geometry), in-process signatures, and build quality of parts made using the laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is the foundational basis for proposing design rules in AM, as well as for detecting the impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: 1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry related factor is the ratio of the length of a thin wall () to its thickness () in the X-Y plane along which powder is deposited (raked or rolled), termed as the aspect ratio (length-to-thickness ratio, /), and the angular orientation (θ) of the part which refers to the inclination of the part in the X-Y plane to the re-coater of the LPBF machine. 2) Monitor the thin-wall build quality by analyzing the images of the part obtained from an in-process optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratios from Titanium alloy (Ti-6Al-4V) material – the aspect ratio / of the thin-walls ranges from 36 to 183 (11 mm long [constant], and 0.3 mm to 0.06 mm in thickness). These thin-wall test artifacts were built under three angular orientations, 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of its geometric integrity was quantified as a function of the aspect ratio and orientation angle, which helped codify a set of design guidelines for building thin-wall structures with LPBF. The resulting geometric design rules are summarized as follows. 1) The orientation angle (θ) of 90° should be avoided while building thin-wall structures. 2) The aspect ratio (/) of a thin wall should not exceed 73 (11 mm / 0.15 mm). 3) The height of a thin wall should not be more than nine times its thickness. To monitor the quality of the thin-wall, in-process images of the top surface of the bed were acquired during the build process. The online optical images were correlated with the offline quantitative measurements of the thin walls through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ) between the offline XCT-measured thin-wall quality and the CNN predicted measurement ranged from 80% to 98%. Consequently, the impending poor quality of a thin wall was captured from in-process data. Advisor: Prahalada Ra

Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis

UID/00667/2020 (UNIDEMI). J. P. Oliveira acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020 Prahalada Rao acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DE-SC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI-1719388, CMMI-1920245, CMMI-1739696, CMMI-1752069, PFI-TT 2044710, ECCS 2020246] for funding his research program. This work espousing the concept of online process monitoring in WAAM was funded through the foregoing DOE Grant (Program Officer: Timothy Fitzsimmons), which partially supported the doctoral graduate work of Mr. Benjamin Bevans at University of Nebraska-Lincoln Benjamin, Aniruddha, and Ziyad Smoqi were further supported by the NSF grants CMMI 1752069 (CAREER) and ECCS 2020246. Detecting flaw formation in metal AM using in-situ sensing and graph theory-based algorithms was a major component of CMMI 1752069 (program office: Kevin Chou). Developing machine learning alogirthms for advanced manufacturing applications was the goal of ECCS 2020246 (Program officer: Donald Wunsch). The XCT work was performed at the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska-Lincoln. The acquisition of the XCT scanner at University of Nebraska was funded through CMMI 1920245 (Program officer: Wendy Crone). Publisher Copyright: © 2022 The AuthorsThe goal of this work is to detect flaw formation in the wire-based directed energy deposition (W-DED) process using in-situ sensor data. The W-DED studied in this work is analogous to metal inert gas electric arc welding. The adoption of W-DED in industry is limited because the process is susceptible to stochastic and environmental disturbances that cause instabilities in the electric arc, eventually leading to flaw formation, such as porosity and suboptimal geometric integrity. Moreover, due to the large size of W-DED parts, it is difficult to detect flaws post-process using non-destructive techniques, such as X-ray computed tomography. Accordingly, the objective of this work is to detect flaw formation in W-DED parts using data acquired from an acoustic (sound) sensor installed near the electric arc. To realize this objective, we develop and apply a novel wavelet integrated graph theory approach. The approach extracts a single feature called graph Laplacian Fiedler number from the noise-contaminated acoustic sensor data, which is subsequently tracked in a statistical control chart. Using this approach, the onset of various types of flaws are detected with a false alarm rate less-than 2%. This work demonstrates the potential of using advanced data analytics for in-situ monitoring of W-DED.publishersversionpublishe

Closed-loop control of meltpool temperature in directed energy deposition

The objective of this work is to mitigate flaw formation in powder and laser-based directed energy deposition (DED) additive manufacturing process through close-loop control of the meltpool temperature. In this work, the meltpool temperature was controlled by modulating the laser power based on feedback signals from a coaxial two-wavelength imaging pyrometer. The utility of closed-loop control in DED is demonstrated in the context of practically inspired trapezoid-shaped stainlesssteel parts (SS 316L). We demonstrate that parts built under closed-loop control have reduced variation in porosity and uniform microstructure compared to parts built under open-loop conditions. For example, post-process characterization showed that closed-loop processed parts had a volume percent porosity ranging from 0.036% to 0.043%. In comparison, open-loop processed parts had a larger variation in volume percent porosity ranging from 0.032% to 0.068%. Further, parts built with closed-loop processing depicted consistent dendritic microstructure. By contrast, parts built with open-loop processing showed microstructure heterogeneity with the presence of both dendritic and planar grains, which in turn translated to large variation in microhardness

On Geometric Design Rules and In-Process Build Quality Monitoring of Thin-Wall Features Made Using Laser Powder Bed Fusion Additive Manufacturing Process

The goal of this thesis is to quantify the link between the design features (geometry), in-process signatures, and build quality of parts made using the laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is the foundational basis for proposing design rules in AM, as well as for detecting the impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: 1) Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry related factor is the ratio of the length of a thin wall () to its thickness () in the X-Y plane along which powder is deposited (raked or rolled), termed as the aspect ratio (length-to-thickness ratio, /), and the angular orientation (θ) of the part which refers to the inclination of the part in the X-Y plane to the re-coater of the LPBF machine. 2) Monitor the thin-wall build quality by analyzing the images of the part obtained from an in-process optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratios from Titanium alloy (Ti-6Al-4V) material – the aspect ratio / of the thin-walls ranges from 36 to 183 (11 mm long [constant], and 0.3 mm to 0.06 mm in thickness). These thin-wall test artifacts were built under three angular orientations, 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of its geometric integrity was quantified as a function of the aspect ratio and orientation angle, which helped codify a set of design guidelines for building thin-wall structures with LPBF. The resulting geometric design rules are summarized as follows. 1) The orientation angle (θ) of 90° should be avoided while building thin-wall structures. 2) The aspect ratio (/) of a thin wall should not exceed 73 (11 mm / 0.15 mm). 3) The height of a thin wall should not be more than nine times its thickness. To monitor the quality of the thin-wall, in-process images of the top surface of the bed were acquired during the build process. The online optical images were correlated with the offline quantitative measurements of the thin walls through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ) between the offline XCT-measured thin-wall quality and the CNN predicted measurement ranged from 80% to 98%. Consequently, the impending poor quality of a thin wall was captured from in-process data. Advisor: Prahalada Ra

Smart Additive Manufacturing: Sensing, Monitoring, and Machine Learning for Quality Assurance in Metal Additive Manufacturing

The long-term goal of this research is to advance in-situ sensing and monitoring approaches to mitigate flaw formation in metal additive manufacturing (AM) processes. Despite the considerable cost and time savings, the lack of process consistency has deterred the application of AM in safety-critical production environments. Due to the layer-wise addition of material in AM, a flaw, e.g., porosity, in a layer may get permanently sealed in by subsequent layers thereby adversely affecting part performance, such as fatigue life and strength. These flaws tend to occur notwithstanding extensive a priori materials and process optimization. Thus, to ensure the commercial viability of AM processes it is necessary to develop efficient sensing, diagnosis, and flaw correction approaches. The goal of this dissertation is to detect flaw formation using data from heterogeneous sensing arrays embedded inside AM machines – a Big Data Analytics and Sensor Fusion problem. As a step towards this goal, the objective of this dissertation is to develop and apply advanced data analytics algorithms that can fuse process signatures acquired from a heterogeneous in-situ sensor array, and subsequently identify the nature (type) and severity of an evolving flaw. Two types of metal-based AM processes are specifically studied in this work: laser powder bed fusion and droplet-on-demand liquid metal jetting. The approaches developed in this work are capable of detecting the type and severity of flaw formation with a statistical fidelity exceeding 95%

Ultrasound emulsification: effect of ultrasonic and physicochemical properties on dispersed phase volume and droplet size

Ultrasonic emulsification of oil and water was carried out and the effect of irradiation time, irradiation power and physicochemical properties of oil on the dispersed phase volume and dispersed phase droplet size has been studied. The increase in the irradiation time increases the dispersed phase volume while decreases the dispersed phase droplets size. With an increase in the ultrasonic irradiation power, there is an increase in the fraction of volume of the dispersed phase while the droplet size of the dispersed phase decreases. The fractional volume of the dispersed phase increases for the case of groundnut oil-water system while it is low for paraffin (heavy) oil-water system. The droplet size of soyabean oil dispersed in water is found to be small while that of paraffin (heavy) oil is found to be large. These variations could be explained on the basis of varying physicochemical properties of the system, i.e., viscosity of oil and the interfacial tension. During the ultrasonic emulsification, coalescence phenomenon which is only marginal, has been observed, which can be attributed to the collision of small droplets when the droplet concentration increases beyond a certain number and the acoustic streaming strength increases