Additive manufacturing (AM) is a new paradigm in design-driven build of customized products. Nonetheless, mass customization and low-volume production make the AM quality assurance extremely challenging. Advanced imaging provides an unprecedented opportunity to increase information visibility, cope with the product complexity, and enable on-the-fly quality control in AM. However, in situ images of a customized AM build show a high level of layer-to-layer geometry variation, which hampers the use of powerful image-based learning methods such as deep neural networks (DNNs) for flaw detection. Very little has been done on deep learning of variant geometry for image-guided process monitoring and control. The proposed research is aimed at filling this gap by developing a novel machine learning approach that is focused on variant geometry in each layer of the AM build, namely region of interests, for the characterization and detection of layerwise flaws. Specifically, we leverage the computer-aided design (CAD) file to perform shape-to-image registration and to delineate the regions of interest in layerwise images. Next, a hierarchical dyadic partitioning methodology is developed to split layer-to-layer regions of interest into subregions with the same number of pixels to provide freeform geometry analysis. Then, we propose a semiparametric model to characterize the complex spatial patterns in each customized subregion and boost the computational speed. Finally, a DNN model is designed to learn variant geometry in layerwise imaging profiles and detect fine-grained information of flaws. Experimental results show that the proposed deep learning methodology is highly effective to detect flaws in each layer with an accuracy of 92.50 ± 1.03%. This provides a significant opportunity to reduce interlayer variation in AM prior to completion of a build. The proposed methodology can also be generally applicable in a variety of engineering and medical domains that entail customized design, variant geometry, and image-guided process control.

A great challenge of metal cutting modeling is the ability of the material constitutive model to describe the mechanical behavior of the work material under the deformation conditions that characterizes this process. In particular, metal cutting generates a large range of state of stresses, as well as strains and strain rates higher than those generated by conventional mechanical tests, including the Split-Hopkinson pressure bar tests. A new hybrid analytical–experimental methodology to identify the material constitutive model coefficients is proposed. This methodology is based on an in situ high-resolution imaging and digital image correlation (DIC) technique, coupled with an analytical model of orthogonal cutting. This methodology is particularly suitable for the identification of the constitutive model coefficients at strains and strain rates higher than those found in mechanical tests. Orthogonal cutting tests of nickel aluminum bronze alloy are performed to obtain the strains and strain rates fields in the cutting zone, using DIC technique. Shear forces derived from stress integrations are matched to the measured ones. Then, the constitutive model coefficients can be determined, which is performed by solving a sequential optimization problem. Verifications are made by comparing the strain, strain rate, and temperature fields of cutting zone from experiments against those obtained by finite element simulations using the identified material constitutive model coefficients as input.

Metals such as Cu, Al, Ni, Ta, and stainless steels, despite their softness and ductility, are considered difficult to machine. This is due to large cutting forces and corresponding formation of a very thick chip during cutting, and hence, these metals are referred to as “gummy.” Their poor machinability of these materials arises because of an unsteady and highly redundant mode of plastic deformation referred to as sinuous flow. The prevailing plastic deformation mode during machining can be overcome by the application of certain coatings and chemical media on the undeformed free surface of the workpiece ahead of the cutting process. Using in situ imaging and concurrent force measurements, we present two different mechanochemical routes through which these media can improve machinability. The first route, which requires chemicals that adhere to the metal surface, such as glues and inks, improves cutting by inducing a change in the local plastic deformation mode—from sinuous flow to one characterized by periodic fracture or segmented flow. The second route, which requires chemicals that can react with the workpiece to form a low-friction layer, changes the sinuous flow mode to a smooth, laminar one. Both routes decrease cutting forces by more than 50% with order of magnitude improvement in surface texture as characterized by measured roughness and defect density. The results suggest a broad range of opportunities for improving the performance of machining processes for many difficult-to-cut gummy metals.

The modern manufacturing industry faces increasing demands to customize products according to personal needs, thereby leading to the proliferation of complex designs. To cope with design complexity, manufacturing systems are increasingly equipped with advanced sensing and imaging capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in the image stream collected from manufacturing processes. This paper presents the joint multifractal and lacunarity analysis to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics in the manufacturing process. Experimental studies show that the proposed method not only effectively characterizes surface finishes for quality control of ultraprecision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed multifractal method shows strong potentials to be applied for process monitoring and control in a variety of domains such as ultraprecision machining and additive manufacturing.

A growing research trend in additive manufacturing (AM) calls for layerwise anomaly detection as a step toward enabling real-time process control, in contrast to ex situ or postprocess testing and characterization. We propose a method for layerwise anomaly detection during laser powder-bed fusion (L-PBF) metal AM. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of the following four-step anomaly detection procedure: (1) using the captured thermal images, a process signature of a just-fabricated layer is generated. Next, a signature difference is obtained by subtracting the process signature of that particular layer from a prespecified reference signature, (2) a screening step selects potential regions of interests (ROIs) within the layer that are likely to contain process anomalies, hence reducing the computational burden associated with analyzing the full layer data, (3) the spatial dependence of these ROIs is modeled using a Gaussian process model, and then pixels with statistically significant deviations are flagged, and (4) using the quantity and the spatial pattern of the flagged pixels as predictors, a classifier is trained and implemented to determine whether the process is in- or out-of-control. We validate the proposed method using a case study on a commercial L-PBF system custom-instrumented with a dual-wavelength imaging pyrometer for capturing the thermal images during fabrication.

The goal of this work is to understand the effect of process conditions on lack of fusion porosity in parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process, and subsequently, to detect the onset of process conditions that lead to lack of fusion-related porosity from in-process sensor data. In pursuit of this goal, the objectives of this work are twofold: (1) quantify the count (number), size and location of pores as a function of three LPBF process parameters, namely, the hatch spacing (H), laser velocity (V), and laser power (P); and (2) monitor and identify process conditions that are liable to cause porosity through analysis of in-process layer-by-layer optical images of the build invoking multifractal and spectral graph theoretic features. These objectives are important because porosity has a significant impact on the functional integrity of LPBF parts, such as fatigue life. Furthermore, linking process conditions to defects via sensor signatures is the first step toward in-process quality assurance in LPBF. To achieve the first objective, titanium alloy (Ti–6Al–4V) test cylinders of 10 mm diameter × 25 mm height were built under differing H, V, and P settings on a commercial LPBF machine (EOS M280). The effect of these process parameters on count, size, and location of pores was quantified based on X-ray computed tomography (XCT) images. To achieve the second objective, layerwise optical images of the powder bed were acquired as the parts were being built. Spectral graph theoretic and multifractal features were extracted from the layer-by-layer images for each test part. Subsequently, these features were linked to the process parameters using machine learning approaches. Through these image-based features, process conditions under which the parts were built were identified with the statistical fidelity over 80% (F-score).

Metal-based powder-bed-fusion additive manufacturing (PBF-AM) is gaining increasing attention in modern industries, and is a promising direct manufacturing technology. Additive manufacturing (AM) does not require the tooling cost of conventional subtractive manufacturing processes, and is flexible to produce parts with complex geometries. Quality and repeatability of AM parts remain a challenging issue that persistently hampers wide applications of AM technology. Rapid advancements in sensing technology, especially imaging sensing systems, provide an opportunity to overcome such challenges. However, little has been done to fully utilize the image profiles acquired in the AM process and study the fractal patterns for the purpose of process monitoring, quality assessment, and control. This paper presents a new multifractal methodology for the characterization and detection of defects in PBF-AM parts. Both simulation and real-world case studies show that the proposed approach effectively detects and characterizes various defect patterns in AM images and has strong potential for quality control of AM processes.

Additive manufacturing, also known as three-dimensional (3D) printing, is an approach in which a structure may be fabricated layer by layer. For 3D inkjet printing, droplets are ejected from a nozzle, and each layer is formed droplet by droplet. Inkjet printing has been widely applied for the fabrication of 3D biological gel structures, but the knowledge of the microscale interactions between printed droplets is still largely elusive. This study aims to elucidate the layer formation mechanism in terms of the formation of single lines and layers comprised of adjacent lines during drop-on-demand inkjet printing of alginate using high speed imaging and particle image velocimetry. Inkjet droplets are found to impact, spread, and coalesce within a fluid region at the deposition site, forming coherent printed lines within a layer. The effects of printing conditions on the behavior of droplets during layer formation are discussed and modeled based on gelation dynamics, and recommendations are presented to enable controllable and reliable fabrication of gel structures. The effects of gelation on droplet impact dynamics are found to be negligible during alginate printing, and interfaces are found to form between printed lines within a layer depending on printing conditions, printing path orientation, and gelation dynamics.

A PED (precision extrusion deposition)/replica molding process enables scaffold guided tissue engineering of a heterocellular microfluidic device. We investigate two types of cell-laden devices: the first with a 3D microfluidic manifold fully embedded in a PDMS (polydimethylsiloxane) substrate and the second a channel network on the surface of the PDMS substrate for cell printing directly into device channels. Fully embedded networks are leak-resistant with simplified construction methods. Channels exposed to the surface are used as mold to hold bioprinted cell-laden matrix for controlled cell placement throughout the network from inlet to outlet. The result is a 3D cell-laden microfluidic device with improved leak-resistance (up to 2.0 mL/min), pervasive diffusion and control of internal architecture.

The extension of electrophotographic (EP) printing into the additive manufacturing space has been seen as a natural step for this technology; however, the self-insulating nature of the process has prevented the creation of structures beyond a limited number of layers where surface defects are evident. This paper examines two control strategies for EP-based three-dimensional (EP3D) printing that minimize the surface defects to obtain the accurate reproduction of the intended 3D geometry. The strategies rely not on material deposition control but rather on progressively compensating layer after layer for irregularities forming on the surface. This represents an important step toward the development and future commercialization of EP3D printing.

A system has been developed to measure the three-dimensional weld pool surface geometry in the gas metal arc welding (GMAW) process. It utilizes the specular nature of the weld pool surface by projecting a five-line laser pattern onto the surface and imaging its reflection. Specifically, the laser reflection is intercepted by an imaging plane and captured using a high speed camera. The reflected pattern is used to reconstruct the weld pool surface based on the law of reflection. Two reconstruction algorithms, referred to as center-points reconstruction and piece-wise weld pool surface reconstruction algorithm, are applied to sequentially reconstruct the weld pool height and three-dimensional surface geometry. Reconstructions has been conducted using simulated weld pool surface to provide a method to compare the reconstruction result with a known surface and evaluate the reconstruction accuracy. It is found that the proposed method is capable of reconstructing weld pool surface with acceptable accuracy. The height error of reconstructed center-points is less than 0.1 mm and the error of estimated weld pool boundary is less than 10%. Reconstruction results from images captured in welding experiments are also demonstrated.

We present an overview of four types of imaging artifacts that can occur during characterization of sharp sample topographies with intermittent contact atomic force microscopy (AFM) when using short nanotube probes ( < 100 nm in length). We discuss the causes behind these artifacts, as well as their implications in the context of nanomanufacturing, and explore theoretically their mitigation using AFM techniques that can perform simultaneous imaging and spectroscopy. In particular, we focus on the experimentally validated spectral inversion method [ Stark et al., 2002, “Inverting Dynamic Force Microscopy: From Signals to Time-Resolved Interaction Forces,” Proc. Natl. Acad. Sci. U.S.A., 99, pp. 8473–8478 ; Sahin et al., 2007, “An Atomic Force Microscope Tip Designed to Measure Time-Varying Nanomechanical Forces,” Nat. Nanotechnol., 2, pp. 507–514 ] and on a recently proposed dual-frequency-modulation method [ Chawla and Solares, 2009, “Single-Cantilever Dual-Frequency-Modulation Atomic Force Microscopy,” Meas. Sci. Technol., 20, p. 015501 ], which has been demonstrated within computational simulations and is under experimental implementation in our laboratory. We discuss the capabilities and limitations of each of these approaches as well as possible areas of future development.

Several aspects of the thermal behavior of deposited stainless steel 410 (SS410) during the laser engineered net shaping (LENS™) process were investigated experimentally and numerically. Thermal images in the molten pool and surrounding area were recorded using a two-wavelength imaging pyrometer system, and analyzed using THERMAVIZ ™ software to obtain the temperature distribution. The molten pool size, temperature gradient, and cooling rate were obtained from the recorded history of temperature profiles. The dynamic shape of the molten pool, including the pool size in both travel direction and depth direction was investigated, and the effect of different process parameters was illustrated. The thermal experiments were performed in a LENS™ 850 machine with a 3 kW IPG Photonics laser for different process parameters. A three-dimensional finite element model was developed to calculate the temperature distribution in the LENS™ process as a function of time and process parameters. The modeling results showed good agreement with the experimental data.

We report the use of a high resolution magnetic resonance (MR) imaging technique to monitor the development and maturation of tissue-printed constructs in vivo . Layer-by-layer inkjet printing technology was used to fabricate three different tissue constructs on alginate∕collagen gels: bovine aortic endothelial cell-printed (to represent soft tissue), human amniotic fluid-derived stem cell-printed (to represent hard tissue as they underwent osteogenic differentiation in vivo ), and cell-free constructs (scaffold only). The constructs were subcutaneously implanted into athymic mice and regularly monitored using a 7 T magnetic resonance imaging (MRI) scanner. The three tissue construct types showed distinct image contrast characteristics due to the different tissue microstructures and biochemical compositions at various time points. In addition, changes in tissue microvasculature were examined with dynamic perfusion MRI. These results indicate that high resolution MRI is a promising method for noninvasive, long-term monitoring of the status of cell-printed construct growth, differentiation, and vascularization.

Nickel and nickel-alloy microparts sized on the order of 5–1000 microns have been imaged in three dimensions using a new microscopic technique, Digital Volumetric Imaging (DVI). The gears were fabricated using Sandia National Laboratories’ LIGA technology (lithography, molding, and electroplating). The images were taken on a microscope built by Resolution Sciences Corporation by slicing the gear into one-micron thin slices, photographing each slice, and then reconstructing the image with software. The images were matched to the original CAD (computer aided design) model, allowing LIGA designers, for the first time, to see visually how much deviation from the design is induced by the manufacturing process. Calibration was done by imaging brass ball bearings and matching them to the CAD model of a sphere. A major advantage of DVI over scanning techniques is that internal defects can be imaged to very high resolution. In order to perform the metrology operations on the microcomponents, high-speed and high-precision algorithms are developed for coordinate metrology. The algorithms are based on a least-squares approach to data registration the { X , Y , Z } point clouds generated from the component surface onto a target geometry defined in a CAD model. Both primitive geometric element analyses as well as an overall comparison of the part geometry are discussed. Initial results of the micromeasurements are presented in the paper.

This paper presents a procedure to characterize deformation behavior applicable to various engineering materials in machining processes. Orthogonal machining tests are used to obtain the relationship between shear stress, strain, strain rate and temperature. Shear plane temperature is measured by an infrared thermal imaging system and compared with the Loewen and Shaw model. A new procedure to determine strain rate in the shear zone is proposed based on a triangular shear zone model and grain boundary determined by optical microscopy. Finally, a constitutive model for a low carbon steel, determined by the procedure, is presented and compared with existing results.

This paper presents a technique for front image sensing of the weld pool in variable polarity plasma arc welding of aluminum alloys, and describes the determination of the geometrical size of the keyhole for subsequent real-time feedback control of a full penetration weld. Image formation occurs when the arc light reflects off the concave mirror-like surface of the depressed keyhole weld pool, and passes through a band-pass filter onto the image sensor. The image of the visual keyhole (nominal keyhole) is a two-dimensional projected picture of the actual keyhole weld pool. The variation in area of the nominal keyhole is closely correlated with the variation of the bottom width of the weld bead.

The cost of feeding parts to a robot for either machine loading or for assembly has been recognized as excessively expensive. The lack of a cost-effective generic part-presenter has prevented the flexibility of the overall manufacturing automation to be fully exploited. For this reason, the design concept of an integrated microprocessor-controlled vision system based on retroreflective vision sensing has been developed at the Material Handling Research Center in Atlanta. Where the orientation of a part can be characterized by its silhouette, outline, or structured “engineering landmarks,” the concept of using retroreflective materials in vision-guided part-presentation system has been proven to have significant potential for improving reliability, reducing computation, and lowering implementation cost. This paper presents optic design concepts for collocated illumination for cost effective part-presentation. The design concepts are illustrated with practical applications and experimental results. The uniformity of the image irradiance across the sensor surface, in general, depends on several factors such as the intensity distribution of the light source, the reflectance characteristics of the object and background, and the geometrical relationship between the imaging sensor, the illumination source, and the target. The trust of this paper focuses on the analytical model which characterizes the influences of these factors for optic design of retro-reflection. This engineering basis is not only necessary for developing practical yet potentially cost-effective optic design concepts, but also provides a useful tool for performance prediction to drive the design for vision-guided part-presentation.

Sensing elements need to be incorporated in robotic welding systems to enable the robot to perceive and adapt to on-line variations occurring in the welding process. In this work, infrared thermal imaging techniques have been used to track variations produced by inadequate control during the joint preparation and fixturing stages. Variations in two joint parameters, gap and position, were studied. Changes in these parameters were found to have peculiar effects on the surface temperature distributions. The observed effects were used to develop quantitative error signals. These error signals were then used to measure the joint gaps and joint-torch offsets in real-time. The joint torch offset error signal was successfully used to control an initial error in joint position during real-time welding.

This paper describes results obtained at the Naval Ocean Systems Center, San Diego, under Independent Exploratory Development funding. The objective was to develop a robust, fully-demountable, high pressure penetrator design suitable for coupling light signals transmitted by optical fiber elements in an undersea cable operated at high ambient hydrostatic pressure into an electronics package or manned space. The feasibility of constructing such penetrators utilizing Graded Refractive INdex (GRIN) rod lenses as combination pressure barriers and imaging devices was demonstrated. Prototype realizations exhibited excellent optical throughput performance and readily survived in excess of 10,000 psi pressure differential as well as tolerating a wide temperature range. The design lends itself to hermetic construction for applications requiring no vapor diffusion over long mission durations. Such devices exhibit excellent potential for satisfying SUBSAFE requirements for manned submersible applications.