Improving precision of material extrusion 3D printing by in-situ monitoring and predicting 3D geometric deviation using Conditional Adversarial Networks

The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D geometric deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the geometric deviation of the predicted profiles by making compensation to the CAD model

Research on hybrid manufacturing using industrial robot

The applications of using industrial robots in hybrid manufacturing overcome many restrictions of the conventional manufacturing methods, such as small part building size, long building period, and limited material choices. However, some problems such as the uneven distribution of motion accuracy within robot working volume, the acceleration impact of robot under heavy external loads, few methods and facilities for increasing the efficiency of hybrid manufacturing process are still challenging. This dissertation aims to improve the applications of using industrial robot in hybrid manufacturing by addressing following three categories research issues. The first research issue proposed a novel concept view on robot accuracy and stiffness problem, for making the maximum usage of current manufacturing capability of robot system. Based on analyzing the robot forward/inverse kinematic, the angle error sensitivity of different joint and the stiffness matrix properties of robot, new evaluation formulations are established to help finding the best position and orientation to perform a specific trajectory within the robot\u27s working volume. The second research issue focus on the engineering improvements of robotic hybrid manufacturing. By adopting stereo vision, laser scanning technology and curved surface compensation algorithm, it enhances the automation level and adaptiveness of hybrid manufacturing process. The third research issue extends the robotic hybrid manufacturing process to the broader application area. A mini extruder with a variable pitch and progressive diameter screw is developed for large scale robotic deposition. The proposed robotic deposition system could increase the building efficiency and quality for large-size parts. Moreover, the research results of this dissertation can benefit a wide range of industries, such as automation manufacturing, robot design and 3D printing --Abstract, page iv

Nonlinear Microscopy for Material Characterization

Making use of femtosecond laser sources, nonlinear microscopy provides access to previously unstudied aspects of materials. By probing third order nonlinear optical signals determined by the nonlinear susceptibility chi(3), which is present in all materials, we gain insight not available by conventional linear or electron microscopy. Third-harmonic (TH) microscopy is applied to supplement laser-induced damage studies of dielectric oxide thin film optical coatings. We present high contrast (S/N\u3e 100 : 1) TH imaging of ~17 nm nanoindentations, individual 10 nm gold nanoparticles, nascent scandia and hafnia films, and laser induced material modification both above and below damage threshold conditions in hafnia thin-films. These results imply that TH imaging is potentially sensitive to laser-induced strain as well as to nanoscale defects or contamination in oxide films. Compared to other sensitive imaging techniques such as Nomarski and dark field, TH imaging exhibits dramatically increased sensitivity to typical material modifications undergone during the formation of optical damage as evidenced by a dynamic range 10^6 : 1. Four-wave mixing (FWM) microscopy is employed to investigate delay dependent FWM signals and their implied characteristic resonant response times in multiple solvents. Mathematical modeling of resonant coherent anti-Stokes Raman scattering (CARS), coherent Stokes Raman scattering (CSRS) and stimulated parametric emission (SPE) processes supplement the FWM studies and suggest a resonant CARS process that accounts for ~95% of the total visible FWM signal which probes a characteristic material response time ~100 fs. This signal enhancement likely indicates the net effects of probing several Raman active C-H stretch bands near 2950 cm^-1. This FWM technique may be applied to characterize the dominant resonant response of the sample under study. Furthermore this technique presents the newfound capability to provide estimates of characteristic material dephasing times in combination with potential spatial resolution ~1 micron. In addition to TH and FWM microscopy, a genetic algorithm is developed and implemented that allows for the synthesis of arbitrary temporal waveforms to maximize the generation of nonlinear optical signals in the focal plane of a microscope without any prior knowledge of the experiment. This algorithm is demonstrated to compensate high order optical dispersion and thereby increase TH microscopy signals ~10x in a fused silica sample

Investigation into adaptive slicing methodologies for additive manufacturing

Adaptive slicing is a methodology used to optimise the trade-off between build-time reduction and geometric accuracy improvement in additive manufacturing (AM). It works by varying decreasing layer thickness in sections of high curvature. However, current adaptive slicing methodologies all face the difficulty of adjusting layer thickness precisely according to the variations of the model’s geometry, thereby limiting the geometric accuracy improvement. This thesis tackles this difficulty by indicating the geometric variations of the model by evaluating the ratio of the volume of each sliced layer’s geometric deviation to the volume of its corresponding region in the digital model. This indication is accomplished because all the topological information of the corresponding region is considered in assessing the geometric deviation (volume) between each sliced layer and its corresponding region. Through having this precise indication to modify each layer thickness, this thesis aims to develop an adaptive slicing that can mitigate geometric inaccuracies (e.g. staircase effect and dimensional deviation) while balancing the build time. This slicing is evaluated using six different test models, compared with three current slicing methodologies (voxelisation-based, cusp height-based, and uniform slicing), and validated through computation and manufacturing. These validations all demonstrate that volume deviation-based slicing optimises the trade-off between build-time reduction and geometric accuracy improvement better than the other existing slicing methodologies. For example, it can reduce the build time by nearly half compared to other existing slicing methodologies assuming a similar degree of printed parts’ geometric accuracy. The improved trade-off optimised by volume deviation-based slicing can directly benefit the AM applications in the aerospace and medical industries. This is because current research has shown geometric inaccuracies are the primary cause of reducing energy efficiency (e.g. turbine blade and wind tunnel testing models) and having failed implants (e.g. hip and cranial implants, dental prostheses). In addition to improving the geometric accuracy of AM-constructed parts, volume deviation-based slicing may also be incorporated with non-planar layer slicing. Non-planar layer slicing is designed to mitigate the mechanical anisotropy of printed parts by using curved-sliced layers. By integrating volume deviation-based slicing with non-planar layer slicing, the thickness of each curved-sliced layer can be adjusted according to the model’s geometric variations and, therefore, has a possibility of reducing the geometric inaccuracies and mechanical anisotropy simultaneously.Open Acces

An experimental investigation into the dimensional error of powder-binder three-dimensional printing

This paper is an experimental investigation into the dimensional error of the rapid prototyping additive process of powder-binder three-dimensional printing. Ten replicates of a purpose-designed part were produced using a three-dimensional printer, and measurements of the internal and external features of all surfaces were made using a general purpose coordinate measuring machine. The results reveal that the bases of all replicates (nominally flat) have a concave curvature, producing a flatness error of the primary datum. This is in contrast to findings regarding other three-dimensional printing processes, widely reported in the literature, where a convex curvature was observed. All external surfaces investigated in this study showed positive deviation from nominal values, especially in the z-axis. The z-axis error consisted of a consistent positive cumulative error and a different constant error in different replicates. By compensating for datum surface error, the average total height error of the test parts can be reduced by 25.52 %. All the dimensional errors are hypothesised to be explained by expansion and the subsequent distortion caused by layer interaction during and after the printing process

An investigation into energy-material properties interaction in additive manufacturing of Polymers.

Additive manufacturing (AM), known as three-dimensional (3D) printing, is a fabrication process to build 3D objects layer by layer based on computer aided design (CAD) model or digital 3D model. Fused filament fabrication (FFF) has become a preferred method for additive manufacturing due to its cost-effectiveness and flexibility. However, the parts built using FFF process suffer from lower mechanical strength compared to that fabricated using traditional method and rough surface finish. With this motivation, this dissertation aims to develop and implement a novel in-process laser assisted technique on FFF to heal the microstructure of FFF built objects by enhancing reptation and relaxation to improve mechanical strength and to heal the surface by increasing surface reflow. This technique utilizes laser energy to reduce with residual stress generated by the extrusion-based deposition process, and to heal interfaces between deposited tracks for improvement of interface adhesion, therefore increase mechanical strength. This dissertation demonstrates that the in-process laser assisted technique can fabricate nearly isotropic object with mechanical strength close to solid bulk material. It also demonstrates the capability of reducing the surface roughness significantly. This dissertation investigates in two directions, the first direction is mechanical strength and mechanical behaviors. In-process pre-laser heating was used to enhancing mechanical strength at inter-layer interface (Z-direction), at the interface between adjacent tracks (Y-direction), and along the deposited track(X-direction). The second direction is surface finish of the side surface. In order to quantify the interaction of laser energy on material structure, laser output power, laser melting pool temperature, mechanical strength were measured. SEM were used to characterize the fracture surface to determine the effect of laser on interface healing

Design for metal fused filament fabrication (DfMF3) of Ti-6Al-4V alloy.

Additive manufacturing (AM) offers unmatchable freedom of design with the ability to manufacture parts from a wide range of materials. The technology of producing three-dimensional parts by adding material layer-by-layer has become relevant in several areas for numerous industries not only for building visual and functional prototypes but also for small and medium series production. Among others, while metal AM technologies have been established as production method, their adoption has been limited by expensive equipment, anisotropy in part properties and safety concerns related to working with loose reactive metal powder. To address this challenge, the dissertation aims at developing the fundamental understanding required to print metal parts with bound metal powder filaments using an extrusion-based AM process, known as metal fused filament fabrication (MF3). MF3 of Ti-6Al-4V has been investigated, owing to significant interest in the material from aerospace and medical industries on account of their high strength-to-weight ratio, excellent corrosion resistance and biocompatibility. To investigate the material-geometry-process interrelationship in MF3 printing, the current work looks into the process modeling and simulation, the influence of material composition and resulting characteristics on printed part properties, effects of printing parameters and slicing strategies on part quality, and part design considerations for printability. The outcome of the work is expected to provide the basis of design for MF3 (DfMF3) that is essential to unlocking the full potential of additive manufacturing. Moreover, the layer-by-layer extrusion-based printing with the highly filled material involves several challenges associated with printability, distortion and dimensional variations, residual stresses, porosity, and complexity in dealing with support structures. Currently, a high dependency on experimental trial-and-error methods to address these challenges limits the scope and efficiency of investigations. Hence, the current work presents a framework of design for MF3 and evaluates a thermo-mechanical model for finite element simulation of the MF3 printing process for virtual analyses. The capability to estimate these outcomes allows optimization of the material composition, part design, and process parameters before getting on to the physical process, reducing time and cost. The quantitative influence of material properties on MF3 printed part quality in terms of part deformation and dimensional variations was estimated using the simulation platform and results were corroborated by experiments. Also, a systematic procedure for sensitivity analysis has been presented that identified the most significant input parameters in MF3 from the material, geometry and process variables, and their relative influence on the print process outcome. Moreover, feasible geometry and process window were identified for supportless printing of Ti-6Al-4V lattice structures using the MF3 process, and an analytical approach has been presented to estimate the extrudate deflection at the unsupported overhangs in lattice structures. Finally, the design and fabrication of Ti-6Al-4V maxillofacial implants using MF3 technology are reported for the first time confirming the feasibility to manufacture patient-specific implants by MF3. The outcome of the work is an enhanced understanding of material-geometry-process interrelationships in MF3 governing DfMF3 that will enable effective design and manufacturing

DETERMINATION AND COMPENSATION OF THE SHRINKAGE BEHAVIOR OF CYLINDRICAL ELEMENTS IN THE FDM PROCESS

Fused Deposition Modeling (FDM) is an additive manufacturing process to produce complex thermoplastic geometries layer by layer. The filament is melted in a nozzle, iteratively deposited, and then cools down. Due to the solidification process, the deposited filament strands deviate from their intended position due to shrinkage, resulting in significant geometric deviations in the final part. In terms of dimensional accuracy, there is a need for optimization, especially for local curved geometries in relation to the global part with higher nominal dimensions. The aim of this study is to investigate the size and shape deviations for cylindrical FDM elements and to compensate the expected deformations by using an in-house software with adaptive scaling factors in the x-y plane. Previous studies mainly focus on simple, non-curved objects, this study also considers the influence of curvature and global as well as local deviations on the final part.Mechanical Engineerin