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

Defect Detection Limits for Additively Manufactured Parts Using Current Thermography Techniques

Additive manufacturing (AM) will support NASA in their moon and mars missions by reducing the amount of redundant equipment carried into space and by providing crew members with the flexibility to design and create parts as needed. The ability to monitor the quality of these additively manufactured parts is critical, especially when using recycled or in-situ materials as NASA plans to do. This project assesses the possibility of detecting small, shallow AM defects with existing active thermography techniques. An axisymmetric, numerical model was created in COMSOL to simulate the heat transfer within AM structures during active thermography. The effects of surface convection, heat conduction through the subsurface defect, and radiative in-depth absorption were included in the model. The simulation results estimate the minimum detectable defect diameter for a given defect depth using a common thermography technique. Additionally, the data demonstrates conditions for which 1D thermography models may be applied to 3D systems

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals

Design and Fabrication of a Polymer FDM Printer Capable of Build Parameter Monitoring and In-Sit Geometric Monitoring Via Photogrammetry

Additive manufacturing, or 3D printing, is a complex process that creates free-form geometric objects by sequentially placing material in a location to construct an object, usually as a layer-by-layer process. One of the most widespread methods is Fused Deposition Modeling (FDM). FDM is used in many of the consumer-grade polymer 3D printers available today. While consumer grade machines are cheap and plentiful, they lack many of the features desired in a machine used for research purposes and are often closed-source platforms. Commercial-grade models are more expensive and are also usually closed-source platforms that do not offer flexibility for modifications often needed for research. This research focuses on the design and fabrication of a machine to be used as a test bed for research in the field of polymer FDM processes. The goal was to create a platform that tightly controls and/or monitors the FDM build parameters so that experiments can be repeated with a known accuracy. The platform offers closed loop position feedback, control of the hot end and bed temperature, and monitoring of environment temperature and humidity. Additionally, the platform is equipped with cameras and a mechanism for in-situ photogrammetry, creating a geometric record of the print throughout the printing process. Through photogrammetry, backtracking and linking of process parameters to observable geometric defects can be achieved. The controls system and instrumentation are built on an open flexible paradigm enabling customization as necessary for future research

Data-driven prediction modeling for part attributes and process monitoring in additive manufacturing

The first study aimed to use artificial neural networks (ANN) to predict how process parameters would affect the part attributes in an extrusion-based additive manufacturing (AM) process. The study involved parts fabrication using an orthogonal array experimental design with five process parameters at three levels: building orientation, print speed, extrusion temperature, deposition direction, and layer thickness. The fabricated parts were measured for dimensional accuracy, surface roughness, and tensile strength. These attributes were then used to train, validate, and test multilayer ANN models. Three of the four ANN models were for predicting each of the three-part qualities separately, while the fourth was for combining all three attributes. Regarding RMSE and correlation coefficient, the findings showed that the individual part attribute ANN models outperformed the model for combining three attributes. To determine which parameters had a higher impact on the individual part qualities, comparisons between the individual part attributes with respect to the process parameters were made. The trained ANN models can forecast and optimize the part properties in extrusion-based AM processes. The second research developed a new method of collecting time series data for process monitoring in a Fused Filament Fabrication (FFF) system using wireless sensors to predict the machine bed angular velocity of FFF using the Vanilla Long Short-Term Memory (VLSTM) network. With two levels, the printer speed and deposition direction of the nozzle head were used in this study following a full factorial experimental design to investigate their effects on machine vibration during printing. Time series machine bed angular velocity data were collected and used to train and test the proposed VLSTM network. Adam optimizer and VLSTM networks with four cells generated the best training accuracy after 100 epochs. One developed VLSTM model was used to train and test the network by inserting four-time series machine bed angular velocity data. Then four-time series simulation results were investigated to analyze the outputs of our developed and trained model. Simulation and experimental results were analyzed using root mean square error (RMSE). Practical data analysis concluded that the deposition direction of the nozzle head and printer speed both significantly affected the angular velocity of the printer bed. The developed VLSTM model can be used to predict the FFF printer bed angular velocity having different unexplored printer speeds and deposition directions, which will eventually help predict the quality of the printed parts through machine vibration analysis

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Monitoring Additive Manufacturing Machine Health

Additive manufacturing (AM) allows the production of parts and goods with many benefits over more conventional manufacturing methods. AM permits more geometrically complex designs, custom and low-volume production runs, and the flexibility to produce a wide variety of parts on a single machine with reduced pre-production cost and time requirements. However, it can be difficult to determine the condition, or health, of an AM machine since complex designs can increase the variability of part quality. With fewer parts produced, destructive testing is less desirable and statistical methods of tracking part quality may be less informative. Combined with the relatively more complex nature of AM machines, qualifying AM machines and monitoring their health to perform maintenance or repairs is a challenging task. We first present a case study that demonstrates the difficulty of monitoring the qualification of an AM machine. We then discuss some unique challenges AM presents when calibrating and taking measurements of laser power, and we demonstrate the relative insufficiency of this method in tracking the qualification status of an AM machine and the quality of the parts produced. Next, we present a framework that reverses the directionality of monitoring AM machine health. Rather than monitoring machine subsystems and intermediate metrics reflective of part quality, we instead directly monitor part quality through a combination of witness builds and witness parts that provide observational data to define the health status of a machine. Witness builds provide more accurate data separated from the noisy influence of parts and parameter settings, while witness artifacts provide more timely data but with less accuracy. Finally, machine health is modeled as a partially observed Markov decision process using the witness parts framework to maximize the long-term expected value per build. We show the superiority of this model by comparison to two less complex models, one that uses no use no witness parts and another that uses only witness builds. A case study shows the benefits of implementing the model, and a sensitivity analysis is performed to show relevant insights and considerations

Advances in structural analysis and process monitoring of thermoplastic composite pipes.

Thermoplastic composite pipes (TCP) in comparison to other pipes have proven beneficial features due to its flexibility which includes being fit for purpose, lightweight and no corrosion. However, during the manufacturing of TCP which involves the consolidation process, certain defects may be induced in it because of certain parameters, and this can affect the performance of the pipe in the long run as the induced defects might lead to in-service defects. Current techniques used in the industry are facing challenges with on-the-spot detection in a continuous manufacturing system. In TCP manufacturing process, the pipe is regularly monitored. When a defect is noticed, the whole process stops, and the appropriate action is taken. However, shutting down the process is costly; hence it is vital to decrease the downtime during manufacturing to the barest minimum. The solutions include optimizing the process for reduction in the manufacturing defects amount and thoroughly understanding the effect of parameters which causes certain defect types in the pipe. This review covers the current state-of-the-art and challenges associated with characterizing the identified manufacturing induced defects in TCP. It discusses and describes all effective consolidation monitoring strategy for early detection of these defects during manufacturing through the application of suitable sensing technology that is compatible with the TCP. It can be deduced that there is a correlation between manufacturing process to the performance of the final part and selection of characterization technique as well as optimizing process parameters

Novel PVA/PLA-Graphene Three-Dimensional Printed Nanocomposites for Gas Sensing, Adsorption-Desorption Cooling and Electrochromic Coatings

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNovel PVA hydrogel nanocomposites, symmetric three-dimension (3D) printed poly(vinyl) alcohol (PVA) hydrogels and asymmetric 3D printed PVA/poly(lactic) acid (PLA)-graphene (G) nanocomposites have been prepared and characterised in detail using Fourier transform IR (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and Raman spectroscopy. The aim was to produce new nanomaterials in part by fused deposition modelling (FDM) and to evaluate them as: (i) Personal sensors for sensing of (a) pollutants (e.g. NO/NO2, SO2, VOCs/PAHs and particulates) in the atmosphere that might adversely affect human health. With improved response time, sensitivity, selectivity, recovery time and stability and an improved low cost spatial and temporal resolution (b) oil that pollutes water and soil. (ii) Localised sensors for CO2 that could be fine-tuned by water and monoethanolamine (MEA) incorporation to provide feedback on process efficiency affecting global warming and breathing efficiency. (iii) Coatings for improved adsorption-cooling of Al-clad building surfaces, lowering power consumption for air conditioning. (iv) Coatings for improved electrochromic displays. Practical applications of such materials are remote patient monitoring through real-time breath analysis, synthetic sweating surfaces for residential and industrial buildings, wearable sensors connected to the IoT and solar reactive glass. 0.045mmol of CO2 produced a conductance change in a hydrogel in 4s for a 3D printed PVA hydrogel with on-board H2O; 1-1.5s slower than a quadrupole mass spectrometer (RGA). It could be that hydrogel sensors form an inexpensive method for breathing analysis and remote patient monitoring; tailoring for SO2 or NO2 detection remains to be seen. 3D printed PLA/G is unlikely to compete with vapour-deposited metal electrodes for detection of oil-in-water. Infra-red thermography (IRT) showed that novel hydrogel coatings exhibited a larger cooling effect (-13K when H2O swollen, -10K when MEA swollen and -11.8K when H2O swollen asymmetric sample) than previously (SPIE) for over 4000s on a construction-type Al substrate and even a 2.6K cooling with PVA hydrogel/Al substrate over a 25h period. “Real-world” IRT was also conducted with PVA-MxOy nanocomposite hydrogels and revealed that the species of MxOy affects the thermal signature of the gel differently when viewed against a background. More importantly, reversible electrochromic responses in novel WO3/PVA hydrogels (with and without additional salt) seen in <1s, open the possibility of colour switchable cool coatings for buildings. Addition of various salts enabled initial tuning of colour to be achieved