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George W. Woodruff School of Mechanical Engineering

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https://hdl.handle.net/1853/70728

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College of Engineering

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Advanced Mechanical Bipedal Experimental Robotics Lab

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Computational Reactor and Medical Physics Laboratory

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Cryo Lab

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Environmentally Conscious Design and Manufacturing

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Fusion Research Center

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https://finding-aids.library.gatech.edu/agents/corporate_entities/120

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Publication Search Results

Now showing 1 - 10 of 13

Build Plate Conduction Cooling for Thermal Management of Wire Arc Additive Manufactured Components

(Georgia Institute of Technology, 2021-10-22) Heinrich, Lauren E.

Wire arc additive manufacturing (WAAM) is often implemented due to its lower investment cost and equipment availability as compared to other metal additive manufacturing techniques. WAAM adds large amounts of thermal energy to a component during the build process, and depending on the size of the component, the cool-down time can be more significant than the deposition time, decreasing productivity. Conduction cooling applied to the build plate was proposed as a strategy to decrease overall part temperature faster than natural energy dissipation, enabling shorter cool down time with comparable part quality. Based on the challenges presented above, the major research question addressed in this thesis is: How does build plate conduction cooling affect process outcomes of WAAM depositions including process time and quality? This thesis presents the results of conduction cooling applied to the build plate of WAAM components using Gas Metal Arc Welding (GMAW) as the additive process. The present results show that build plate conduction cooling can be used to thermally isolate fixturing, decrease the dwell time by 50%, and decrease the cool down to room temperature time by 75%. Build plate cooling was shown to keep the fixturing at 33% the temperature of the conventional sample, indicating that cooling can be used to thermally isolate the fixturing from the build plate of the deposition. The thermal results of the deposition were also accurately modeled using Finite Element Analysis (FEA) to help determine the maximum temperatures achieved during the depositions. The user can set up the cooling system before the deposition begins and the approach does not require user adjustment throughout the deposition time making this system more user friendly than other processes investigated in literature. The system can also be implemented in retrofit systems without major system modification which decreases cost compared to other thermal isolating techniques.
Ensemble Machine Learning Model Generalizability and its Application to Indirect Tool Condition Monitoring

(Georgia Institute of Technology, 2021-08-24) Schueller, Alexandra Gayle

A practical, accurate, robust, and generalizable system for monitoring tool condition during a machining process would enable advancements in manufacturing process automation, cost reduction, and efficiency improvement. Previously proposed systems using various individual machine learning (ML) models and other analysis techniques have struggled with low generalizability to new machining and environmental conditions, as well as a common reliance on expensive or intrusive sensory equipment which hinders their industry adoption. While ensemble ML techniques offer significant advantages over individual models in terms of performance, overfitting reduction, and generalizability improvement, they have only begun to see limited applications within the field of tool condition monitoring (TCM). To address the research gaps which currently surround TCM system generalizability and optimal ensemble model configuration for this application, nine ML model types, including five heterogeneous and homogeneous ensemble models, are employed for tool wear classification. Sound, spindle power, and axial load signals are utilized through the sensor fusion of practical external and internal machine sensors. This original experimental process data is collected through tool wear experiments using a variety of machining conditions. Four feature selection methods and multiple tool wear classification resolution values are compared for this application, and the performance of the ML models is compared across metrics including k-fold cross validation and leave-one-group-out cross validation. The generalizability of the models to data from unseen experiments and machining conditions is evaluated, and a method of improving the generalizability levels using noisy training data is examined. T-tests are used to measure the significance of model performance differences. The extra-trees ensemble ML method, which had never before been applied to signal-based TCM, shows the best performance of the nine models.
A DEEP LEARNING APPROACH TO ADDITIVE MANUFACTURING PROCESS MONITORING AND CONTROL

(Georgia Institute of Technology, 2021-05-12) Klesmith, Zoe

Currently, determining the layer height command to keep a consistent standoff distance in directed energy deposition (DED) is a time and resource intensive trail-and-error process. Additionally, the same is true for choosing process parameters to prevent over and under deposition for part geometries with small-angled corners and high curvature features. Due to the complexity of the additive manufacturing (AM) process as well as the uniqueness of each part geometry, further development of real-time process monitoring, and control is needed for reliable part dimensional accuracy. Specifically, the use of deep learning (DL) offers a promising solution for real-time process control due to DL’s ability to recognize complex, nonlinear patterns with high accuracy. In this thesis, a set of experiments were designed to train two machine learning models to indirectly detect bead height as well as standoff distance from a coaxial image of the melt pool. The performance of these models was accessed, and it was found that including the laser’s reflection on the nozzle in the image greatly increased prediction accuracy. Overall, the top mean accuracy achieved was 99.74% for standoff distance classification, and the top mean accuracy achieved was 99.87% for bead height classification.
Bead Geometry, Microstructure and Texture in Multi-Axis Directed Energy Deposition

(Georgia Institute of Technology, 2020-07-30) Elsayed, Omar Hesham

The purpose of this study is to examine the effect of varying laser incidence angles on textural, microstructural and geometric characteristics of DED-processed materials, provide a more comprehensive outlook on participating laser-matter interaction phenomena and ultimately devise strategies to ameliorate print performance. In this study, single-layer, single-/multi-track specimens were processed to examine the effect of non-orthogonal angular configurations on bead morphology, microstructure, phase composition and textural representation of DED-processed materials. Laser power measurements were also conducted to understand the effect(s) of laser spot size changes and laser attenuation on the effective power reaching the substrate surface. It has been observed that bead morphology experienced a normal curve behavior with increasing lead angles, while it experienced a decrease with decreasing lean angles. The orthogonal setting exhibited the most promising bead morphology values. An asymmetry in the distribution of the bead morphology plots indicates that there is preferential bias in utilizing a certain angular configuration over another in terms of potential incorporation of non-orthogonal deposition operations in industrial applications. No significant differences in phase composition, texture and microstructure were observed with processed samples of various angular configurations as well as raw, unprocessed powders, indicating that a potential route for enhanced process robustness is achievable without significantly affecting bulk material properties. In unfocused beams, laser incidence angles have little effect on the extent of laser power loss due to both laser spot size variations as well as powder cloud laser attenuation, indicating that more involved studies are required.
Surface qualification toolpath optimization for hybrid manufacturing

(Georgia Institute of Technology, 2020-07-21) Thien, Austen Edward

Hybrid manufacturing machine tools have been shown to have great potential in revolutionizing the manufacturing of components by combining both additive manufacturing (AM) and subtractive manufacturing (SM) processes on the same machine tool. However, a prominent issue that can occur when going from AM to SM is that the toolpath for the SM process does not take into account the geometric discrepancies caused by the previous AM step. Thus, the toolpaths used for the SM process are inefficient and can lead to increased production times and increased tool wear, particularly in the case of wire-based directed energy deposition (DED). This work discusses a methodology for approximating the geometric surface of parts manufactured using an on-machine touch probe to gather geometric data and create a digital twin of the part surface. Three different geometric approximation methods using minimal probe points are formed: triangular, trapezoidal, and an augmented hybrid of the two. Optimized SM toolpaths are created using each geometric approximation with multiple objectives of reducing total machining time, surface roughness, and cutting force. Different prioritization scenarios of the multi-objective optimization goals are evaluated to determine efficiency and quality trade-offs. Based on multi-objective optimization results for all prioritization scenarios and a comparison of the toolpaths generated for each geometric approximation, the optimal geometric surface approximation is determined to be the augmented geometric approximation. Furthermore, it is shown that prioritization of the machining time and cutting force optimization goals leads to poor performance improvements in the other optimization goals.
Qualification of laser powder bed fusion processed 17-4 PH stainless steel as a function of powder condition

(Georgia Institute of Technology, 2020-07-21) Berez, Jaime Michael Schnaier

The metal additive manufacturing process of laser powder-bed fusion (LPBF) presents a challenge to develop qualified processes to match the rapid pace of technology development. An aspect of the LPBF process where this applies is defining the how powder feedstock conditions affect the quality of produced components. This study examines how in-machine powder feedstock supplies evolve and are otherwise affected during the LPBF process, and how these effects impact subsequent builds which use said feedstock. An examination of powder flowability, rheology, and morphology is conducted to characterize the powder conditions. To study the effects, an assessment of produced component tensile, fatigue, and microstructural properties is conducted. Fatigue life is analyzed using a reliability modeling approach in order to provide detailed statistical conclusions often missing in other analyses. Powders are found to evolve their characteristics over exposure to repeated LPBF processes, particularly in the extremes of powder size distribution and measures of bulk flow. No significant effects on microstructural, hardness, tensile, and fatigue properties of the produced components are shown. Fatigue life is discovered to exhibit a dependence on spatial origin of the produced component. Additionally, a detailed characterization of the scatter in fatigue life typical to the process and material is provided.
The effectiveness of various chatter detection methods under noisy conditions

(Georgia Institute of Technology, 2020-05-17) Lu, Lance C.

Unmanned operations are sought after in manufacturing processes such as milling and lathing. During these processes, the detection and mitigation of machine tool chatter is critical. The veracity of these methods under noise conditions that would be found in a live factory environment is not well understood. This study aims to evaluate the performance of various classification methods for the detection of chatter under periodic and white noise. Different training methods and artificial noise injection are used to highlight the benefits and pitfalls of the different methods for chatter detection. It is found that machine learning models like Support Vector Machines have a significant ability to classify noisy data even when untrained on noise.
Using machine learning for anomalous toolpath identification in subtractive manufacturing

(Georgia Institute of Technology, 2020-04-28) Nguyen, Edward Pham

The emphasis and application of machine learning with respect to manufacturing and machining has focused primarily on tool wear or bearing health. Few studies have focused on the parts produced by these processes and how changing parameters during machining operations can affect the final outcome. Quality control is a costly but necessary step in the manufacturing process to ensure that a finished part meets specification. For a machined part, this is usually accomplished using inspection and measurement techniques. However, inspection of a machined part has typically occurred after certain predetermined milestones. This study aims to identify and classify machining phenomenon compared to a reference signal to determine if the toolpath mimics reflects the intended behavior. To accomplish this, a Computer Numerical Control (CNC) milling machine is instrumented with accelerometers to track and record vibrations. This data is collected from the spindle and processed using a machine learning algorithm that segregates signatures based on selected features and classifies them as expected behaviors or anomalous. The results of the study indicate that certain phenomena can be accurately identified and labeled as normal or abnormal with respect to feed rate or spindle speed overrides. It is a promising insight into more complex toolpath identification and integration with Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) software to anticipate and mitigate machining errors.
An ensemble machine vision system for automated detection of surface defects in aircraft propeller blades

(Georgia Institute of Technology, 2020-04-28) Ren, Ivan

Visual inspections comprise the majority of inspections for large transport aircraft. The traditional inspection process is time-consuming, inconsistent, and subject to human errors. Automated defect detection systems have been developed to leverage computer vision and deep learning to decrease inspection times and improve detection performance. Prior methodologies have used convolutional neural networks to detect defects from image data. The performance of these methods is insufficient for critical aircraft inspection. Furthermore, the tradeoff between error rates of false alarms and missed detections has not been well addressed in the literature. This thesis presents a novel application of deep learning ensembles to automated aircraft visual inspection and provides a methodology for using ensembles to manage the error rates of the algorithm. Stacked ensembles are constructed from three deep learning base learners and a logistic regression meta-learner is used to combine their predictions. The performance of the stacked ensembles is evaluated, and it is found that the stacked ensembles outperform the current state-of-the-art defect detection approaches. Furthermore, it is shown that with sufficient error diversity, ensembles can be constructed to eliminate the missed detections that may lead to critical failures in aircraft inspection.
Detection of bearing defects with approximate bearing configuration

(Georgia Institute of Technology, 2019-07-10) Prevost, Eymard Antoine Marie Edudes

Unscheduled maintenance in a production line due to breakdowns is highly detrimental. The ability to predict impending failure and anticipate it is a high value proposition. Such a prediction can be achieved by monitoring components that are known to fail often in mechanical systems, such as bearings. Prior research has led to the development of bearing monitoring approaches widely used today. However, one of the main challenges is the fact that there is often incomplete information about the systems. This thesis will focus on approaches that can be employed to detect bearing defects and incipient bearing failure in the presence of incomplete and inaccurate system knowledge.