Optimal Design of Process Parameters During Laser Direct Metal Deposition of Multi-Material Parts

During the past few years, the need for multi-material parts or heterogeneous objects (HOs) has surfaced with the rapid growth of laser technology, material science and additive manufacturing techniques. Direct Metal Deposition (DMD) process, a metal based additive manufacturing technique, can locally deposit dissimilar metal powders to produce HOs as needed. While some theoretical and experimental studies have been conducted to investigate the DMD process, there are still some challenges such as the process parameters design, optimization, and adjustment during the fabrication of HOs that have not been well elucidated. This dissertation aims at developing the manufacturing science needed to design a laser additive manufacturing system capable of mixing two or more dissimilar powders to manufacture heterogeneous meta-materials objects. This research would enable moving beyond rapid “prototyping†into the realm of functional heterogeneous metal based additive manufacturing (HMAM). Therefore, the objective of this research is to develop the science needed to support the design and manufacture of HOs, placing materials where needed, when needed, in the proportions specified by the design, and combining them in-situ to achieve significant performance enhancements. The dissertation starts by showing the whole picture of the design process, then identify where the challenges and improvement opportunities rest. The whole DMD system design includes the geometrical design of the powder delivering nozzles, the optimal design of the process parameters when depositing dissimilar materials, and the control or planning of the process parameters during the DMD fabrication of HOs. The Laser Engineered Net Shaping (LENSTM) system developed at Sandia and commercialized by Optomec® Inc. is referred to and used to implement the research. An Artificial Neural Network (ANN) based method is proposed using FEM (Finite Element Method) as simulation tool to find the optimal geometry of the injection nozzles in order to maximize the process efficiency. Then, a mathematical model-based design method is proposed combining a multi-objective optimization algorithm to optimize the process parameters including the injection angles, injection velocities, and injection nozzle diameters for the two materials, as well as the laser power and the scanning speed. Finally, a comprehensive study investigating the relationship between the desired part\u27s composition and the process parameters is conducted to fabricate a part with precise composition compared to the heterogeneous components design information. This dissertation provides a better understanding of the physical process in the DMD manufacturing of HOs. This work would help design the whole DMD system, and make it a more efficient, more precise and more flexible process

LASER-ASSISTED PRINTING OF ALGINATE AND CELLULAR TUBES

Laser-assisted printing such as laser-induced forward transfer (LIFT) has found increasing biofabrication applications as an orifice-free cell/organ printing approach. Unfortunately, there have been very few studies on its efficacy of three-dimensional (3D) printing performance. In addition, the effects of printing parameters on jet/droplet formation during the printing of Newtonian and non-Newtonian fluids are lacking. Therefore, it is important to investigate its printing process and quality. The resulting knowledge will help to better control the resulting printing quality and feature resolution. The objective of this study is to investigate the feasibility of laser-assisted 3D printing process and its applicability in making non-cellular and cellular tubes. To better understand the printing process, the effects of fluid properties and operating conditions on the jet/droplet formation process is studied using time-resolved imaging analysis during LIFT of glycerol and sodium alginate (NaAlg) solutions. Operating diagrams regarding different jetting dynamics are constructed. In addition, the effects of NaAlg concentration and operating conditions on the printing quality during laser-assisted printing of alginate annular constructs (short tubes) with a nominal diameter of 3 mm has been studied. It is found in this study that a well-defined jet forms only under certain combinations of glycerol/NaAlg concentration and laser fluence. The inverse of Ohnesorge number is used to characterize the jettability (J) of glycerol solution. An operating diagram regarding J number and laser fluence is constructed for illustrating different printing regimes. An operating diagram is also constructed for NaAlg printing with respect to Deborah number and Reynolds number. It is found that in order to have jet contact-based printing, which is the preferred jetting regime, relatively large Deborah number and Reynolds number are favored. It is also demonstrated that highly viscous materials such as alginate can be printed into well-defined long tubes and annular constructs. The tube wall thickness and tube outer diameter decrease with the NaAlg concentration, while they first increase, then decrease and finally increase again with the laser fluence. Alginate cellular tubes have also been printed with the post-printing cell viability of 60% immediately after printing and 80% after 24 hours of incubation. To better understand the laser-assisted printing mechanism, more experimental and theoretical work on the entire printing process is expected. Prior to practical biomedical applications, printing of high concentration cell suspension to mimic the real tissue environment is desirable. Future work should also include mathematical models accounting for the entire printing process

Mathematics RTI/MTSS Implementation: A Literature Review from the Perspective of Implementation Science

This article reviews published research on implementing the Response to Intervention (RTI)/Multi-tiered System of Support (MTSS) educational framework in mathematics at schools. We utilized the Implementation Driver framework from Implementation Science (Eccles & Mittman, 2006) to analyze current RTI/MTSS implementation practices. Eleven studies qualified to be included in this research. Findings showed more research is needed to expand the investigations in implementation fidelity, systems intervention, facilitative administration, decision-support data systems, coaching, and selection driver

Klondike Elementary School: Serving Purdue and the Local Community and Empowering Students from around the Globe through High-Quality Learning Experiences Accenting Diversity and Inclusion

Disabilities Awareness Program (DAP) aims to cultivate an understanding of disability and inclusion among young children through accessible instructions, age-appropriate activities, and engaging discussions. We want to take this opportunity to introduce one of our school partners - Klondike Elementary School (KES), to recognize its contributions to DAP in providing service-learning opportunities and showcase KES as one of the community partners

Control-Aware Trajectory Predictions for Communication-Efficient Drone Swarm Coordination in Cluttered Environments

Swarms of Unmanned Aerial Vehicles (UAV) have demonstrated enormous potential in many industrial and commercial applications. However, before deploying UAVs in the real world, it is essential to ensure they can operate safely in complex environments, especially with limited communication capabilities. To address this challenge, we propose a control-aware learning-based trajectory prediction algorithm that can enable communication-efficient UAV swarm control in a cluttered environment. Specifically, our proposed algorithm can enable each UAV to predict the planned trajectories of its neighbors in scenarios with various levels of communication capabilities. The predicted planned trajectories will serve as input to a distributed model predictive control (DMPC) approach. The proposed algorithm combines (1) a trajectory compression and reconstruction model based on Variational Auto-Encoder, (2) a trajectory prediction model based on EvolveGCN, a graph convolutional network (GCN) that can handle dynamic graphs, and (3) a KKT-informed training approach that applies the Karush-Kuhn-Tucker (KKT) conditions in the training process to encode DMPC information into the trained neural network. We evaluate our proposed algorithm in a funnel-like environment. Results show that the proposed algorithm outperforms state-of-the-art benchmarks, providing close-to-optimal control performance and robustness to limited communication capabilities and measurement noises.Comment: 15 pages, 15 figures, submitted to IEEE Transactions on Intelligent Vehicle

Planning the Process Parameters During Direct Metal Deposition of Functionally Graded Thin-Walled Parts Based on a 2D Model

The need for functionally graded material (FGM) parts has surfaced with the development of material science and additive manufacturing techniques. Direct Metal Deposition (DMD) processes can locally deposit different metallic powders to produce FGM parts. Yet inappropriate mixing of materials without considering the influence of varying dilution rates and the variation of material properties can result in inaccurate material composition ratios when compared to the desired or computed compositions. Within such a context, this paper proposes a 2D simulation based design method for planning the process parameters in the DMD manufacturing of designed thin-walled parts. The proposed scheme is illustrated through two case studies, one of which is a part with one-dimensional varying composition and the other with two dimensional variation. Using the proposed method, the process parameters can be planned prior to the manufacturing process, and the material distribution deviation from the desired one can be reduced.Mechanical Engineerin

Disability Awareness Program for Young Children: A Community Service-Learning Program at Preschool and Elementary School

Individuals with disabilities account for 26% of Americans and 14% of public education students. This minority group may not only deal with physical and/or mental impairments but also negative societal misunderstandings and attitudes that may lead to decreased friendships and increased social exclusion. Awareness and knowledge about disabilities can play a role in changing people’s attitudes towards disabilities and aid in creating a more positive and inclusive environment. Researchers have found that disability awareness programs in schools can positively teach young children to build positive attitudes about disabilities. In our program, a group of doctoral students helped young children in a local preschool and an elementary school to learn about disabilities through age-appropriate activities. This program collaborated with community partners to design, modify, and decide on activities for each age group. This program was delivered to 80 young children between the ages of two to seven. Feedback from both schools showed that students gained a better understanding of disability and how to appropriately interact with people with disabilities

Non-intrusive measurement and hydrodynamics characterization of gas–solid fluidized beds: a review

Gas-solid fluidization is a well-established technique to suspend or transport particles and has been applied in a variety of industrial processes. Nevertheless, our knowledge of fluidization hydrodynamics is still limited for the design, scale-up and operation optimization of fluidized bed reactors. It is therefore essential to characterize the two-phase flow behaviours in gas-solid fluidized beds and monitor the fluidization processes for control and optimization. A range of non-intrusive techniques have been developed or proposed for measuring the fluidization dynamic parameters and monitoring the flow status without disturbing or distorting the flow fields. This paper presents a comprehensive review of the non-intrusive measurement techniques and the current state of knowledge and experience in the characterization and monitoring of gas-solid fluidized beds. These techniques are classified into six main categories as per sensing principles, electrostatic, acoustic emission and vibration, visualization, particle tracking, laser Doppler anemometry and phase Doppler anemometry as well as pressure fluctuation methods. Trend and future developments in this field are also discussed

Non-intrusive Characterisation of Particle Cluster Behaviours in a Riser through Electrostatic and Vibration Sensing

Particle clusters are important mesoscale flow structures in gas-solid circulating fluidised beds (CFBs). An electrostatic sensing system and two accelerometers are installed on the riser of a CFB test rig to collect signals simultaneously. Cross correlation, Hilbert-Huang transform (HHT), V-statistic analysis, and wavelet transform are applied for signal identification and cluster characterisation near the wall. Solids velocities are obtained through cross correlation. Non-stationary and non-linear characteristics are distinctly exhibited in the Hilbert spectra of the electrostatic and vibration signals, and the cluster dynamic behaviours are represented by the energy distributions of the signal intrinsic mode functions (IMFs). The cycle feature and main cycle frequency of cluster motion are characterised through V-statistic analysis of the vibration signals. Consistent characteristic information about particle clusters is extracted from the electrostatic and vibration signals. Furthermore, a cluster identification criterion for electrostatic signals is proposed, including a fixed and a wavelet dynamic thresholds, based on which the cluster time fraction, average cluster duration time, cluster frequency, and average cluster vertical size are quantified. Especially, the cluster frequency obtained from this criterion agrees well with that from the aforementioned V-statistic analysis. Results from this 3 work provide a new non-intrusive approach to the characterisation of cluster dynamic behaviours and their effects on the flow field

Characterisation of Flow Intermittency and Coherent Structures in a Gas-Solid Circulating Fluidised Bed through Electrostatic Sensing

Flow intermittency and coherent structures are important hydrodynamic phenomena in a gas–solid circulating fluidized bed (CFB). In this work, an electrostatic measurement system based on arc-shaped sensing electrodes is designed and implemented on a CFB test rig. Cross correlation, statistical analysis, wavelet transform, and probability density function (PDF) are applied to the electrostatic signal processing, providing a comprehensive description of the solids velocity, solids holdup, flow intermittency, and coherent structure behaviors. A conditional sampling method is used to extract the coherent structure signals from the electrostatic signals. By comparing the extended self-similarity (ESS) scaling law curves before and after the extraction, the effects of coherent structures on the flow intermittency are further confirmed. Experimental results have demonstrated that the electrostatic signals contain important information about the intermittent hydrodynamic behaviors in a CFB, and the analysis of electrostatic signals through appropriate methods results in an in-depth understanding of the fluidization process