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Articles 1 - 11 of 11
Full-Text Articles in Manufacturing
Smart Process Planning For Automated Fiber Placement, Matthew J. Godbold
Smart Process Planning For Automated Fiber Placement, Matthew J. Godbold
All Dissertations
Automated Fiber Placement (AFP) is a preeminent manufacturing technology for producing high-performance composite structures, offering precise material placement, reduced waste, and the potential for highly optimized lightweight designs. While AFP has been widely adopted in aerospace applications, its broader use across industries such as automotive, wind energy, maritime, and sporting goods remains limited. A key barrier to wider adoption is the traditional separation of design, manufacturing, and inspection processes, which drives up cost and results in inefficiencies, sub-optimal designs, and limited feedback between lifecycle stages. In current practice, manufacturing is treated as an isolated process, disconnected from early-stage design decisions …
Modernizing Plastics And Composites Manufacturing Using Hybrid Modeling Techniques, Luke Shannon
Modernizing Plastics And Composites Manufacturing Using Hybrid Modeling Techniques, Luke Shannon
All Dissertations
Plastics and composites manufacturing encompasses a broad family of processes, including injection molding, resin transfer molding, compression molding, and extrusion, that together account for the vast majority of polymer and composite component production worldwide. These processes are inherently challenging due to the multi-physics nature of polymer processing, the vast possible combinations of resins, reinforcements, and additives, and the strong dependence of final part properties on process history. Among these processes, injection molding is one of the most widely deployed, producing high-volume components across automotive, medical, consumer electronics, and packaging industries. However, accurately monitoring and predicting process behavior in injection molding …
Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan
Human Comfort Modeling, Measurement, And Improvement In Human–Robot Collaboration, Yuchen Yan
All Dissertations
A dissertation is proposed to explore human comfort in human-robot collaboration (HRC) through modeling, prediction, and enhancement methodologies. Human comfort is a crucial yet underexplored factor in HRC, directly influencing task efficiency, trust, and overall collaboration effectiveness. Understanding the influential factors, developing computational models, and refining methods to improve human comfort in HRC are essential steps toward advancing the field of collaborative robotics. To address these challenges, multiple studies have been conducted. A series of experimental studies were performed to investigate how robot motion-based parameters affect human comfort in HRC. These studies examined both analytical comfort modeling approaches and physiological …
An Efficient Computational Frameworks For Design And Analysis Of Metamaterials, Raj Pradip Khawale
An Efficient Computational Frameworks For Design And Analysis Of Metamaterials, Raj Pradip Khawale
All Dissertations
Advancement in additive manufacturing helps in building artificial lattice structures with unique properties that are not available in naturally occurring materials or in continuum structures. Specifically, beam-based lattices are well known for producing lightweight structures with very high strength, auxetic behavior, and energy absorption capabilities. In recent years, numerous research studies have been conducted on generating algorithms and frameworks to obtain unusual properties based on the variation in the cell geometry and material properties. However, the exploration of the full design space is hampered in practice primarily due to restrictions on cell tiling variation. Additionally, the lattices are very intricate, …
Neural Operator And Physics-Informed Deep Learning Approaches For Inverse Design Of Composites And Manufacturing Processes, Minglei Lu
All Dissertations
In this dissertation, artificial intelligence (AI) models are designed and used to accelerate inverse design of composites and manufacturing processes. The critical bottlenecks in machine learning (ML) including data availability, data quality, model generalization and adaptation, interpretability, physical consistency, and the ’black box’ nature of models for the inverse design are addressed. And the proposed AI models are tested under different engineering scenarios. Firstly, a fast deep neural operator (DNO) structure was developed to significantly reduce training time. This model was tested in the context of additive manufacturing, a transformative industrial technology that allows for the creation of materials with …
Study Of Electrospun Nanocomposite Nanofiber Nonwoven Mats With Precursor Solutions Prepared By Ultrasonic Processing, Stephanie Tussey
Study Of Electrospun Nanocomposite Nanofiber Nonwoven Mats With Precursor Solutions Prepared By Ultrasonic Processing, Stephanie Tussey
All Dissertations
Understanding the complexity of changes in a polymer solution through the addition of nanoparticles and the process used to prepare the solutions is vitally important to obtain desirable characteristics of electrospun nanofibers such as degree of crystallinity, morphological, thermal and mechanical properties. Although there have been significant efforts in this research area, a more in-depth understanding on how nanoparticles can affect the formation of polymeric structures during electrospinning is still needed to ensure overall quality and performance of the nonwoven material. The present study aimed to specifically explore the relationship between solution processing method, nanoparticle volume fraction, and solution properties …
A Manufacturing-To-Response Pathway For Manufacturing Optimization Of Carbon Fiber Reinforced Polymer Composite Structures, Madhura Limaye
A Manufacturing-To-Response Pathway For Manufacturing Optimization Of Carbon Fiber Reinforced Polymer Composite Structures, Madhura Limaye
All Dissertations
Over the past decade, there has been an increased adoption of thermoplastic and thermoset based continuous carbon fiber reinforced polymer (CFRP) composites for structural applications in several industries. Among the different manufacturing methods, thermoforming process for thermoplastic based continuous CFRP’s offer a major advantage in reducing cycle times for large scale productions. Similarly, out-of-autoclave curing process for thermoset based continuous CFRP’s using heated tooling enables production of large composite structures. However, these manufacturing processes can have a significant impact on the structural performance of parts by inducing undesirable effects. These effects include inhomogeneous fiber orientations, thickness variations, and residual stresses …
Femtosecond Laser Shock Peening And The Dynamics Of Femtosecond Laser-Induced Shock Waves, Yuxin Li
Femtosecond Laser Shock Peening And The Dynamics Of Femtosecond Laser-Induced Shock Waves, Yuxin Li
All Dissertations
Laser shock peening (LSP) is an important material surface strengthening technique that uses laserinduced shock waves to cause severe plastic deformation near material surfaces. It can significantly improve material properties like hardness, strength, fatigue life, and corrosion resistance and is widely used in industrial sectors such as automotive, aerospace, nuclear, and medical areas. The conventional nanosecond laser shock peening (ns-LSP) has demonstrated effectiveness but faces challenges such as complex setup, low throughput, high energy consumption, and inflexibility for complex geometries. Femtosecond laser shock peening (fs-LSP) offers a promising alternative to overcome these issues, thanks to its ultra-high laser intensity and …
Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon
Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon
All Dissertations
The development of composite materials for structural components necessitates methods for evaluating and characterizing their damage states after encountering loading conditions. Laminates fabricated from carbon fiber reinforced polymers (CFRPs) are lightweight alternatives to metallic plates; thus, their usage has increased in performance industries such as aerospace and automotive. Additive manufacturing (AM) has experienced a similar growth as composite material inclusion because of its advantages over traditional manufacturing methods. Fabrication with composite laminates and additive manufacturing, specifically fused filament fabrication (fused deposition modeling), requires material to be placed layer-by-layer. If adjacent plies/layers lose adhesion during fabrication or operational usage, the strength …
Characterization Of Friction Element Welding Using Finite Element Modeling, Ankit Varma
Characterization Of Friction Element Welding Using Finite Element Modeling, Ankit Varma
All Dissertations
Friction element welding (FEW) has been advocated as a solution to weld different materials together, with the ability to join high-strength materials for a range of thicknesses with low input energy and a short processing time. This work develops a coupled thermal-mechanical finite element model to better understand the physical mechanisms involved in the process and to predict temperature and material flow during the process. Furthermore, microstructural analysis is performed for the steel layer using a scanning electron microscope and Vickers microhardness tester to understand the variation in its grain structure and hardness. Results from the finite element model and …
Numerical And Experimental Study Of Machining Process Of High Strength Lightweight Materials, Chao Zhang
Numerical And Experimental Study Of Machining Process Of High Strength Lightweight Materials, Chao Zhang
All Dissertations
No abstract provided.