Master of Science (MS), Ohio University, 2020, Industrial and Systems Engineering (Engineering and Technology)

In this research, a designer-driven approach was utilized to construct uniform structure and density infill designs suitable for most AM technologies. Infills were designed using a selected geometry—square, hexagonal, diamond, or triangular— and a specified air gap—1mm, 3mm, 5mm, 7mm, and 9mm. By simulating a flexural test using finite element analysis (FEA), the different infill designs were assessed. The placement of infill and its effect on part performance is also discussed. These infill designs were then selectively used in regions of the flexural test specimen where there is minimal stress. The selective infill designs were subjected to the same FEA as the uniform infill designs for a comparative analysis and to assess the effect of selectively adding infill to minimize material without sacrificing performance. Test samples used in the selective infill FEA were then printed on the Stratasys Objet30 and tested for validation. Additionally, infill designs were improved by adding material to the part thickness. Material was added to the part thickness and studied to analyze the effects of this technique, and to discover more efficient methods of utilizing material in additive manufacturing. The results of this methodology proved to be an effective way to analyze the effects of infill designs commonly in additive manufacturing.

Committee: Dale Masel PhD (Advisor) Subjects: Design; Engineering

MS, University of Cincinnati, 2022, Engineering and Applied Science: Mechanical Engineering

With so many different manufacturing processes around, the industry has started combining different processes to combine their respective advantages and counter the disadvantages. Hybrid manufacturing processes, a combination of additive and subtractive methods, are being explored to increase the overall efficiency and part quality. There is a need for the selection of the optimal set of processes based on the part geometry and part material. The part features, in turn, directly affect the selection of the optimal sequence of processes. This thesis explores the idea of evaluating the STL models of a part based on Design for Manufacturing (DFM) and Design for Additive Manufacturing (DfAM) rules to select the optimal combination of subtractive and additive processes for manufacturing the part. The metrics are extracted directly from the features of the STL model by performing a slice-by-slice analysis of the part to determine the combinations of the geometric demarcation point between various processes. For additive processes, the list of metrics extracted includes the volume of material to be added, staircase error, sharp corner, and support structure volume. The metrics considered for subtractive processes are the volume of material removal, tool inaccessibility, part geometry complexity, and sharp internal corners that may be difficult to machine. For each part geometry and build orientation, the overall final score is calculated for subtractive and additive process metrics, and a decision is made on the optimal combination and demarcation points for using the various processes for manufacturing the part. Several case studies of varying complexity have been presented for calculating the metrics and determining the optimal process plans.

Committee: Sam Anand Ph.D. (Committee Member); Xinyi Xiao Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science (MS), Ohio University, 1998, Mechanical Engineering (Engineering)

Virtual manufacturing on the web: Extrusion die design.

Committee: Bhavin Mehta (Advisor) Subjects: Engineering, Mechanical

Master of Science, The Ohio State University, 2022, Materials Science and Engineering

Recent increases in computational power have led to growing enthusiasm about the volume of data that can be collected and analyzed for many applications. However, the amount of data some physical/virtual systems generate is so great that an increased reliance on mathematical, statistical, and algorithmic based approaches to analyze and make decisions from the data is required. Application of these computational tools can lead to sharper decision making and vast amounts of knowledge discovered. The abstraction of the scientific decision-making process has led many researchers to consider observing systems with more tunable experimental parameters. This makes traditional experimentation, which is based on human researchers conducting the experiment and using their intuition to drive the next set of experiments, intractable for these applications. Autonomous experimentation (AE) systems, which are also a byproduct of the computational explosion, are able to address this issue and have found use across the fields of biology, chemistry, and materials science. AE systems are typically capable of conducting certain types of experiments with lower and more reliable turnaround times as opposed to their human counterparts. The automated execution of experiments naturally leads one to think about how those experiments can be parallelized and otherwise completed faster due to the lack of human presence in the experimentation environment. Therefore, AE systems are considered when designing many high-throughput experimentation (HTE) efforts. This thesis presents an overview of the current state-of-the-art for AE systems in Chapter 1, a framework developed to increase the independence of AE systems from human assistance in Chapter 2, and a machine-learning (ML) data processing pipeline that automates the image post-processing phase of the analysis of backscattered-electron scanning electron microscope images in Chapter 3.

Committee: Stephen Niezgoda (Advisor); Joerg Jinschek (Advisor); Sriram Vijayan (Other); Gopal Viswanathan (Committee Member); Oksana Chkrebtii (Committee Member) Subjects: Business Administration; Computer Science; Engineering; Experiments; Industrial Engineering; Information Science; Information Systems; Information Technology; Metallurgy; Operations Research; Robotics; Statistics

Doctor of Philosophy (PhD), Ohio University, 2019, Industrial and Systems Engineering (Engineering and Technology)

As a strategic decision in the supply chain design, the manufacturing system design impacts the quality, flexibility and the profitability of the entire supply chain. The global supply chain network confronts challenges such as the uncertain market demand and the global trade tariffs. The main goal of this dissertation is to design global supply chain under the stochastic demand considering manufacturing operations and the impact of tariffs. The methodology consists of eight steps. First, the local manufacturer, located in USA, groups the similar products into families to save time, effort and cost. Second, as a clustering approach, the p-median model is studied and then modified to identify families considering the minimum average family similarity. Third, the manufacturer decides the best design for the manufacturing operations, in this step, the classical-cellular manufacturing system is designed under the stochastic market demand. Fourth, the expected revenues generated by the cells open for product families, considering the expected sales and selling prices, are determined. Besides, the expected manufacturing costs including the labor, machine, material and shortage costs are determined as well. Eventually, the expected profits are calculated and the optimal number of cells is identified based on the highest profits generated. Although, the optimal design of the manufacturing system generates higher profits, the demand may not be fully covered.Fifth, based on the optimal design obtained in the third step, the optimal expected profits of the product, based on the scenarios of restrictions on their demand coverage probabilities, are determined by a proposed mathematical model. In this step, if there is no restriction, the maximum profits are made when only one product is produced and sold. This is due to that it has the lowest processing time among all products. When restrictions are applied on the demand coverage, other products are produced and sold; however, l (open full item for complete abstract)

Committee: Gürsel Süer (Advisor); Khurrum Bhutta (Committee Member); Gary Weckman (Committee Member); Tao Yuan (Committee Member); Ashley Metcalf (Committee Member) Subjects: Business Administration; Industrial Engineering; Management; Systems Science

MDES, University of Cincinnati, 2011, Design, Architecture, Art and Planning: Design

My professional experience is a combination of theoretical and practical education with employment in both China and the U.S. This duality gives me the proper perspective to both compare and contrast the problems and opportunities in the industrial design profession in the U.S. and China. Chinese industrial design is experiencing rapid development on the grounds that China is becoming the world's manufacturing center. Each year, an increasing number of international collaborations occur between the U.S. and China, requiring accurate communication and mutual understanding. However, due to cultural differences, uneven industrial design development stages, and dissimilar education systems, Chinese industrial designers, in general, work differently compared to U.S. industrial designers. This difference very often creates difficulties in international partnerships which results in wastes of time, cost, and energy for both sides. Based on my experience and research there appears to be significant differences in the goals that drive US and Chinese designers in the product development process. Most US industrial designer respond to emerging trends in markets, refine branding, clarify distinction, and create intellectual property, whereas most Chinese industrial designers' goals consist of observing the success of existing products in the market place. Thus, in the U.S., designers mostly focus on consumer research and have more of an influence on product strategy, opposed to Chinese designers who focus more on manufacturing and have greater influence on design execution. This thesis is to describe this difference and explain how China's industrial design education and practice must be changed. In addition, China must learn from the Japanese by integrating their own cultural value into industrial designing, so that China can occupy a unique position in the design world to compete with other cultures. Furthermore, design promotional organizations such as the IDSA (Industrial (open full item for complete abstract)

Committee: Craig Vogel MD (Committee Chair); Peter Chamberlain MFAMPhil (Committee Member) Subjects: Design

MS, University of Cincinnati, 2003, Engineering : Industrial Engineering

Sheet metal part design relies heavily on manufacturing experience, which is not very easily available to the designer. The manufacturing experience has to be documented and incorporated in the design process. This would eliminate frequent redesign of parts, after being assessed as infeasible or costly for manufacture in the design stage. Such a DFM analysis would reduce product time to market and reduce overall product costs. The present work aims at compiling a comprehensive set of design rules for sheet metal part design and a methodology for implementing inter-feature design rule checking for reducing infeasible designs, costs and production cycle times. The inter-feature module is part of a Design Advisory and Feature Extraction system that aims at checking the CAD model for various design rules. These rules are incorporated here and implemented for SolidWorks 2000, which has a separate sheet metal modeling module. The implementation has been done in Visual Basic using the OLE Interface provided by SolidWorks API.

Committee: Dr. Sam Anand (Advisor) Subjects: Engineering, Industrial

Master of Science in Engineering, Youngstown State University, 2024, Department of Electrical and Computer Engineering

As part of this project, a complex terahertz (THz) antenna was fabricated using two-photon polymerization (2PP), a highly precise additive manufacturing method. The design and rigorous simulation testing were conducted using Ansys HFSS, with a focus on achieving minimal losses. Special emphasis was placed on impedance matching, confirmed by the S11 parameter showing minimal power reflection over a large part of the THz band. The antenna was fabricated using OrmoComp, a hybrid polymer. A significant portion of the thesis is dedicated to fine-tuning the intricate fabrication steps necessary for producing complex designs, demonstrating the capability to also fabricate simpler structures. The most significant outcomes of this work on the highly directional THz antenna are the optimized process parameters such as slicing direction, way of printing, power and speed settings of laser for 2PP and finally development time of post processing, which enabled the production of the complex structure. The fidelity of the final fabricated design was verified using electron and light microscopy.

Committee: Vamsi Borra PhD (Advisor); Frank X. Li PhD (Committee Member); Srikanth Itapu PhD (Committee Member); Pedro Cortes PhD (Committee Member) Subjects: Design; Electrical Engineering; Electromagnetics; Nanotechnology

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Materials Science

This research focuses on studying and analyzing the properties of high entropy alloys (HEAs) built by additive manufacturing (AM) methods. This work also proposes a novel approach of designing HEAs by integrating two established computational methods into a single integrated design approach. In addition to that, this work (Chapter 3) presents a study aimed to bridge the knowledge gap about the variations in material properties of HEA parts produced with two major metal AM methods, namely, selective laser melting (SLM) and directed energy deposition (DED). It was found that the crystallographic texture, homogeneity of elements distribution, and microhardness parameters were on the same order for both SLM and DED samples. However, the DED samples contained multiple spherical manganese oxides, whereas the SLM samples exhibited only lack of fusion (LOF) defects. Chapter 4 explores the feasibility of in-situ alloying of HEAs using a cost-effective cobalt-rich metal powder as the base material. This powder, typically used for thermal spray applications, was enhanced by adding relatively inexpensive elemental powders to synthesize the AlCoCrFeNi HEA. This method provides the flexibility to create HEAs by in-situ alloying, eliminating the need for expensive elemental powder blends or gas-atomized pre-alloyed powders. Chapter 5 investigates the effect of heat treatment on the microstructure and properties of CoCrFeMnNi HEA produced by SLM. It was discovered that the ductility and energy absorption increased significantly after heat treatment, while tensile strength only changed slightly. In the same chapter, the doping of CoCrFeMnNi HEA by rare-earth elements through DED was also analyzed, where dendritic microstructure with columnar grains and a cubic texture were observed in the 1 wt% CeO2-doped HEA. Finally, Chapter 6 proposes an approach for rapid and economical search for novel and stable HEA systems by integrating a semi-empirical method with an ab-initi (open full item for complete abstract)

Committee: Jing Shi Ph.D. (Committee Chair); Donglu Shi Ph.D. (Committee Member); Matthew Steiner Ph.D. (Committee Member); Ashley Paz y Puente Ph.D. (Committee Member) Subjects: Materials Science

PhD, University of Cincinnati, 2023, Engineering and Applied Science: Mechanical Engineering

In recent years, the demand for high-functioning industrial parts and components has increased significantly. In addition to the enhanced functional performance, the parts are also expected to be manufacturable at minimum cost and lead times. Additive manufacturing processes have the capability to meet these demands. The inherent nature of additive manufacturing enables the users to fabricate complicated parts with reduced cost and material requirements. Designers can apply scenario-specific topology optimization approaches, infill lattices, and multi-material structures to ensure the eventual design performs well under the applied loading. However, most of these design methods fail to consider the manufacturability of that particular part. Typical additive manufacturing post-processing operations, such as support structure removal and entrapped powder elimination, are not addressed during a part's design phase. This dissertation aims to bridge the gap between a high-performance part design and its additive manufacturability to achieve a cost and time efficient product design and manufacturing pipeline. To simultaneously optimize the part geometry while ensuring improved manufacturability, a novel support structure accessibility filter is embedded inside a compliance minimization topology optimization model. Computational geometry and graph-based algorithms are developed to detect and characterize part geometries for powder entrapment challenges. Also, a graph-search approach for optimum rotation sequence for powder removal is developed as a preliminary tool for designers to consider depowdering challenges during the design phase. Typical requirements for additively manufactured parts consist of high functional performance with appropriate weight reduction. Multi-material topology optimization methods combined with variable-density lattice structures are used to obtain lightweight parts that perform well under thermal and mechanical loading. Due to the prohibitive c (open full item for complete abstract)

Committee: Sam Anand Ph.D. (Committee Chair); Michael Alexander-Ramos Ph.D. (Committee Member); Gen Satoh Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Prashant Khare Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2024, Materials Science and Engineering

The transition from a make and dispose linear materials economy to a sustainable circular economy is being pushed due to limited resources. This involves developing high-performance alloys, advanced manufacturing processes, and recyclable materials. This presents challenges and opportunities for materials scientists. The role of minor alloying elements in alloy design strategy is crucial, as recycled alloys can contain unexpected and potentially deleterious alloying elements. The advent of metal 3D printing has led to new non-equilibrium hierarchical microstructural features, requiring further exploration on its impact on material properties. Corrosion resistance is a key driver in enhancing longevity of structures that enhances the sustainability of infrastructure. This dissertation addresses two key challenges that have gained importance in the context of sustainability, namely: (i) understanding the role of minor alloying elements, especially copper whose effect on localized corrosion of Fe and Ni-based corrosion-resistant alloys (CRAs) in chloride environments is elusive, (ii) understanding the impact of metal 3D printing processes on localized corrosion in common Fe and Ni-based CRAs (316L stainless steel, IN 625). In Chapter 2, the literature is briefly reviewed to point out gaps that are addressed in the rest of the dissertation. In Chapter 3, we show the beneficial effect of copper on pitting corrosion in a face-centered cubic Ni-13Cr-10Fe alloy using electrochemical corrosion experiments and first-principles modelling of electro-chemo adsorption of ionic species in a pit. Copper alloying inhibits chloride adsorption and promotes proton adsorption on corroding pit surfaces, reducing pit stabilization. In Chapter 4, heat treatments were designed to alter the distribution of copper in model Ni-alloys by inducing grain boundary segregation through thermal aging. The microstructure was characterized using scanning transmission electron microscopy (STEM). (open full item for complete abstract)

Committee: Gerald Frankel (Advisor); Eric Schindelholz (Committee Member); Yunzhi Wang (Committee Member); Narasi Sridhar (Committee Member); Christopher Taylor (Committee Member) Subjects: Materials Science

Doctor of Philosophy, University of Toledo, 2023, Mechanical Engineering

Shape Memory Effect (SME) and Superelasticity (SE) are key characteristics of shape memory alloy (SMA) materials. SME allows the material to return to its original shape after heating, while superelasticity enables recovery from significant inelastic deformation. NiTiHf is a highly promising SMA, known for its elevated SME and SE performances. Designing and controlling NiTiHf SMA properties as desired poses challenges due to its dependence on many factors. Three core characteristics define SMA materials: transformation temperatures (TTs), thermal hysteresis (TH), and actuation strain (AS). TTs are crucial design properties that determine the activation threshold for SME and SE effects. TH, resulting from TTs differences, reflects the energy loss during each SME action. AS represents the amount of recoverable strain during each SME actuation. Traditional approaches to designing NiTiHf TTs, TH, and AS have relied solely on experimental studies, which have not yielded comprehensive results and can be impractical due to high costs and time requirements. Cost-effective modeling approaches, including physics-based and data-driven methods, expedite material design and process optimization. Machine learning (ML) modeling, equipped with strong regression analyses, significantly reduces the need for experimental trials to optimize alloy design. Physics-based modeling, considering underlying physical principles, plays a critical role as error compensation tools. In this study, both data-driven and physics-based modeling were utilized to overcome the high-dimensional dependency of NiTiHf TTs and AS on various factors and the limited understanding of governing physics. The input parameters for the machine learning models included elemental composition, thermal treatments, and common post-processing steps used in NiTiHf fabrication. This feature selection incorporated a majority of accessible information from the literature on NiTiHf TTs and AS, making use of all essential proce (open full item for complete abstract)

Committee: Mohammad Elahinia (Committee Chair); Ala Qattawi (Committee Chair); Othmane Benafan (Committee Member); Behrang Poorganji (Committee Member); Meysam Haghshenas (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2023, Mechanical Engineering

Traditional design tasks and manufacturing systems often require a multitude of manual efforts to fabricate sophisticated artifacts with desired performance characteristics. Engineers typically iterate on a first principles-based model to make design decisions and then iterate once more by manufacturing the artifacts to take manufacturability into design consideration. Such manual decision-making is inefficient because it is prone to errors, labor intensive and often fails to discover process-structure-property relationships for novel materials. As robotics is an integral part of modern manufacturing, building autonomous robots with decision-making capabilities is of crucial importance for this application domain. Unfortunately, most of the robots and manufacturing systems in the industries lack such cognitive abilities. We argue that this whole process can be made more efficient by utilizing machine learning (ML) approaches, more specifically by leveraging sequential decision-making, and thus making these robots, design processes and manufacturing systems autonomous. Such data-driven decision-making has multiple benefits over traditional approaches; 1) machine learning approaches may discover interesting correlations in the data or process-structure-property relationship, 2) ML algorithms are scalable, can work with high dimensional unstructured problems and learn in highly nonlinear systems where a model is not available or feasible, 3) thousands of man-hour and extensive manual labor can be saved by building autonomous data-driven methods. Due to the sequential nature of the problem, we consider reinforcement learning (RL), a type of machine learning algorithm that can take sequential decisions under uncertainty by interacting with the environment and observing the feedback, to build autonomous manufacturing systems (AMS). Unfortunately, traditional RL is not suitable for such hardware implementation because (a) data collection for AMS is expensive and (b) tradit (open full item for complete abstract)

Committee: David Hoelzle (Advisor); Parinaz Naghizadeh (Committee Member); Jieliang Luo (Committee Member); Kira Barton (Committee Member); Michael Groeber (Committee Member) Subjects: Artificial Intelligence; Design; Mechanical Engineering; Robotics

Master of Science (MS), Wright State University, 2021, Human Factors and Industrial/Organizational Psychology MS

Four interfaces were developed to factorially apply two principles of ecological interface design (EID; direct perception and direct manipulation) to a flexible manufacturing system (FMS). The theoretical foundation and concepts employed during their development, with findings related to more significant issues regarding interface design for complex socio-technical systems, are discussed. Key aspects of cognitive systems engineering (CSE) and EID are also discussed. An FMS synthetic task environment was developed, and an experiment was conducted to evaluate real-time decision support during supervisory operations. Participants used all four interfaces to supervise and maintain daily part production at systematically varied levels of difficulty across sessions. Significant results provide evidence that the incorporation of direct perception and direct manipulation in interface design produced an additive effect, allowing for greater support for the supervisory agents.

Committee: Kevin B. Bennett Ph.D. (Advisor); Scott Watamaniuk Ph.D. (Committee Member); John Flach Ph.D. (Committee Member) Subjects: Experimental Psychology; Psychology; Systems Design

Master of Science (M.S.), University of Dayton, 2021, Aerospace Engineering

With more efficient computational capabilities, the use of topology optimization (TO) is becoming more common for many different types of structural design problems. Rapid prototyping and testing is often used to further validate optimized designs, but depending on a design's complexity, the structural behavior of physical models can vary significantly compared to that of their computational counterparts. For graph-based topologies such differences are caused, in part, by a need to realize finite-thickness structures from the infinitely thin geometries described by graph theory. Other differences are caused by limitations on manufacturing processes such as the need to fabricate large models from smaller components. While additive manufacturing (AM) can be more conducive for fabrication of complex topologies, its limitations are generally less understood than those for traditional subtractive manufacturing processes. Understanding and incorporating limitations on AM into a TO process in the form of added constraints would allow the algorithm to produce not only optimal designs, but also those that are feasible for AM. In this work, two specific AM constraints are characterized for Lindenmayer system (L-system) graph-based topologies of a multi-material, diamond-shaped, morphing airfoil in supersonic flow. One constraint is related to the feasible generation of thick structural members from the infinitely thin beams of graph-based topologies. To characterize the effects of geometric overlap, structural behavior of finite element models made of lower-fidelity beam elements is compared to that of finite element models made of higher-fidelity volume elements. Results indicate that at intersections where 10% or more of a member's length is overlapped, there will be significant variations in stress and effective torsional stiffness when thin members are converted to thick members. The second AM constraint characterized in this work is related to partitioning of large mo (open full item for complete abstract)

Committee: Markus Rumpfkeil (Committee Chair); Richard Beblo (Committee Member); Alexander Pankonien (Committee Member); Raymond Kolonay (Committee Member) Subjects: Aerospace Engineering; Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Mechanical Engineering

Ultrasonic additive manufacturing (UAM) is a solid-state manufacturing technology that produces near-net shape metallic parts. UAM has been demonstrated to make robust structures with a variety of material combinations such as Al-Al, Al-Ti, Cu-Cu, and Al-Cu. However, UAM welding of high strength steels has proven challenging. The focus of this work is to develop a fundamental understanding of the structure-property-process relationship of UAM steel welding through experiments and modeling. Process and post-processing methods to improve UAM steel weld quality were investigated. A custom shear test was first developed and optimized to test the mechanical strength of UAM builds. The second study demonstrated the UAM fabrication of stainless steel 410 builds which possess, after post-processing, mechanical properties comparable with bulk 410 material. Fracture surface analyses confirm the weld quality improvement caused by increasing the baseplate temperature and the application of hot isostatic pressing (HIP) post weld. In the third study, a higher weld power is demonstrated by using a cobalt-based sonotrode coating, achieving shear strengths comparable to bulk 4130 material without post treatment. Weld parameters for making UAM 4130 builds were optimized via a design of experiments study. Baseplate temperature of 400 ˚F (204.4 ˚C), amplitude of 31.5 µm, welding speed of 40 in/min (16.93 mm/s), and normal force of 6000 N were identified as optimal within the selected process window. Analysis of variance and main effect plots show that normal force, amplitude, and welding speed are significant for interfacial temperature. Similar analyses show that normal force and amplitude have a statistically significant effect on shear strength. Residual stress in UAM 4130 samples was measured for the first time using neutron diffraction. The maximum tensile residual stress for UAM 4130 is found to be relatively low at 176.5 MPa, which suggests a potentially better fatig (open full item for complete abstract)

Committee: Marcelo Dapino (Advisor); David Hoelzle (Committee Member); Farhang Pourboghrat (Committee Member) Subjects: Materials Science; Mechanical Engineering

Doctor of Philosophy (PhD), Ohio University, 2020, Mechanical and Systems Engineering (Engineering and Technology)

In the world of manufacturing, different strategies could be followed to handle the rapidly changing consumer needs and desires in order to remain competitive, and enable their manufacturing systems to respond quickly to new demand and handle the fluctuation in demand. Since the cellular manufacturing system is an important part of the manufacturing system, a new design method, multi-stage cellular manufacturing system design, is proposed in this dissertation. Three performance measures, total number of machines, total machine cost, and %actual risk level, are utilized to evaluate the performance of the proposed design. Considering the uncertainty in the product demand and processing times, two types of the multi-stage cellular manufacturing system are studied. The first type is a deterministic multi-stage cellular manufacturing system. This type of system is propounded to improve the flexibility of the system where the possibility of adding new machines, mini-cells, and stages is existent. Based on the similarity coefficient type used to group the operations into a stage, two design methods are introduced. The first design method is the multi-stage cellular manufacturing system based on the similarity among machines. A new mathematical model is developed to group the machines into stages by maximizing the similarity coefficient among machines. The second design method is the multi-stage cellular manufacturing system based on the similarity among products. A novel heuristic algorithm and mathematical model are proposed to assign machines to stages based on the newly similarity coefficient “cumulative similarity coefficient among products”. In the two design methods, two mini-cell types, regular and flexible flowshop mini-cells, are used in a stage considering the type of products and the possibility to duplicate the machine type. Additionally, the number of stages and product families is un-predetermined and predetermined to minimize the total number of machines. Th (open full item for complete abstract)

Committee: Gürsel A. Süer Dr. (Advisor); Tao Yuan Dr. (Committee Member); Dusan Sormaz Dr. (Committee Member); M. Khurrum S. Bhutta Dr. (Committee Member); Ana L. Rosado Feger Dr. (Committee Member) Subjects: Design; Engineering; Industrial Engineering

Master of Science in Engineering, Youngstown State University, 2020, Department of Mechanical, Industrial and Manufacturing Engineering

Laser Powder Bed Fusion (LBPF) allows for unparalleled freedom in design to manufacture complicated structures in high performance materials. Due to the advancement of additive manufacturing technologies, 3D printed parts have moved from the R&D phase to the development phase, with the aerospace industry having adapted this technology to cater small batch replacement parts mainly for an aging fleet of aircraft. The goal of this research was to systematically generate defects, mainly lack-of-fusion defects, to understand the mechanical and corrosion behavior of these defects in parts that are susceptible to flight criticality and safety criticality. This work investigated the influence of the main Selective Laser Melting process parameters (laser power, travel velocity, hatch spacing, and layer thickness) on the defect characteristics using an AlSi10Mg alloy. Five studies were conducted to analyze, evaluate, and compare the defect nature, including linearity, in different orientations and builds. Seventy-seven sample coupons were manufactured from two parameter development builds and image analysis was performed using Image J and Photoshop. Optical microscopy and X-ray CT imaging were the methods used for defect detection. The results showed that for decreasing energy density, the defect density and defect size increase which results in the decrease of the % average relative density, for the set of process parameters investigated, with lack-of-fusion defects predominantly forming at energy densities below 35 J/mm3. Following defect characterization, the effects of each of the four major process parameters were interpreted using a DOE approach with the help of regression and ANOVA testing. Hatch spacing proved to be the most significant process parameter affecting the defect density, while the layer thickness showed the most significant effect when predicting the average defect dimensions and the ratio of defect length to height for the set of process parameters (open full item for complete abstract)

Committee: Holly Martin PhD (Advisor); Brett Conner PhD (Committee Member); Hojjat Mehri PhD (Committee Member) Subjects: Aerospace Materials; Materials Science; Metallurgy; Statistics

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The organic structure of the ant neck joint is a highly specialized compliant mechanism which can withstand high loads relative to its weight. Previous research has captured the structure of the joint in measurable, single body CAD models. This project builds on that work by parameterizing the full biological model into a simplified engineering model. The engineered CAD model contains three distinct bodies representing the thorax, head, and membranous connection. Design tables are utilized to automate model generation, additionally, the model retains the capability to introduce additional parameters in the future. This project also explores additive manufacturing as a fabrication method for generating larger scale physical models and outlines the feasibility for generating the ant neck joint as a compliant mechanism. This approach resulted in a successful proof-of-concept multi-material prototype. The modeling and manufacturing framework provides an environment for further engineering analysis of an artificial ant neck joint. The parameters, models, and methods established in this research can be used as a first step to move beyond biomimicry and into purposeful engineering design.

Committee: Sandra Metzler Dr. (Advisor); Haijun Su Dr. (Committee Member); Blaine Lilly Dr. (Committee Member) Subjects: Mechanical Engineering

Master of Science (MS), Ohio University, 2020, Industrial and Systems Engineering (Engineering and Technology)

Additive manufacturing (AM) is well known for its freedom of design, but components that are printed or solidified in layers are weak compared to solid castings. This research develops a method of rapid tooling that uses a cost-effective AM process to create dissolvable mold boxes that, when cast out of a filled thermoset material, produce durable tools for injection molding. The method, otherwise referred to as the rapid dissolvable mold box (RDMB) method, describes the steps required to start with a moldable part design and finish with a tool capable of being used in both hobbyist level and industrial-grade injection molding machines. Testing and statistical analyses prove the addition of CNTs has a significant effect on the impact, flexural, and compressive properties of a cast epoxy tool as well as the time required to cool in between molding cycles. These findings, and others, suggest that CNTs are an excellent additive for polymer tooling materials. The method successfully reduced rapid tooling material costs by 75 and 84 percent when it was applied and tested. It is also hypothesized to be an effective solution for creating tools for extrusion, blow molding and end-of-arm tool applications.

Committee: Dale Masel PhD (Advisor); Tao Yuan PhD (Committee Member); Gary Weckman PhD (Committee Member); Chulho Jung (Committee Member) Subjects: Design; Engineering; Mechanical Engineering; Polymer Chemistry; Polymers