Dissertations / Theses on the topic '091099 Manufacturing Engineering not elsewhere classified'

Manufacturing today is increasingly competitive and every organization around the world is looking to decrease costs. Maintenance costs generated an average of 28 percent of total manufacturing cost at the Fiat Chrysler Indiana Transmission Plant One in 2018, states Rex White, Head Maintenance Planner at Fiat Chrysler (2018). Maintenance is a supportive expense that does not generate a profit, which makes maintenance an attractive expense to decrease. The cost for components and skilled labor are expensive; however, the downtime is exponentially a larger threat to production cost. One most feared scenarios within a manufacturing facility is that one machine takes down several as it backs up the entire production process.

The three major types of maintenance are reactive, preventive, and predictive. The research project focused on applying the principles of predictive maintenance to the Fiat Chrysler facilities in Indiana. The report explains the techniques and principles of applying the technology currently available to reduce downtime and maintenance cost. The predictive maintenance procedures and saving are compared with reactive and preventive methods to determine a value of return. The report will examine the benefits of using the Internet of Things technology to create autonomous self-diagnosing smart machines. The predictive maintenance plan in this research illustration will introduce health check equipment used to implement longer lasting machine components. In conclusion, the project developed out an entire predictive maintenance plan to reduce downtime and maintenance costs.

Robust process monitoring in real-time is a challenge for Continuous Pharmaceutical Manufacturing. Sensors and models have been developed to help to make process monitoring more robust, but they still need to be integrated in real-time to produce reliable estimates of the true state of the process. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this work, we present such a systematic framework, which for a given sensor network and measurement uncertainties will predict the most likely state of the process. As a result, real-time process decisions, whether for process control, exceptional events management or process optimization can be based on the most reliable estimate of the process state.

Data reconciliation (DR) and gross error detection (GED) have been developed to accomplish robust process monitoring. DR and GED mitigate the effects of random measurement errors and non-random sensor malfunctions. This methodology has been used for decades in other industries (i.e., Oil and Gas), but it has yet to be applied to the Pharmaceutical Industry. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. However, it requires the sensor network to be redundant (i.e., the number of measurements has to be greater than the degrees of freedom).

In this dissertation, the SSDR framework is defined and implemented it in two different continuous tableting lines: direct compression and dry granulation. The results for two pilot plant scales via continuous direct compression tableting line are reported in this work. The two pilot plants had different equipment and sensor configurations. The results for the dry granulation continuous tableting line studies were also reported on a pilot-plant scale in an end-to-end operation. New measurements for the dry granulation continuous tableting line are also proposed in this work.

A comparison is made for the model-based DR approach (SSDR-M) and the purely data-driven approach (SSDR-D) based on the use of principal component constructions. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. The reconciled measurement values generate using SSDR-M satisfy the model equations and can be used together with the model to estimate unmeasured variables. However, in the presence of nonlinearities, the SSDR-M and SSDR-D will differ. SSDR successfully estimates the real state of the process in the presence of gross errors, as long as steady-state is maintained and the redundancy requirement is met. Gross errors are also detected whether using SSDR-M or SSDR-D.

Additive Manufacturing (AM) allows for the creation of elements with novel forms and functions. Utilizing AM in development of components of civil infrastructure allows for achieving more advanced, innovative, and unique performance characteristics. The research presented in this dissertation is focused on development of a better understanding of the fabrication challenges and opportunities in AM of cement-based materials. Specifically, challenges related to printability and opportunities offered by 3D-printing technology, including ability to fabricate intricate structures and generate unique and enhanced mechanical responses have been explored. Three aspects related to 3D-printing of cement-based materials were investigated. These aspects include: fresh stability of 3D-printed elements in relation to materials rheological properties, microstructural characteristics of the interfaces induced during the 3D-printing process, and the mechanical response of 3D-printed elements with bio-inspired design of the materials’ architecture. This research aims to contribute to development of new pathways to obtain stability in freshly 3D-printed elements by determining the rheological properties of material that control the ability to fabricate elements in a layer-by-layer manner, followed by the understanding of the microstructural features of the 3D-printed hardened cement paste elements including the interfaces and the pore network. This research also introduces a new approach to enhance the mechanical response of the 3D-printed elements by controlling the spatial arrangement of individual filaments (i.e., materials’ architecture) and by harnessing the weak interfaces that are induced by the 3D-printing process.

One way to reduce the lifecycle cost and environmental impact of a product in a circular economy is to extend its lifespan by either creating longer-lasting products or managing the product properly during its use stage. Life extension of a product is envisioned to help better utilize raw materials efficiently and slow the rate of resource depletion. In the case of manufacturing equipment (e.g., an electric motor on a machine tool), securing reliable service life as well as the life extension are important for consistent production and operational excellence in a factory. However, manufacturing equipment is often utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost.

Recently, the emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to foster sustainable practices in manufacturing by maximizing the useful lives of components. In addition, advances in sensor technology (e.g., lower fabrication cost) enable greater use of sensors in a factory, which in turn is producing greater and more diverse sets of data. Widespread use of wireless sensor networks (WSNs) and plug-and-play interfaces for the data collection on product/equipment states are allowing predictive maintenance on a much greater scale. Through advances in computing, big data analysis is faster/improved and has allowed maintenance to transition from run-to-failure to statistical inference-based or machine learning prediction methods.

Moreover, maintenance practice in a factory is evolving from equipment “health management” to equipment “wellness” by establishing an integrated and collaborative manufacturing system that responds in real-time to changing conditions in a factory. The equipment wellness is an active process of becoming aware of the health condition and of making choices that achieve the full potential of the equipment. In order to enable this, a large amount of machine condition data obtained from sensors needs to be analyzed to diagnose the current health condition and predict future behavior (e.g., remaining useful life). If a fault is detected during this diagnosis, a root cause of a fault must be identified to extend equipment life and prevent problem reoccurrence.

However, it is challenging to build a model capturing a relationship between multi-sensor signals and mechanical failures, considering the dynamic manufacturing environment and the complex mechanical system in equipment. Another key challenge is to obtain usable machine condition data to validate a method.

A goal of the proposed work is to develop a systematic tool for maintenance in manufacturing plants using emerging technologies (e.g., AI, Smart Sensor, and IoT). The proposed method will facilitate decision-making that supports equipment maintenance by rapidly detecting a worn component and estimating remaining useful life. In order to diagnose and prognose a health condition of equipment, several data-driven models that describe the relationships between proxy measures (i.e., sensor signals) and machine health conditions are developed and validated through the experiment for several different manufacturing-oriented cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and the prediction capability of the data-driven models, signal processing is conducted to preprocess the raw signals using domain knowledge. Through this process, useful features from the large dataset are extracted and selected, thus increasing computational efficiency in model training. To make a decision using the processed signals, a customized deep learning architecture for each case is designed to effectively and efficiently learn the relationship between the processed signals and the model’s outputs (e.g., health indicators). Ultimately, the method developed through this research helps to avoid catastrophic mechanical failures, products with unacceptable quality, defective products in the manufacturing process as well as to extend equipment service life.

To summarize, in this dissertation, the assessment of technical, environmental and economic performance of the AI-driven method for the wellness of mechanical systems is conducted. The proposed methods are applied to (1) quantify the level of tool wear in a machining process, (2) detect different faults from a power transmission mini-motor testbed (CNN), (3) detect a fault in a motor operated under various rotation speeds, and (4) to predict the time to failure of rotating machinery. Also, the effectiveness of maintenance in the use stage is examined from an environmental and economic perspective using a power efficiency loss as a metric for decision making between repair and replacement.

Ceramics have been extensively used in aerospace, automotive, medical, and energy industries due to their unique combination of mechanical, thermal, and chemical properties. The objective of this thesis is to develop an extrusion based ceramic 3D printing process to digitally produce a casting mold. To achieve the objective, an in-house designed ceramic 3D printer was developed by converting a filament based plastic 3D printer. For mold making applications, zircon was selected because it is an ultra-high temperature ceramic with high toughness and good refractory properties. Additionally, alumina, bioglass, and zirconia slurries were formulated and used as the feedstock material for the ceramic 3D printer.

The developed 3D printing system was used to demonstrate successful printing of special feature parts such as thin-walled high aspect ratio structures and biomimetically inspired complex structures. Also, proof of concept with regard to the application of 3D printing for producing zircon molds and casting of metal parts was also successfully demonstrated.

To characterize the printed parts, microhardness test, scanning electron microscopy (SEM), and X-ray diffraction (XRD) analyses were conducted. The zircon samples showed an increase in hardness value with an initial increase in heat treatment temperature followed by a drop due to the development of porosity in the microstructure, caused by the decomposition of the binder. The peak hardness value for zircon was observed to be 101±10 HV0.2. Similarly, the microhardness values of the other 3D printed ceramic specimens were observed to increase from 37±3 to 112±5 HV0.2 for alumina, 23±5 to 35±1 HV0.2 for bioglass, and 22±5 to 31±3 HV0.2 for zirconia, before and after the heat-treatment process, respectively.

Finally, a system model for the ceramic 3D printing system was developed through the application of the model-based systems engineering (MBSE) approach using the MagicGrid framework. Through the system engineering effort, a logical level solution architecture was modeled, which captured the different system requirements, the system behaviors, and the system functionalities. Also, a traceability matrix for the system from a very abstract logical level to the definition of physical requirements for the subsystems was demonstrated.

Tantalum, niobium, stainless steels, and nickel are corrosion-resistant metals that have become critical in many industrial sectors. Due to the demanding environments and temperatures in which they operate, few materials can serve as substitutes. The advantages of these materials are offset by the difficulties in their machining. Belonging to a group of metals and alloys often referred to as ‘gummy’, their poor machinability or gumminess is manifest as thick chip formation, large cutting forces, and poor finish on cut surface. Hence, machining costs can be prohibitive, and applications limited. The gumminess has been attributed broadly to their high strain-hardening capacity.

To examine why these metals are difficult to machine, we used direct in situ observations of the cutting process with a high-speed imaging system, complemented by force measurements. The observations showed that chip formation occurred by repeated large-amplitude folding of the material – sinuous flow – with vortex-like components and extensive redundant deformation. The folding was particularly severe in Ta and Nb. Although Ta and Nb displayed a higher rate of fold nucleation than the Ni and stainless steel, the flow dynamics underlying chip formation across the metals was the same – sinuous flow nucleated by a plastic (buckling-type) flow instability on the workpiece surface just ahead of the advancing tool. The large strains and energy dissipation associated with sinuous flow is the reason for the poor machinability of these metals.

Prior work with Cu and Al has shown that sinuous flow can be disrupted and replaced by an energetically more favorable (segmented) flow mode, characterized by quasi-periodic fracture, when suitable chemical media are applied to the initial workpiece surface – a mechanochemical effect. The segmented flow is beneficial for machining processes since it involves much smaller forces and plastic strains. It has been hypothesized that the chemical media influence the flow through their adsorption onto the workpiece surface, thereby altering the surface energy and/or surface stress, and effecting a local embrittlement (ductile-to-brittle transition).

We demonstrate similar media (mechanochemical) effects and segmented flow development in cutting of the corrosion-resistant metals, with significant benefits for their machining. These benefits include > 35 percent reduction in the cutting force/energy, and an order of magnitude improvement in cut surface quality (finish, tears and residual strain). Importantly, the experiments with the corrosion-resistant metals provide strong evidence that it is indeed adsorption – not corrosion, as in case of hydrogen embrittlement – that underpins the mechanochemical effect. The experiments used chemical agents well-known for their strong adsorption to metal surfaces, namely green corrosion inhibitors (e.g., plant extracts, propolis) and other natural organic molecules (e.g., dyes, antibacterial drugs, cow’s milk). Lastly, the suitability and application of the mechanochemical effect at industrial cutting speeds is explored in turning experiments with these corrosion-resistant metals. Collectively, our observations, measurements, and analysis show that the gumminess of metals in cutting is due to sinuous flow; the gumminess can be eliminated by use of chemical media; and adsorption is the key to engendering the mechanochemical effect. Implications of the results for industrial processes ranging from machining to particle comminution, and for sustainable manufacturing are discussed.

Extrusion deposition additive manufacturing (EDAM) has enabled upscaling the dimensions of the objects that can be additively manufactured from the desktop scale to the size of a full vehicle. The EDAM process consists of depositing beads of molten material in a layer-by-layer manner, thereby giving rise to temperature gradients during part manufacturing. To investigate the phenomena involved in EDAM, the Composites Additive Manufacturing Research Instrument (CAMRI) was developed as part of this project. CAMRI provided unparalleled flexibility for conducting controlled experiments with carbon fiber reinforced semi-crystalline polymers and served as a validation platform for the work presented in this dissertation.

Since the EDAM process is highly non-isothermal, modeling heat transfer in EDAM is of paramount importance for predicting interlayer bonding and evolution of internal stresses during part manufacturing. Hence, local heat transfer mechanisms were characterized and implemented in a framework for EDAM process simulations. These include local convection conditions, heat losses in material compaction as well as heat of crystallization or melting. Numerical predictions of the temperature evolution during the printing process of a part were in great agreement with experimental measurements by only calibrating the radiation ambient temperature.

Metamaterials are artificially structured materials which attain their unconventional macroscopic properties from their cellular configuration rather than their constituent chemical composition. The judicious design of this cellular structure opens the possibility to program and control the optical, mechanical, acoustic, or thermal responses of metamaterials. This Ph.D. dissertation focuses on scalable design and manufacturing strategies for optical and mechanical metamaterials.

The fabrication of optical metamaterials still relies heavily on low-throughput process such as electron beam lithography, which is a serial technique. Thus, there is a growing need for the development of high-throughput, parallel processes to make the fabrication of optical metamaterials more accessible and cost-effective. The first part of this dissertation presents a scalable manufacturing method, termed “roll-to-roll laser induced superplasticity” (R2RLIS), for the production of flexible optical metamaterials, specifically metallic near-perfect absorbers. R2RLIS enables the rapid and inexpensive fabrication of ultra-smooth metallic nanostructures over large areas using conventional CO₂ engravers or inexpensive diode lasers. Using low-cost metal/epoxy nanomolds, the minimum feature size obtained by R2RLIS was <40 nm, facilitating the rapid fabrication of flexible near-perfect absorbers at visible frequencies with the capability to wrap around non-planar surfaces.

The existing approaches for designing mechanical metamaterials are mostly ad hoc, and rely heavily on intuition and trial-and-error. A rational and systematic approach to create functional and programmable mechanical metamaterials is therefore desirable to unlock the vast design space of mechanical properties. The second part of this dissertation introduces a systematic, algorithmic design strategy based on Voronoi tessellation to create architected soft machines (ASMs) and twisting mechanical metamaterials (TMMs) with programmable motion and properties. ASMs are a new class of soft machines that benefit from their 3D-architected structure to expand the range of mechanical properties and behaviors achievable by 3D printed soft robots. On tendon-based actuation, ASMs deform according to the topologically encoded buckling of their structure to produce a wide range of motions such as contraction, twisting, bending, and cyclic motion. TMMs are a new class of chiral mechanical metamaterials which exhibit compression-twist coupling, a property absent in isotropic materials. This property manifests macroscopically and is independent of the flexible material chosen to fabricate the TMM. The nature of this compression-twist coupling can be programmed by simply tuning two design parameters, giving access to distinct twisting regimes and tunable onset of auxetic (negative Poisson’s ratio) behavior. Taking a metamaterial approach toward the design of soft machines substantially increases their number of degrees of freedom in deformation, thus blurring the boundary between materials and machines.

Cellulose nanocrystals (CNCs) and its composite coatings may impart many benefits in packaging, electronic, optical, etc. applications; however, large-scale coating production is a major engineering challenge. To fill this knowledge gap, a potential large-scale manufacturing technique, roll-to-roll reverse gravure processing, has been described in this work for the manufacture of CNC and CNC-poly(vinyl alcohol) (PVA) coatings on a flexible polymer substrate. Various processing parameters which control the coating structure and properties were examined. The most important parameters in controlling liquid transfers were gravure roll, gravure speed, substrate speed, and ink viscosity. After successful fabrication, coating adhesion was investigated with a crosshatch adhesion test. The surface roughness and morphology of the coating samples were characterized by atomic force microscopy and optical profilometer. The Hermans order parameter (S) and coating transparency were measured by UV–Vis spectroscopy. The effect of viscosity on CNC alignment was explained by the variation of shear rate, which was controlled by the micro-gravure rotation. Finally, the CNC alignment effect was investigated for gas barrier and thermal management applications.

In packaging applications, cellulose nanomaterials may impart enhanced gas barrier performance due to their high crystallinity and polarity. In this work, low to superior gas barrier pristine nanocellulose films were produced using a shear-coating technique to obtain a range of anisotropic films. Induction of anisotropy in a nanocellulose film can control the overall free volume of the system which effectively controls the gas diffusion path and hence, controlled anisotropy results in tunable barrier properties. The highest anisotropy materials showed a maximum of 900-fold oxygen barrier improvement compared to the isotropic arrangement of nanocellulose film. The Bharadwaj model of nanocomposite permeability was modified for pure nanoparticles, and the CNC data were fitted with good agreement. Overall, the oxygen barrier performance of anisotropic nanocellulose films was 97 and 27 times better than traditional barrier materials such as biaxially oriented poly(ethylene terephthalate) (BoPET) and ethylene vinyl alcohol copolymer (EVOH), respectively, and thus could be utilized for oxygen-sensitive packaging applications.

Stringent regulatory requirements and modern diesel engine technologies have engaged automotive manufacturers and researchers in accurately predicting and controlling diesel engine-out emissions. As a result, engine control systems have become more complex and opaquer, increasing the development time and costs. To address this challenge, Model-based control methods are an effective way to deal with the criticality of the system study and controls. And physics-based combustion engine modeling is a key to achieve it. This thesis focuses on development and validation of a physics-based model for both engine and emissions using model-based design tools from MATLAB & Simulink. Engine model equipped with exhaust gas circulation and variable geometry turbine is adopted from the previously done work which was then integrated with the combustion and emission model that predicts the heat release rates and NO_x emission from engine. Combustion model is designed based on the mass fraction burnt from CA10 to CA90 and then NO_x predicted using the extended Zeldovich mechanism. The engine models are tuned for both steady state and dynamics test points to account for engine operating range from the performance data. Various engine and combustion parameters are estimated using parameter estimation toolbox from MATLAB and Simulink by applying least squared solver to minimize the error between measured and estimated variables. This model is validated against the virtual engine model developed in GT-power for Cummins 6.7L turbo diesel engine. To account the harmonization of the testing cycles to save engine development time globally, a world harmonized stationary cycle (WHSC) is used for the validation. Sub-systems are validated individually as well as in loop with a complete model for WHSC. Engine model validation showed promising accuracy of more than 88.4 percent in average for the desired parameters required for the NO_x prediction. NO_x estimation is accurate for the cycle except warm up and cool down phase. However, NO_x prediction during these phases is limited due to actual NO_x measured data for tuning the model for real time NO_x estimation. Results are summarized at the end to compare the trend of NO_x estimation from the developed combustion and emission model to show the accuracy of in-cylinder parameters and required for the NO_x estimation.

This study is concerned with the predictive modeling of the machining and the mechanical behaviors of additive manufactured (AMed) Ti6AlV/TiC composites and Ti6Al4V, respectively, using microstructure-based hierarchical multiscale modeling. The predicted results could constitute as a basis for optimizing the parameters of machining and AM of the current materials.

Through hierarchical flow of material behaviors from the atomistic, to the microscopic and the macroscopic scales, multiscale heterogeneous models (MHMs) coupled to the finite element method (FEM) are employed to simulate the conventional and the laser assisted machining (LAM) of Ti6AlV/TiC composites. In the atomistic level, molecular dynamics (MD) simulations are used to determine the traction-separation relationship for the cohesive zone model (CZM) describing the Ti6AlV/TiC interface. Bridging the microstructures across the scales in MHMs is achieved by representing the workpiece by macroscopic model with the microscopic heterogeneous structure including the Ti6Al4V matrix, the TiC particles, and their interfaces represented by the parameterized CZM. As a result, MHMs are capable of revealing the possible reasons of the peculiar high thrust forces behavior during conventional machining of Ti6Al4V/TiC composites, and how laser assisted machining can improve this behavior, which has not been conducted before.

Extending MHMs to predict the mechanical behaviors of AMed Ti6Al4V would require including the heterogeneous microstructure at the grain level, which could be computational expensive. To solve this issue, the extended mechanics of structure genome (XMSG) is introduced as a novel multiscale homogenization approach to predict the mechanical behavior of AMed Ti6Al4V in a computationally efficient manner. This is realized by embedding the effects of microstructure heterogeneity, porosity growth, and crack propagation in the multiscale calculations of the mechanical behavior of the AMed Ti6Al4V using FEM. In addition, the XMSG can predict the asymmetry in the Young’s modulus of the AMed Ti6Al4V under tensile and compression loading as well as the anisotropy in the mechanical behaviors. The applicability of XMSG to fatigue life prediction with valid results is conducted by including the energy dissipations associated with cyclic loading/unloading in the calculations of the cyclic response of the material.

The miniaturization and thinning of wearable, soft robotics and medical devices are soon to require higher performance modeling as the physical flexibility causes direct impacts on the electrical characteristics of the circuit – changing its behavior. As a representative flexible electronic component, the organic field effect transistor (OFET) has attracted much attention in its manufacturing as well as applications. However, as the strain and stress effects are integrated into multiphysics modelers with deeper interactions, the computational complexity and accuracy of OFET modeling is resurfacing as a limiting bottleneck.

The dissertation was organized into three interrelated studies. In the first study, the Mass-Spring-Damper (MSD) model for an inverted staggered thin film transistor (TFT) was proposed to investigate the TFT’s internal stress/strain fields, and the strain effects on the overall characteristics of the TFT. A comparison study with the finite element analysis (FEA) model shows that the MSD model can reduce memory usage and raises the computational convergence speed for rendering the same results as the FEA. The second study developed the generalized solid-state model by incorporating the density of trap states in the band structure of organic semiconductors (OSCs). The introduction of trap states allows the generalized solid-state model to describe the electrical characteristics of both inorganic TFTs and organic field-effect transistors (OFETs). It is revealed through experimental verification that the generalized solid-state model can accurately characterize the bending induced electrical properties of an OFET in the linear and saturation regimes. The third study aims to model the transient and steady-state dynamics of an arbitrary organic semiconductor device under mechanical strain. In this study, the fractional drift-diffusion (Fr-DD) model and its computational scheme with high accuracy and high convergence rate were proposed. Based on simulation and experimental validation, the transconductance and output characteristics of a bendable OFET were found to be well determined by the Fr-DD model not only in the linear and saturation regimes, but also in the subthreshold regime.

The last two decades have shown a boom in the field of wearable sensing technology. Particularly in the consumer industry, growing trends towards personalized health have pushed new devices to report many vital signs, with a demand for high accuracy and reliability. The most common technique used to gather these vitals is photoplethysmography or PPG. PPG devices are ideal for wearable applications as they are simple, power-efficient, and can be implemented on almost any area of the body. Traditionally PPGs were utilized for capturing just heart rate, however, recent advancements in hardware and digital processing have led to other metrics including respiratory rate (RR) and peripheral oxygen saturation (SpO2), to be reported as well. Our research investigates the potential for wearable devices to be used for outpatient apnea monitoring, and particularly the ability to detect opioid misuse resulting in respiratory depression. Ultimately, the long-term goal of this work is to develop a wearable device that can be used in the rehabilitation process to ensure both accountability and safety of the wearer. This document details contributions towards this goal through the design, development, and evaluation of a device called “Kick Ring”. Primarily, we investigate the ability of Kick Ring to record heartrate (HR), RR, and SpO2. Moreover, we show that the device can calculate RR in real time and can provide an immediate indication of abnormal events such as respiratory depression. Finally, we explore a novel method for reporting apnea events through the use of several PPG characteristics. Kick Ring reliably gathers respiratory metrics and offers a combination of features that does not exist in the current wearables space. These advancements will help to move the field forward, and eventually aid in early detection of life-threatening events.