Cellular activities are composite responses to stimuli from the surroundings. Materials for biological applications, therefore, must be designed with care such that undesired interactions between cells and the materials will not be elicited while cellular responses that are beneficial to the dedicated applications are promoted. Efforts have been made to construct such materials based on both synthetic polymers and natural polymers including poly(ethylene glycol) (PEG) and proteins. In particular, recombinant proteins have drawn great interest for their similar biocompatibility to natural proteins and the uniformity of material properties that is found in manufacturing of synthetic polymers. Recombinant proteins are designed at the DNA level, which allows precise control over the translated protein sequence. By assembling encoded DNA sequences of amino acids with desired functional groups or protein domains conferring desired functionalities, a recombinant protein-based material can be tailored. In this dissertation, works toward developing functional biomaterials based on both synthetic polymers and recombinant proteins are presented.<br>The first part of this thesis encompasses the development of a new thiol-based crosslinking approach to achieve independent control over degradability and mechanical properties of a hydrogel system. Thiol chemistry was chosen as the focus here because it can easily be incorporated into recombinant protein designs by inserting cysteine residues. In addition, the low frequency of cysteine residues in natural proteins can reduce unwanted reactions between the hydrogel material and encapsulated biomolecules or cells. We utilized divinyl sulfone (DVS) to form thioether crosslinking through thiol-ene addition and ferric ethylenediaminetetraacetic acid (ferric EDTA) to make disulfide crosslinking via thiol oxidation. By controlling the ratio between the non-reducible thioether bonds to reducible disulfide bonds, hydrogels with similar mechanical properties can be made with different degradability in reducing conditions. Accelerated degradation and increased release of encapsulated dextran was observed in response to an extracellular reducing condition. Good viability of encapsulated fibroblasts also suggested high cytocompatibility of the crosslinking approach. This work demonstrated the potential of thiol crosslinking by DVS and ferric EDTA for making redox-responsive drug delivery vehicles and tissue engineering scaffolds.<br>In the second part, we developed protein adhesives using thiol- or catechol-based adhesion. Every year more than 310 million surgeries are performed around the world, and more than 50% of these surgeries used sutures or staples for wound closure. Surgical sealants or adhesive can be applied together with sutures and staples to mitigate the risk of infection. Protein-based adhesives could have better biocompatibility than synthetic polymer-based adhesives and have the potential of providing biochemical cues for cellular responses. Many adhesive proteins have been found in nature. Among them, mussel adhesive proteins have been actively studied for their outstanding underwater adhesion. The capability of being able to cure in a wet environment is critical for an ideal surgical sealant and adhesive. Mussels uses both thiols and a catechol, 3, 4-dihydroxyphenylalanine (DOPA), to achieve underwater adhesion. Inspired by mussel adhesive proteins and modular recombinant design, we developed two proteins harboring thiol or DOPA groups with highly similar amino acid sequences. The adhesion performance, including curing kinetics, adhesion strength, and cytocompatibility, were compared between the two proteins. The similarity in the protein sequences allows us to focus on the performance difference between thiol- and DOPA-based adhesion. We also showed that a synergistic increase in the adhesion strength can be achieved when the two proteins are combined. This increase indicates a cross-reaction between thiol and DOPA groups. Our results provide insights into selecting the chemistry for designing adhesives based on the needs of the applications.<br>In the last part, we studied the lower critical solution temperature (LCST) behavior of elastin-like polypeptides (ELPs) with a series of ELPs with rationally designed charge distributions and chain lengths. The LCST behavior of ELPs are controlled by multiple factors including the amino acid composition, ELP chain length, protein concentration, salt identity, salt concentration, and pH of the solution. Fusion of other non-ELP recombinant protein domains to ELPs have also been shown to influence the LCST behavior of the fusion ELP protein. Inspired by this effect, we explored the use of short non-ELP sequences as a new way to tailor the LCST behavior of ELP-based proteins. We designed the non-ELP and the ELP sequences with different pH-dependent charge states and showed that pH sensitivity was introduced to the LCST behavior by electrostatic and hydrophobic interactions between the non-ELP and ELP sequences. The electrostatic interactions can be shielded by the ionic strength in the protein solution. The pH sensitivity was introduced by the non-ELP sequences, and this sensitivity decreased when the relative length of the ELP domain increased. We also found that the hydrophobicity of the non-ELP sequences changes the interactions between the proteins and Hofmeister ions in solution. Our results demonstrated the potential of using non-ELP sequences as a new factor in controlling the LCST behavior of ELP proteins.<br><br>

<p>  </p> <p>Recently, there has been significant increase in research and development in the field of organic electronics. This is mainly because organic electronic devices can be flexible, lightweight, and processed from solution using low-cost manufacturing techniques. Typically, these devices have utilized conjugated polymers as their active layer components. This approach has been successful, but the use of conjugated polymers comes with limitations. To address these limitations and expand the field of organic electronics, this work studies a novel class of macromolecules, radical polymers. Unlike their conjugated polymer counterparts, radical polymers are comprised of a non-conjugated backbone with stable open-shell groups at their pendant sites. By studying the structure-property relationships of these radical polymers, this work developed novel polymer systems for a variety of organic electronic applications. Furthermore, these studies can be applied to future radical polymer systems yet to be discovered. Ultimately, this work served as a template for expanding the field of organic electronics. </p>

In recent years, new regulatory guidance has spurred organizations to replace hazardous chemicals with safer alternatives. The factors and influences that shape decisions to transition to safer chemicals are of interest to decision scientists. Previous studies have examined the role that various factors, such as regulation, health impacts, and environmental impacts, have played in shaping such decisions. However, two key research gaps have been identified. First, existing semi-quantitative-based studies do not adequately capture the complexity of decision-making. Second, no in-depth qualitative study of a current substitution process, elucidating decision-making mechanisms at various stages of the design process, has yet been performed. The current research addresses these gaps. The first component of the study is an extensive survey of product and chemical manufacturers to elicit potential tradeoffs concerning final product design and redesign decisions. Such tradeoffs are characterized by a set of six factors affecting product design, which are further disaggregated into thirty-three attributes distributed across these factors. Statistical methods including Bayesian Dirichlet modeling and Principal Component Analysis were used to show: 1) two factors were statistically significantly different than other factors, 2) how features such as company size and time of decision affected factor weighting, and 3) that nine principal components explain 79% of the variance in the attribute scores. The second component of the study was a phenomenological assessment of a current substitution process: replacement of cadmium with Zn-Ni for aircraft components, undertaken by the U.S. Navy and Air Force. This study synthesized existing research in cognition, decision-making, and knowledge management. Semi-structured interviews were conducted with participants representing engineering, environmental, safety, and management disciplines. Qualitative analysis was used to identify and characterize the underlying mechanisms guiding the decision process, including external/internal influences, organizational structure and inertia, and innovative team problem solving. The results from this research contribute to theoretical knowledge in decision-making and cognition, as well as practical knowledge for organizations and policymakers. The broader implications of this research study include a realization that decision tradeoffs vary based on decision contexts, indicating that sector-specific future policy and guidance efforts are needed.

<div> <p>Guided by the continuous manufacturing paradigm shift in the pharmaceutical industry, the proposed thesis focuses on the implementation of an integrated continuous crystallization platform, the oscillatory baffled crystallizer (OBC), with real time process monitoring. First, by defining an appropriate operating regime with residence time distribution (RTD) measurements, a system can be defined that allows for plug flow operation while also maintaining solid suspension in a two-phase system. The aim of modern crystallization processes, narrow crystal size distributions (CSDs), is a direct result of narrow RTDs. Using a USB microscope camera and principal component analysis (PCA) in pulse tracer experiments, a novel non-contact RTD measurement method was developed using methylene blue. After defining an operating region, this work focuses on a specific process intensification technique, namely spherical crystallization.</p> <p>Used mainly to tailor the size of a final dosage form, spherical crystallization removes the need for downstream size-control based unit operations (grinding, milling, and granulation), while maintaining drug efficacy by tailoring the size of the primary crystals in the agglomerate. The approach for generating spherical agglomerates is evaluated for both small and large molecules, as there are major distinctions in process kinetics and mechanisms. To monitor the spherical agglomeration process, a variety of Process Analytical Technology (PAT) tools were used and the data was implemented for scale-up applications.</p> <p>Lastly, a compartmental model was designed based on the experimental RTD data with the intention of predicting OBC mixing and scale-up dynamics. Together, with validation from both the DN6 and DN15 systems, a scale independent equation was developed to predict system dispersion at different mixing conditions. Although it accurately predicts the behavior of these two OBC systems, additional OBC systems of different scale, but similar geometry should be tested for validation purposes.</p> </div> <br>

<p>Molecular separations are essential in the production of many chemicals and purified products. Of all the available separation technologies, distillation, which is a thermally driven process, has been and continues to be one of the most utilized separation methods in chemical and petrochemical plants. Although distillation and other commercial technologies fulfilled most of the current separation needs, the energy-intensive nature of many molecular separations and the growing concern of reducing CO₂ emissions has led to intense research to seek for more energy-efficient separation processes.<br></p><p><br></p><p></p> <p>Among the emerging separation technologies alternative to distillation, there is special attention on non-thermally driven methods, such as membranes. The growing interest in non-thermal methods, and particularly in the use of membranes, has been influenced significantly from the widespread perception that they have a potential to be markedly less energy-intensive than thermal methods such as distillation. Even though many publications claim that membranes are more energy-efficient than distillation, except for water desalination, the relative energy intensity between these processes in the separation of chemical mixtures has not been deeply studied in the literature. One of the objectives of this work focuses on introducing a framework for comparative analysis of the energy intensity of membranes and distillation. </p><p><br></p> <p>A complication generally encountered when comparing the energy consumption of membranes against an alternative process is that often the purity and recovery that can be achieved through a single membrane stage is limited. While using a multi-stage membrane process is a plausible solution to achieve both high purity and recovery, even for a simple binary separation, finding the most suitable multistage membrane process is a difficult task. This is because, for a given separation, there exists multiple cascades that fulfill the separation requirements but consume different amounts of energy. Moreover, the energy requirement of each cascade depends on the operating conditions. The first part of this work is dedicated to the development of a Mixed Integer Non-linear Program (MINLP) which allows for a given gaseous or liquid binary separation, finding the most energy-efficient membrane cascade. The permeator model, which is derived from a combination of the cross-flow model and the solution diffusion theory, and is originally expressed as a differential-algebraic equation (DAE) system, was integrated analytically before being incorporated in the optimization framework. This is in contrast to the common practice in the literature, where the DAE system is solved using various discretization techniques. Since many of the constraints have a non-convex nature, local solvers could get trapped in higher energy suboptimal solutions. While an option to overcome this limitation is to use a global solver such as BARON, it fails to solve the MINLP to the desired optimality in a reasonable amount of time for most of the cases. For this reason, we derive additional cuts to the problem by exploiting the mathematical properties of the governing equations and from physical insights. Through numerical examples, we demonstrate that the additional cuts aid BARON in expediting the convergence of branch-and-bound and solve the MINLP within 5%-optimality in all the cases tested in this work.</p><p><br></p> <p>The proposed optimization model allows identifying membrane cascades with enhanced energy efficiency that could be potentially used for existing or new separations. In addition, it allows to compare the optimum energy consumption of a multistage membrane process against alternative separations methods and aid in the decision of whether or not to use a membrane system. Nevertheless, it should be noted that when a membrane process or any other non-thermal separation process is compared with a thermal process such as distillation, an additional complication often arises because these processes usually use different types of energies. Non-thermal processes, such as membranes, consume electrical energy as work, whereas thermal processes, such as distillations, usually consume heat, which is available in a wide range of temperatures. Furthermore, the amount of fuel consumed by a separation process strongly depends on how its supplied energy is produced, and how it is energy integrated with the rest of the plant. Unfortunately, common approaches employed to compare the energy required by thermal and non-thermal methods often lead to incorrect conclusions and have driven to the flawed perception that thermal methods are inherently more energy-intensive than non-thermal counterparts. In the second part of this work, we develop a consistent framework that enables a proper comparison of the energy consumption between processes that are driven by thermal and non-thermal energy (electrical energy). Using this framework, we refute the general perception that thermal separation processes are necessarily the most energy-intensive and conclusively show that in several industrially important separations, distillation processes consume remarkably lower fuel than non-thermal membrane alternatives, which have often been touted as more energy efficient.</p><p><br></p> <p>In order to gain more understanding of the conditions where membranes or distillation are more energy-efficient, we carried out a comprehensive analysis of the energy consumed by these two processes under different operating conditions. The introduced energy comparison analysis was applied to two important separation examples; the separation of p-xylene/o-xylene, and propylene/propane. Our results showed that distillation is more energy favored than membranes when the target purity and recovery of the most volatile (resp. most permeable) component in the distillate (resp. permeate) are high, and particularly when the feed is not too concentrated in the most volatile (resp. most permeable) component. On the other hand, when both the recovery and purity of the most volatile (resp. most permeable) component are required at moderate levels, and particularly when the feed is highly enriched in the most volatile (resp. most permeable) component, membranes show potential to save energy as compared to distillation.</p>

<p>Explosives and rocket fuel are just two prime examples of energetic materials, compounds that contain a combustible fuel and oxidizer within the same substance. Recent advances have enabled the construction of energetic materials through multiple variations of additive manufacturing, principally inkjet, direct-write, fused filament fabrication, electrospray deposition, and stereolithography. Many of the methods used for creating multiple layered objects (three-dimensional) from energetic materials involve the use of highly viscid materials.</p> <p>The focus of this work was to design a process capable of additively manufacturing three-dimensional objects from melt-castable energetic materials, which are known for their low viscosity. An in-depth printer design and fabrication procedure details the process requirements discovered through previous works, and the adaptations available and used to construct an additive manufacturing device capable of printing both energetic and non-energetic (also referred to as inert) melt-castable materials. Initial characterization of three proposed inert materials confirmed their relative similarity in rheological properties to melt-castable energetic materials and were used to test the printer’s performance.</p> <p>Preliminary tests show the constructed device is capable of additively manufacturing melt-castable materials reproducibly in individual layers, with some initial successful prints in three-dimensions, up to three layers. An initial characterization of the printer’s deposition characteristics additionally matches literature predictions. With the ability to print three-dimensional objects from melt-castable materials confirmed, future work will focus on the reproducibility of multi-layered objects and the refined formulation of melt-castable energetic materials.</p>

<p>Producing bio-based commodity chemicals, such as polymers and fuels, is of significant interest as petroleum reserves continue to decline. A major roadblock to bio-based production is high processing costs. These costs are associated with the need for highly-specialized catalysts to produce bio-based commodity chemicals from agricultural products and wastes. This prevents bioprocessing facilities from fully taking advantage of commodities of scale, where purchasing materials in greater quantities reduces the material cost. Discovering catalysts capable of being used in multiple production pathways could reduce the per unit processing of a biorefinery. <br> Recent works have shown that maleic acid can be used for multiple conversion reactions of plant material to valuable products: xylose to furfural, glucose to hydroxymethylfurfural (HMF), and the pretreatment of lignocellulosic material for second generation biofuel production. This work evaluates the use of maleic acid as a catalyst for producing HMF from corn starch, with a specific focus on reducing operating costs. Additionally, the use of maleic acid as a liquefaction catalyst for producing corn stover slurries is tested. </p> <p>To evaluate HMF production from starch, a combined computational and experimental approach is used. Through modelling and experimental validation, molar HMF yields of ~30% are reached by incorporating dilute dimethylsulfoxide and acetonitrile into the reaction mixture. However, HMF yield was limited by low stability in the reaction media. The addition of activated carbon to the reactor overcomes challenges with second order side reactions, resulting in HMF selling prices that are competitive with similar petroleum-derived chemicals. The key technical roadblocks to commercialization of HMF production are identified as solvent recycling and HMF separation efficiency in a sensitivity analysis. During liquefaction of corn stover, maleic acid was found to reduce the yield stress required to begin slurry flow through a pipe. However, a reduction in the free water content of the reactor through binding of water in the matrix of biomass limited liquefaction, resulting in solids concentrations not financially feasible at scale. To overcome this, maleic acid treatment was performed at solids contents of 25%, followed by a water removal step and enzymatic liquefaction at 30% solids. Yield stress was reduced from >6000 Pa for untreated samples to ~50 Pa for samples treated with maleic acid and enzymes sequentially. Such treatment reduces the challenges associated with feeding solid biomass into a pretreatment reactor. Additionally, reduced slurry yield stress results in lower capital costs, since smaller pumps can be used in the production facility. </p> This work provides a step forward in transitioning away from a petroleum-based economy to a bio-based economy without significant disruptions in product pricing and availability.

<p>In this dissertation, numerical analysis frameworks and software tools for digital design of process systems are developed. More specifically, these tools have been focused on digital design within the pharmaceutical manufacturing space. Batch processing represents the traditional and still predominant pathway to manufacture pharmaceuticals in both the drug substance and drug product spaces. Drug substance processes start with raw materials or precursors to produce an active pharmaceutical ingredient (API) through synthesis and purification. Drug product processes take this pure API in powder form, add excipients, and process the powder into consumer doses such as capsules or tablets.  Continuous manufacturing has allowed many other chemical industries to take advantage of real-time process management through process control, process optimization, and real-time detection of off-spec material. Also, the possibility to reduce total cleaning time of units and encourage green chemistry through solvent reduction or recycling make continuous manufacturing an attractive alternative to batch manufacturing. However, to fully understand and take advantage of real-time process management, digital tools are required, both as soft sensors during process control or during process design and optimization.  Since the shift from batch to continuous manufacturing will proceed in stages, processes will likely adopt both continuous and batch unit operations in the same process, which we will call {\em hybrid} pharmaceutical manufacturing routes. Even though these processes will soon become common in the industry, digital tools that address comparison of batch, hybrid, and continuous manufacturing routes in the pharmaceutical space are lacking. This is especially true when considering hybrid routes. For this reason, PharmaPy, an open-source tool for pharmaceutical process development, was created to address rapid in-silico design of hybrid pharmaceutical processes.  Throughout this work, the focus is on analyzing alternative operating modes within the drug substance manufacturing context. First, the mathematical models for PharmaPy's synthesis, crystallization, and filtration units are discussed. Then, the simulation capabilities of PharmaPy are highlighted, showcasing dynamic simulation of both fully continuous and hybrid processes. However, the technical focus of the work as a whole is primarily on optimization techniques for pharmaceutical process design. Thus, many derivative-free optimization frameworks for simulation-optimization were constructed and utilized with PharmaPy performing simulations of pharmaceutical processes.  The timeline of work originally began with derivative-based methods to solve mixed-integer programs (MIP) for water network sampling and security, as well as nonlinear programs (NLPs) and some mixed-integer nonlinear programs (MINLPs) for design space and feasibility analysis. Therefore, a method for process design that combines both the ease of implementation from a process simulator (PharmaPy) with the computational performance of derivative-based optimization was implemented. Recent developments in Pyomo through the PyNumero package allow callbacks to an input-output or black-box model while using {\sc Ipopt} as a derivative-based solver through the cyipopt interface. Using this approach, it was found that using a PharmaPy simulation as a black box within a derivative-based solver resulted in quicker solve times when compared with traditional derivative-free optimization strategies, and offers a much quicker implementation strategy than using a simultaneous equation-oriented algebraic definition of the problem.  Also, uncertainty exists in virtually all process systems. Traditionally, uncertainty is analyzed through sampling approaches such as Monte Carlo simulation. These sampling approaches quickly become computational obstacles as problem scale increases. In the 1980s, chemical plant design under uncertainty through {\em flexibility analysis} became an option for explicitly considering model uncertainty using mathematical programming. However, such formulations provide computational obstacles of their own as most process models produce challenging MINLPs under the flexibility analysis framework.  Specifically when considering pharmaceutical processes, recent initiatives by the FDA have peaked interest in flexibility analysis because of the so called {\em design space}. The design space is the region for which critical quality attributes (CQAs) may be guaranteed over a set of interactions between the inputs and process parameters. Since uncertainty is intrinsic to such operations, industry is interested in guaranteeing that CQAs hold with a set confidence level over a given operating region. In this work, the {\em probabilistic design space} defined by these levels of confidence is presented to address the computational advantages of using a fully model-based flexibility analysis framework instead of a Monte Carlo sampling approach. From the results, it is seen that using the flexibility analysis framework decreased design space identification time by more than two orders of magnitude.  Given implementation difficulty with new digital tools for both students and professionals, educational material was developed for PharmaPy and was presented as part of a pharmaceutical API process development course at Purdue. The students were surveyed afterward and many of the students found the framework to be approachable through the use of Jupyter notebooks, and would consider using PharmaPy and Python for pharmaceutical modeling and data analysis in the future, respectively.  Through software development and the development of numerical analysis frameworks, digital design of pharmaceutical processes has expanded and become more approachable. The incorporation of rigorous simulations under process uncertainty promotes the use of digital tools in regulatory filings and reduces unnecessary process development costs using model-based design. Examples of these improvements are evident through the development of PharmaPy, a simulation-optimization framework using PharmaPy, and flexibility analysis tools. These tools resulted in a computational benefit of 1 to 2 orders of magnitude when compared to methods used in practice and in some cases reduce the modeling time required to determine optimal operating conditions, or the design space of a pharmaceutical manufacturing process.</p>

<p>Cutting greenhouse gas emissions to as close to zero as possible, or ”net-zero”, may be the biggest sustainability goal to be achieved in the next 30 years. While chemical engineering evolved against the backdrop of an abundant supply of fossil resources for chemical production and energy, renewable energy resources such as solar and wind will find more usage in the future. This thesis work develops new concepts, methods and algorithms to identify and synthesize process schemes to address multiple aspects towards sustainable chemical and energy systems. Shale gas can serve as both energy resource and chemical feedstock for the transition period towards a sustainable economy, and has the potential to be a carbon source for the long term. The past two decades have seen increasing natural gas flaring and venting due to the lack of transforming or transportation infrastructure in emerging shale gas producing regions. To reduce carbon emission and wastage of shale resources, an innovative process hierarchy is identified for the valorization of natural gas liquids from shale gas at medium to small scale near the wellhead. This paradigm shift fundamentally changes the sequencing of various separation and reaction steps and results in dramatically simplified and intensified process flowsheets. The resulting processes could achieve over 20% lower capital with a higher recovery of products. Historically, heat energy is supplied to chemical plants by burning fossil resources. However, in future, with the emphasis on greenhouse gas reduction, renewable energy resources will find more usage. Renewable electricity from photovoltaic and wind has now become competitive with the electricity from fossil resources. Therefore, a major challenge for chemical engineering processes is how to use renewable electricity efficiently within a chemical plant and eliminate any carbon dioxide release from chemical plants. We introduce several decarbonization flowsheets for the process to first convert natural gas liquids (NGLs) to  mainly ethylene in an energy intensive dehydrogenation reactor and subsequent conversion of ethylene into value-added and easy-to-transport liquid fuels. </p> <p><br></p> <p>Molecular separations are needed across many types of industries, including oil and gas, food, pharmaceutical, and chemical industries. In a chemical plant, 40%–60% of energy and capital cost is tied to separation processes. For widespread use of membrane-based processes for high recovery and purity products from gaseous and liquid mixtures on an industrial scale, availability of models that allow the use of membrane cascades at their optimal operating modes is desirable towards sustainable separation systems. This will also enable proper comparison of membrane performance vis-a-vis other competing separation technologies. However, such a model for multicomponent fluid separation has been missing from the literature. We have developed an MINLP global optimization algorithm that guarantees the identification of minimum power consumption of multicomponent membrane cascades. The proposed optimization algorithm is implemented in GAMS and is demonstrated to have the capability to solve up to 4-component and 5-stage membrane cascades via BARON solver, which is significantly more advantageous than the state-of-the-art processes. The model is currently being further developed to include optimization of total cost including capital. Such a model holds the promise to be useful for the development in implementation of energy-efficient separation plants with least carbon footprint. This thesis work also addresses important topics in separation including dividing wall columns and water desalination. </p>

<p>Inaccurate cost estimates contribute to lost implementation opportunity of novel manufacturing technologies or lost revenue due to under-bidding or loss of an over-bid contract. High-volume, long-term orders, such as those the automotive industry begets, are desired as they lock in revenue streams for months into years. However, high-rate composite materials and their manufacturing processes are novel among the industry and traditional costing methods have not advanced at a proportional rate. This research effort developed a method to reduce the complex composite manufacturing systems to fungible, upgradable, and linkable individual processes that derive their manufacturing parameters from the performance part design process. Employing technical cost modeling, this method accurately quantifies the value of pursuing composite manufacturing by integrating impregnation, solidification, heat transfer, kinetics, and additional technical data from computer-aided part design simulation tools to deliver an accurate cost estimate. </p> <p>Cost modeling provides a quantitative result that weighs heavily in the decision making process for adoption of a new manufacturing method. In this dissertation, three case studies were investigated for three different management decision cases: part production management, in-house manufacturing management, and global manufacturing management. </p> <p>Part production management is the decision making process for selecting a certain manufacturing method. A case study with a Tier 1 Part Producer was conducted to provide a comparison of two emerging novel preforming systems versus their in-use, metals based high-rate manufacturing line in manufacturing a structural automotive part. Determining material usage was the primary cost driver focus. Equipment Supplier A’s process operated by seaming single layers of thermoplastic tape into rolls and then stacking prior to consolidation and resulted in a scrap rate of 23-28% with a cost of $32.87-36.01 per kilogram saved depending on the input tape width. Equipment Supplier B’s layup process, essentially a multi-head automatic tape layup machine, resulted in scrap rate of 20-27% with a cost of $34.48-36.67 per kilogram saved depending on the input tape width. This exceeded the Tier 1 Part Producer’s requirement of $6.6-11 per kilogram saved and led to them to abandon this application as a feasible project and instead look for a different part with a higher return regarding cost for weight saved.</p> <p>In-house manufacturing management is the decision making process governing manufacturing operating procedures. A case study for the Manufacturing Design Laboratory’s (MDLab) hybrid molding line was undertaken to determine the manufacturing cost for a composite test coupon. Processing parameters were obtained from three sources: performance design computer aided engineering (CAE), common industry transfer estimation times, and a calculated preform layup time. Compared to a similarly shaped test coupon made of aluminum, highly-automated manufacturing realizes weight savings of 46.25% and cost savings of 16.5%. Low-automation manufacturing captures the same weight savings, but has a cost for weight saved penalty, cost increase, of $9.89 per kilogram, showing how influential the labor contribution is to manufacturing cost. </p> <p>Global manufacturing management is the decision making process governing manufacturing location. Various manufacturing cost drivers are location dependent, thus a dataset was developed to alter these parameters for the U.S. states. Global comparisons are accomplished through indexing of global cost of living allowances and labor rates. Within the U.S., high-automation manufacturing costs in the West Coast/Pacific are 20.1% greater compared to the Midwest and similarly, low-automation costs are 21.2% greater. Globally, high-automation manufacturing costs in North America are 52.1% greater compared to Asia while low-automation costs are 116.5% greater. These variations highlight why we see geographically clustered manufacturing centers within the states and major manufacturing relocations due to cost sensitive and labor sensitive production. </p>