Indiana University-Purdue University Indianapolis (IUPUI) / Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization. The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.

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<p> Zinc, a non-ferrous metal, is consumed as an intermediate input in construction and a wide variety of manufacturing industries. Canada, Australia, Peru and Mexico together produce about 55 percent of the total output but absorb only about 8 percent of the total zinc consumed in the free market world. On the other hand, U.S.A., Japan and the E.E.C. countries together share in about 72 percent of the total consumption but produce only 25 percent of the total zinc ores produced in the free market world. These large imbalances in production and consumption of zinc place it in the group of important international primary commodities. The major aims of this study are to provide a systematic understanding of the institutional and behavioral characteristics of the world zinc industry, and to analyse its performance properties in the framework of a formal model of the international market. </p> <p> A detailed study of the organisational structure of the industry reveals that as many as 24 corporate groups (including their multinational operations) share in about 65 percent of the raw zinc produced in the free market world. In the absence of any other information to the contrary, this low degree of concentration in terms of market control is taken as an evidence for the absence of non-competitive behavior on the sellers' side. However, the working of the free market forces has, often, been influenced by the intervention of the U.S. Government through its stockpile program, tariffs, quotas, and other measures for the protection of the domestic industry. This environment, in turn, has enabled the major U.S. producers to exercise some degree of control on the domestic market through the variations in their stocks of zinc and capacity utilisation ratio. However, the world market on the buyers' side consists of a large number of small consumers of zinc providing a competitive environment.</p> <p> A fairly detailed market form of econometric model is built, based on the above institutional framework and relevant technological and behavioral features. An estimated version of the model indicates different systems of lag responses in the structures of demand and supply to the price of zinc, a very poor substitutability on the demand side, free market price as a long-run equilibrator for the U.S. producers' price, and an important influence of the U.S. interventions on the world market. The model meets reasonably well the predictability criterion based on the technique of dynamic simulation. The performance properties of the world zinc industry, analysed through dynamic multiplier simulation technique, show that the industry exhibits a reasonably stable market environment to the exogenous disturbances such as an increase in the activity levels of consumers and variations in the prices of substitutes. It is, however, quite ·sensitive to technological changes in the consumer industries. The stockpile policy of the U.S. Government does not seem to be properly geared to its objectives, and, in general, it seems to have restricted the development of the industry as a whole.</p> <p> Despite the usual limitations of a first systematic study, it is hoped that this work will contribute towards a better understanding of the salient features of the industry, provide a reasonably sufficient scope for broad policy evaluations, and facilitate the forecasting of the behaviour of major market variables.</p> / Thesis / Doctor of Philosophy (PhD)

Selective Laser Melted (SLM) Additively Manufactured (AM) metal parts are of special interest to many industries because their designs can be made arbitrarily complex while still maintaining bulk type mechanical properties. However, the large thermal gradients inherent to the SLM process generate residual stresses (RS) and distortions that are detrimental to the fit, functionality and integrity of parts. Predicting the state of stresses in as-built 3D printed parts is a difficult problem that currently has only been approached with the use of transient thermomechanical Finite Element Models (FEMs). The nonlinearities associated with AM processes are difficult to capture in these FEMs without incurring into extreme mesh refinements methods which ultimately limit the capability of the simulations. Thus, a significant amount of research has been dedicated to the development of simplified analyses techniques for the prediction of RS due to AM processes. This work presents a novel analytical framework that combines lumped capacitance nonlinear heat transfer with time dependent classical lamination theory and an elastoplastic material model to efficiently and accurately predict residual stresses in as-built SLM parts. The numerical implementation of the analytical model was performed without the need of FEMs, using Python scripting language to generate a simulation capable of producing layer-by-layer temperature time histories as well as time histories of the residual stresses and strains. The simulation was validated using Neutron Diffraction (ND) residual strains measurements of IN718 and Haynes 282 samples, as well as Synchrotron X-Ray Diffraction (XRD) strain data published by the National Institute of Standards and Technology (NIST). Comparisons between the simulation predictions and the experimental data showed excellent agreement for the in-plane strain directions, and general agreement for the out of plane strain component, highlighting an area where further development can be implemented. Significantly, every simulation was able to complete the thermal and structural analysis in a combined time range of 10-20 minutes, compared to the combined solving times associated with nonlinear transient thermal and structural FEA problems, usually in the order of hours to days. The findings of this work pave the way for a better understanding of the cause and effect relationship between SLM printing parameters and the resulting residual stress fields by developing an analytical framework that can incorporate statistical methods, which is not possible with current nonlinear thermomechanical FEMs.

Manufacturing is currently undergoing a fourth industrial revolution, referred to as Industry 4.0, enabled by digital technologies and advances in our ability to collect and use data. Quality 4.0 is the application of Industry 4.0 to enhance the quality function within an organization. Quality practitioners are uniquely positioned within organizations and already possess data application skillsets. Despite a perception that Quality 4.0 will be critical to future success shared by a majority of industry, most companies have not attempted to implement Quality 4.0 strategy, and those that have report very low rates of success. The goal of this study was to understand the challenges and key factors behind implementation of a Quality 4.0 system and develop a model for implementation, highlighting those key factors. The model was developed through literature review, case study analysis, and expert interviews. The model indicated that four main constructs exist in Quality 4.0 deployment, digital strategy, enabling factors, methodologies, and technology. A top-level strategy should be developed to address key technology development themes as well as nontechnical business process themes. Strategy should then be executed in the domain of enabling factors and methodologies with a clear technology application serving as the output. A successful Quality 4.0 implementation will use the technology application to drive tangible quality improvement activities which add value to the business.

Non-destructive evaluation (NDE) techniques are critical for assessing the integrity, health, and mechanical properties of materials manufactured from various methods. High fidelity NDE techniques are essential for quality control but often lead to massive data generation. Such a vast data load cannot be manually processed, this leads to a severe bottleneck for process engineers. Machine learning (ML) offers a solution to this problem by providing powerful and adaptable algorithms capable of learning patterns, identifying features, and finding hidden relationships in large sets of data. Various ML models are used in this work to improve predictions, improve measurements, detect anomalies, classify anomalies, segment images, determine material health, and directly model behavior. These neural network or ML models are implemented to perform these tasks by utilizing data gathered through various NDE techniques. Additive manufacturing enables the production of complex geometries and customized parts with reduced waste and lead times. The development of new material printing capability and techniques is necessary to expand its capabilities to produce high performance parts with unique properties and functionality. Contributions to advanced additive manufacturing are made via the application of customized machine learning algorithms in this work. The development of a novel grain image generation method was completed to improve grain and grain boundary image segmentation methods on microstructure images. Convolutional Neural Networks (CNNs) were also applied to datasets of Stainless Steel Powder to help identify, qualify, and classify the health of the powder prior to print application. A feasibility study of the implementation of Binder Jetting (BJT) is conducted on Martian and Lunar regolith using a simplistic binder in this work. The need for efficient techniques to process data gathered from NDE methods is crucial to enhance the accuracy, efficiency, and speed of the analysis of this data. This will lead to faster development and implementation of advanced manufacturing techniques.

Additive manufacturing (AM) is a revolutionary technology that greatly improves the flexibility of fabricating parts with complex structures and eliminates the cost of making molds. While AM techniques offer unique benefits over traditional manufacturing processes, it is challenging to predict the mechanical behaviors of additively manufactured parts based on design and process parameters. With recent advances in machine learning, data-driven methods have the potential to overcome such limitations. In this work, data-driven modeling frameworks were proposed to predict the tensile, flexural, and compressive behaviors of additively manufactured plastics and composites. Ensemble learning was used to predict the tensile strength of polylactic acid (PLA) with cooperative AM process parameters. A 12.97% mean absolute percentage error (MAPE) was achieved by combining lasso, support vector regression, and extreme gradient boosting in the computational framework. An enhanced ensemble learning method that combines eight different machine learning algorithms was introduced to predict the flexural strength of continuous carbon fiber and short carbon fiber reinforced nylon (CCF-SCFRN) composites with design parameters. Learned knowledge from CCF-SCFRN composites was transferred to continuous glass fiber and short carbon fiber reinforced nylon (CGF-SCFRN) composites for flexural stress-strain curve prediction using an optimal transport (OT) integrated transfer learning framework. Compared with traditional transfer learning, the OT-integrated framework improves the stress-strain curve prediction accuracy by 10.46% in terms of MAPE. The transfer learning framework was further demonstrated in predicting the compressive stress-strain curves of PLA scaffolds with both AM process and design parameters. Three cases were studied by selecting different parameters for domain transfer to validate the generalizability of the proposed framework in predicting mechanical behaviors of additively manufactured materials with limited data.

The need for small and medium size manufacturing enterprises (SMEs) to have access to unbiased advice on best practices and related improvement approaches has been well established. However, this need was not found addressed very effectively in the research literature. Current practice is consultants peddling assessment tools which have the veneer of objectivity, but in reality only highlight the need to purchase their canned solutions. In response, this research attempts to synthesize previous research results and other published assessment methodologies into a taxonomy based assessment methodology (TBAM) which targets the delivery of focused recommendations which target improved performance of the manufacturing enterprise. The assessment methodology which emerges from this research, draws upon two different taxonomies, termed the Manufacturing Enterprise Taxonomy (MET) and the Production System Taxonomy (PST). The MET was developed as one of the deliverables of this research and the PST was developed by a modest modification of previously published research. The TBAM approach was piloted using three different SMEs in order to obtain feedback from the field. As a result TBAM was enhanced using feedback obtained from these three pilot cases. In addition, a review panel process was developed so that a third party review was made of the methodology and its application within the case studies. The review panel was comprised of senior managers which have substantial experience in leading improvements across small and medium size manufacturers. Also, concerns about reliability and validity were addressed and a preliminary set of measures was obtained and evaluated. Based upon this preliminary technique, the validity and reliability results associated with the TBAM approach appear promising.