Industrial & Manufacturing Engineering

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Research from the Department of Industrial & Manufacturing Engineering. The department website may be found at https://www.ndsu.edu/ce/https://www.ndsu.edu/ime/

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Adaptive Production Planning and Scheduling for the Make-to-order DNA Manufacturing System
(North Dakota State University, 2010) Song, Dan
This thesis develops an adaptive production planning and scheduling system for the make-to-order plasmid (DNA) manufacturing system. The system, which has stochastic nature and random demand, was represented by a mathematical programming model first. Then in order to solve it, discrete-event simulation models were developed to generate a feasible schedule that maximizes the production throughput in the planning horizon in a mix-product type environment. A special heuristic order selecting and splitting procedure was designed to aid the production planning and scheduling process. Experiments were conducted to evaluate the algorithm and results are compared with those obtained by using four classic dispatching rules, such as first come first served (FCFS) and shortest processing time (SPT). To take advantage of simulation results, a rule-based expert system was created with pre-defined scheduling rules. Rules regarding production planning and scheduling can be used by human schedulers easily and the system is very flexible in further extension.
Advanced Numerical Modeling in Manufacturing Processes
(North Dakota State University, 2022) Dey, Arup
In manufacturing applications, a large number of data can be collected by experimental studies and/or sensors. This collected data is vital to improving process efficiency, scheduling maintenance activities, and predicting target variables. This dissertation explores a wide range of numerical modeling techniques that use data for manufacturing applications. Ignorance of uncertainty and the physical principle of a system are shortcomings of the existing methods. Besides, different methods are proposed to overcome the shortcomings by incorporating uncertainty and physics-based knowledge. In the first part of this dissertation, artificial neural networks (ANNs) are applied to develop a functional relationship between input and target variables and process parameter optimization. The second part evaluates the robust response surface optimization (RRSO) to quantify different sources of uncertainty in numerical analysis. Additionally, a framework based on the Bayesian network (BN) approach is proposed to support decision-making. Due to various uncertainties, estimating interval and probability distribution are often more helpful than deterministic point value estimation. Thus, the Monte Carlo (MC) dropout-based interval prediction technique is explored in the third part of this dissertation. A conservative interval prediction technique for the linear and polynomial regression model is also developed using linear optimization. Applications of different data-driven methods in manufacturing are useful to analyze situations, gain insights, and make essential decisions. But, the prediction by data-driven methods may be physically inconsistent. Thus, in the fourth part of this dissertation, a physics-informed machine learning (PIML) technique is proposed to incorporate physics-based knowledge with collected data for improving prediction accuracy and generating physically consistent outcomes. Each numerical analysis section is presented with case studies that involve conventional or additive manufacturing applications. Based on various case studies carried out, it can be concluded that advanced numerical modeling methods are essential to be incorporated in manufacturing applications to gain advantages in the era of Industry 4.0 and Industry 5.0. Although the case study for the advanced numerical modeling proposed in this dissertation is only presented in manufacturing-related applications, the methods presented in this dissertation is not exhaustive to manufacturing application and can also be expanded to other data-driven engineering and system applications.
Assessing Reliability of Highly Reliable Products Using Accelerated Degradation Test Design, Modeling, and Bayesian Inference
(North Dakota State University, 2019) Limon, Shah Mohammad
The accelerated degradation test methods have proven to be a very effective approach to quickly evaluate the reliability of highly reliable products. However, the modeling of accelerated degradation test data to estimate reliability at normal operating condition is still a challenging task especially in the presence of multi-stress factors. In this study, a nonstationary gamma process is considered to model the degradation behavior assuming the strict monotonicity and non-negative nature of the product deterioration. It further assumes that both the gamma process parameters are stress dependent. A maximum likelihood method has been used for the model parameter estimation. The case study results indicate that traditional models that assume only shape parameter as stress dependent underestimate the product reliability significantly at normal operating conditions. This study further revealed that the scale parameter at a higher stress level is very close to the traditional constant assumption. However, at the normal operating condition, scale parameter value differs significantly with the traditional constant assumption value. This difference leads to the larger difference of reliability and lifetime estimates provided by the proposed approach. A Monte Carlo simulation with the Bayesian updating method has been incorporated to update the gamma parameters and reliability estimates when additional degradation data become available. A generalized reliability estimation framework for using the ADT data is also presented in this work. Further, in this work, an optimal constant-stress accelerated degradation test plan is presented considering the gamma process. The optimization criteria are set by minimizing the asymptotic variance of the maximum likelihood estimator of the lifetime at operating condition under total experimental cost constraint. A heuristic based more specifically genetic algorithm approach has been implemented to solve the model. Additionally, a sensitivity analysis is performed which revealed that increasing budget causes longer test duration time with smaller sample size. Also, it reduces the asymptotic variance of the estimation which is very intuitive as more budget increase the possibility to generate more degradation information and helps to increase the estimation accuracy. The overall reliability assessment methodology and the test design has been demonstrated using the carbon-film resistor degradation data.
CFD Heat Transfer Simulation of the Human Upper Respiratory Tract for Oronasal Breathing Condition
(North Dakota State University, 2011) Srinivasan, Raghavan
In this thesis. a three dimensional heat transfer model of heated airflow through the upper human respiratory tract consisting of nasal, oral, trachea, and the first two generations of bronchi is developed using computational fluid dynamics simulation software. Various studies have been carried out in the literature investigating the heat and mass transfer characteristics in the upper human respiratory tract, and the study focuses on assessing the injury taking place in the upper human respiratory tract and identifying acute tissue damage based on level of exposure. The model considered is for the simultaneous oronasal breathing during the inspiration phase with high volumetric flow rate of 90/liters minute and a surrounding air temperature of 100 degrees centigrade. The study of the heat and mass transfer, aerosol deposition and flow characteristics in the upper human respiratory tract using computational fluid mechanics simulation requires access to a two dimensional or three dimensional model for the human respiratory tract. Depicting an exact model is a complex task since it involves the prolonged use of imaging devices on the human body. Hence a three dimensional geometric representation of the human upper respiratory tract is developed consisting of nasal cavity, oral cavity, nasopharynx, pharynx, oropharynx, trachea and first two generations of the bronchi. The respiratory tract is modeled circular in cross-section and varying diameter for various portions as identified in this study. The dimensions are referenced from the literature herein. Based on the dimensions, a simplified model representing the human upper respiratory tract is generated.This model will be useful in studying the flow characteristics and could assist in treatment of injuries to the human respiratory tract as well as help optimize drug delivery mechanism and dosages. Also a methodology is proposed to measure the characteristic dimension of the human nasal and oral cavity at the inlet/outlet points which are classified as internal measurements.
Designing Bio-Ink for Extrusion Based Bio-Printing Process
(North Dakota State University, 2019) Habib, MD Ahasan
Tissue regeneration using in-vitro scaffold becomes a vital mean to mimic the in-vivo counterpart due to the insufficiency of animal models to predict the applicability of drug and other physiological behavior. Three-dimensional (3D) bio-printing is an emerging technology to reproduce living tissue through controlled allocation of biomaterial and cell. Due to its bio-compatibility, natural hydrogels are commonly considered as the scaffold material in bio-printing process. However, repeatable scaffold structure with good printability and shape fidelity is a challenge with hydrogel material due to weak bonding in polymer chain. Additionally, there are intrinsic limitations for bio-printing of hydrogels due to limited cell proliferation and colonization while cells are immobilized within hydrogels and don’t spread, stretch and migrate to generate new tissue. The goal of this research is to develop a bio-ink suitable for extrusion-based bio-printing process to construct 3D scaffold. In this research, a novel hybrid hydrogel, is designed and systematic quantitative characterization are conducted to validate its printability, shape fidelity and cell viability. The outcomes are measured and quantified which demonstrate the favorable printability and shape fidelity of our proposed material. The research focuses on factors associated with pre-printing, printing and post-printing behavior of bio-ink and their biology. With the proposed hybrid hydrogel, 2 cm tall acellular 3D scaffold is fabricated with proper shape fidelity. Cell viability of the proposed material are tested with multiple cell lines i.e. BxPC3, prostate stem cancer cell, HEK 293, and Porc1 cell and about 90% viability after 15-day incubation have been achieved. The designed hybrid hydrogel demonstrate excellent behavior as bio-ink for bio-printing process which can reproduce scaffold with proper printability, shape fidelity and higher cell survivability. Additionally, the outlined characterization techniques proposed here open-up a novel avenue for quantifiable bio-ink assessment framework in lieu of their qualitative evaluation.
A Domain-Knowledge Modeling of Hospital-Acquired Infection Risk in Healthcare Personnel From Retrospective Observational Data: A Case Study for Covid-19
(North Dakota State University, 2022) Huynh, Phat
Healthcare personnel (HCP) is facing a consistent risk of viral infections. We proposed a domain-knowledge-driven infection risk model to quantify the individual HCP and the population-level risks. For individual-level risk estimation, a time-variant model was proposed to capture the disease transmission dynamics. At the population-level, the infection risk was estimated using a Bayesian network model constructed from three feature sets. For model validation, we investigated the case study of the Coronavirus disease. The variance-based sensitivity analysis indicated that the uncertainty in the estimated risk was attributed to two variables: the number of close contacts and the viral transmission probability. We further validated the individual risk model by considering six occupations in the U.S. O*Net database. For the population-level risk model validation, the infection risk in Texas and California was estimated. The accurate estimation of infection risk will significantly enhance the PPE allocation, safety plans for HCP, and hospital staffing strategies.
Dynamic Pricing in Supply Chains Bringing the Perishable Approach to Dynamic Car Market
(North Dakota State University, 2014) Tripathi, Prateek
In a business environment, using dynamic pricing is a standard practice, especially in the management of revenue. Given the availability of online information concerning inventory and pricing, customers are in a position to understand pricing strategies that sellers employ, and at the same time to be able to develop a possible response strategy. In this thesis, Dynamic Pricing in the Supply Chain: Bringing the Perishable Approach to Dynamic Car Market is investigated and evaluated. This study incorporates strategic consumer response to dynamic prices, particularly for perishable goods, using a number of variables, such as income, demand and price. The main factors that influence stochastic behavior of prices in car market supply chains are the focus of the analysis. It also includes the appropriate parameters to include in a dynamic optimization-pricing supply chain problem and a discussion of how businesses can efficiently optimize the pricing problem in a stochastic market situation.
Economic Analysis of Packaging Systems
(North Dakota State University, 2011) Biradar, Vaibhav Mahadev
Packaging has a significant impact on the efficiency and effectiveness of the supply chain, where improvement can be achieved through the development and selection of an appropriate packaging system. One way to explore this is through the development and use of mathematical models that facilitate economic analysis of packaging systems. Recently, one of the most remarkable trends in logistics is the extensive use of returnable or reusable containers. Returnable container systems have increasingly been introduced in various industries to take advantages of cost savings, but it is very crucial to ensure that a reusable packaging system is an economical packaging choice. In this thesis, an extensive study of an economic analysis of disposable, recyclable, and reusable packaging systems is conducted. This includes identification of significant cost factors and variables involved in the management of disposable, recyclable and reusable packaging systems, and formulation of a mathematical model to compare total cost of packaging systems. The developed mathematical model can be used to choose the most economical packaging system for industries. The linear programming (LP) method is used to develop the mathematical model. The various new factors such as the collapsible ratio of recyclable, disposable and reusable packages have been introduced for the first time in the economic analysis of the packaging systems. The developed mathematical model can be used for a range of industries and for different industry scenarios. The packaging system information of Toyota assembly plant is used for the validation of a mathematical model. The obtained results are compared with previous research based on the same data set and results found in concert with the finding of previous research which validate the model.
The Effect of Stress on Task Capacity and Situational Awareness
(North Dakota State University, 2012) Karim, Reza Ul
In today’s industry, many occupations require manpower resources to include both labor and cognitive resources. As the technology is rapidly changing and businesses are becoming more dependent on cognitive performance, it is essential to find any effect physical stress might have on task performance. Situational awareness is also becoming an integral part of human task performance. It is critical for many operations to design systems such that the effects of physical stress, however minute, on task performance and situational awareness are considered. The test methodology developed here measures the effect of stress on cognitive task performance as a result of situational awareness related to the task. The test measured and compared task capacity among different age groups and different working groups. A comparison was made on task performance based on the effects of low level physical stress and lack of it. Response time and accuracy were measured for statistical analysis. The subject’s stress levels were measured before starting the test to create a baseline for the candidates stress level. The developed tool was able to detect the effect of stress on task performance successfully and efficiently. Subjects with previous work experience performed better both in Phase I and Phase II of the experiment as compared to subjects with no previous work experience. The analysis indicates low level stress does have significant effects on task performance. In reality, stress is an unavoidable factor in daily activities. When designing any system that requires cognitive tasks, stress needs to be considered as a contributing factor to the variability of operation.
Electrical Performance Analysis of a Novel Embedded Chip Technology
(North Dakota State University, 2012) Sarwar, Ferdous
Recently ultra-thin embedded die technology gained much attention for their reduced footprint, light weight, conformality and three-dimensional assembly capabilities. The traditional flexible circuit fabrication process showed its limitations to meet the demand for increasing packaging density. The embedded die technology can be successfully utilized to develop flexible printed circuits that will satisfy the demand for reliable and high density packaging. With a tremendous application potential in wearable and disposable electronics, the reliability of the flexible embedded die package is of paramount importance. Presented is the author's contribution to the novel fabrication process for flexible packages with ultrathin (below 50 µm) dice embedded into organic polymer substrate and the results from the investigation of the electrical performance of embedded bare dice bumped using three different techniques. In this research, embedded flexible microelectronic packaging technology was developed and reliability of different packages was evaluated through JEDEC test standards based on their electrical performance. The reliability test of the developed packages suggested the better and stable performance of stud bump bonded packages. This research also covered the thinning and handling ultra-thin chips, die metallization, stud bump formation, laser ablation of polymers, and assembly of ultra-thin die. The stud bumped flexible packages that were designed and developed in this research have promising application potential in wearable RFID tags, smart textile and three dimensional-stacked packaging, among the many other application areas.
Form and Functionality of Additively Manufactured Parts with Internal Structure
(North Dakota State University, 2019) Ahsan, AMM Nazmul
The tool-less additive manufacturing (AM) or 3D printing processes (3DP) use incremental consolidation of feed-stock materials to construct part. The layer by layer AM processes can achieve spatial material distribution and desired microstructure pattern with high resolution. This unique characteristics of AM can bring custom-made form and tailored functionality within the same object. However, incorporating form and functionality has their own challenge in both design and manufacturing domain. This research focuses on designing manufacturable topology by marrying form and functionality in additively manufactured part using infill structure. To realize the goal, this thesis presents a systematic design framework that focuses on reducing the gap between design and manufacturing of complex architecture. The objective is to develop a design methodology of lattice infill and thin shell structure suitable for additive manufacturing processes. Particularly, custom algorithmic approaches have been developed to adapt the existing porous structural patterns for both interior and exterior of objects considering application specific functionality requirements. The object segmentation and shell perforation methodology proposed in this work ensures manufacturability of large scale thin shell or hollowed objects and incorporates tailored part functionality. Furthermore, a computational design framework developed for tissue scaffold structures incorporates the actual structural heterogeneity of natural bones obtained from their medical images to facilitate the tissue regeneration process. The manufacturability is considered in the design process and the performances are measured after their fabrication. Thus, the present thesis demonstrates how the form of porous structures can be adapted to mingle with functionality requirements of the application as well as fabrication constraints. Also, this work bridges the design framework (virtual) and the manufacturing platform (realization) through intelligent data management which facilitates smooth transition of information between the two ends.
Impact of Integrating Zone Bypass Conveyor on the Performance of a Pick-To-Light Order Picking System
(North Dakota State University, 2012) Xu, Xia
This thesis investigates the impact of integrating Zone Bypass (ZBP) conveyor to a Pick-To-Light (PTL) order picking system. This integration results in a new system (PTL+Z), which could be helpful to achieve higher levels of productivity in warehousing operations. Two options have been proposed to improve the current PTL system productivity. One is to adapt the ZBP conveyor, which will help each order to bypass unnecessary zones with nothing to pick. Another one is to better plan stock keeping units (SKU) assignment by applying level loading assignment. Mathematical models are developed to evaluate system throughput of PTL system with random assignment (PTL/R), PTL system with level loading assignment (PTL/L), PTL+Z system with random assignment (PTL+Z/R), and PTL+Z system with level loading assignment (PTL+Z/L). Simulation models are validated to test the reliability of mathematical models. Also, economic analysis is developed in term of payback period for decision purpose.
Improvement of Wind Forecasting Accuracy and its Impacts on Bidding Strategy Optimization for Wind Generation Companies
(North Dakota State University, 2012) Li, Gong
One major issue of wind generation is its intermittence and uncertainty due to the highly volatile nature of wind resource, and it affects both the economy and the operation of the wind farms and the distribution networks. It is thus urgently needed to develop modeling methods for accurate and reliable forecasts on wind power generation. Meanwhile, along with the ongoing electricity market deregulation and liberalization, wind energy is expected to be directly auctioned in the wholesale market. This brings the wind generation companies another issue of particular importance, i.e., how to maximize the profits by optimizing the bids in the gradually deregulated electricity market based on the improved wind forecasts. As such, the main objective of this dissertation research is to investigate and develop reliable modeling methods for tackling the two issues. To reach the objective, three main research tasks are identified and accomplished. Task 1 is about testing forecasting models for wind speed and power. After a thorough investigation into currently available forecasting methods, several representative models including autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are developed for short-term wind forecasting. The forecasting performances are evaluated and compared in terms of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The results reveal that no single model can outperform others universally. This indicates the need of generating a single robust and reliable forecast by applying a post-processing method. As such, a reliable and adaptive model for short-term forecasting the wind power is developed via adaptive Bayesian model averaging algorithms in Task 2. Experiments are performed for both long-term wind assessment and short-term wind forecasting. The results show that the proposed BMA-based model can always provide adaptive, reliable, and iv comparatively accurate forecast results in terms of MAE, RMSE, and MAPE. It also provides a unified approach to tackle the challenging model selection issue in wind forecasting applications. Task 3 is about developing a modeling method for optimizing the wind power bidding process in the deregulated electricity wholesale market. The optimal bids on wind power must take into account the uncertainty in wind forecasts and wind power generation. This research investigates the application of combining improved wind forecasts with agent-based models to optimize the bid and maximize the net earnings. The WSCC 9-bus 3-machine power system network and the IEEE 30-bus 9-GenCo power system network are adopted. Both single-sided and double-sided auctions are considered. The results demonstrate that improving wind forecasting accuracy helps increase the net earnings of wind generation companies, and that the implementation of agent learning algorithms further improves the earnings. The results also verify that agent-based simulation is a viable modeling tool for providing realistic insights about the complex interactions among different market participants and various market factors.
Integrated Projection and Regression Models for Monitoring Multivariate Autocorrelated Cascade Processes
(North Dakota State University, 2014) Khan, Anakaorn
This dissertation presents a comprehensive methodology of dual monitoring for the multivariate autocorrelated cascade processes using principal component analysis and regression. Principle Components Analysis is used to alleviate the multicollinearity among input process variables and reduce the dimension of the variables. An integrated principal components selection rule is proposed to reduce the number of input variables. An autoregressive time series model is used and imposed on the time correlated output variable which depends on many multicorrelated process input variables. A generalized least squares principal component regression is used to describe the relationship between product and process variables under the autoregressive regression error model. The combined residual based EWMA control chart, applied to the product characteristics, and the MEWMA control charts applied to the multivariate autocorrelated cascade process characteristics, are proposed. The dual EWMA and MEWMA control chart has advantage and capability over the conventional residual type control chart applied to the residuals of the principal component regression by monitoring both product and the process characteristics simultaneously. The EWMA control chart is used to increase the detection performance, especially in the case of small mean shifts. The MEWMA is applied to the selected set of variables from the first principal component with the aim of increasing the sensitivity in detecting process failures. The dual implementation control chart for product and process characteristics enhances both the detection and the prediction performance of the monitoring system of the multivariate autocorrelated cascade processes. The proposed methodology is demonstrated through an example of the sugar-beet pulp drying process. A general guideline for controlling multivariate autocorrelated processes is also developed.
Integration of Simulation and DEA to Determine the Most Efficient Patient Appointment Scheduling Model for a Specific Clinic Setting
(North Dakota State University, 2011) Aslani, Nazanin
This study develops a method to determine the most efficient scheduling model for a specific clinic setting. The appointment scheduling system assigns clinics' timeslots to incoming requests. There are three major scheduling models: centralized scheduling model (CSM), decentralized scheduling model (DSM) and hybrid scheduling model (HSM). In order to schedule multiple appointments, CSM involves one scheduler, DSM involves all the schedulers of individual clinics and HSM combines CSM and DSM. Clinic settings are different in terms of important factors such as randomness of appointment arrival and proportion of multiple appointments. Scheduling systems operate inefficiently if there is not an appropriate match between scheduling models and clinic settings to provide balance between indicators of efficiency. A procedure is developed to determine the most efficient scheduling model by the integrated contribution of simulation and Data Envelopment Analysis (DEA). A case study serves as a guide to use and as proof for the validity of the developed procedure.
Modeling and Optimization of Biofuel Supply Chain Considering Uncertainties, Hedging Strategies, and Sustainability Concepts
(North Dakota State University, 2013) Awudu, Iddrisu Kaasowa
Due to energy crisis and environmental concerns, alternative energy has attracted a lot of attention in both industry and academia. Biofuel is one type of renewable energy that can reduce the reliance on fossil fuel, and also help reduce environmental effect and provide social benefits. However, to deliver a competitive biofuel product requires a robust supply chain. The biofuel supply chain (BSC) consists of raw material sourcing, transporting of raw materials to pre-treatment and biorefinery sites, pre-treating the raw material, biofuel production, and transporting of the produced biofuel to the final demand zones. As uncertainties are involved throughout the supply chain, risks are introduced. We first propose a stochastic production planning model for a biofuel supply chain under demand and price uncertainties. A stochastic linear programming model is proposed and Benders decomposition (BD) with Monte Carlo simulation technique is applied to solve the proposed model. A case study compares the performance of a deterministic model and the proposed stochastic model. The results indicate that the proposed model obtain higher expected profit than the deterministic model under different uncertainty settings. Sensitivity analyses are performed to gain management insights. Secondly, a hedging strategy is proposed in a hybrid generation biofuel supply chain (HGBSC). A hedging strategy can purchase corn either through futures or spot, while the ethanol end-product sale is hedged using futures. A two-stage stochastic linear programming method with hedging strategy is proposed, and a Multi-cut Benders Decomposition Algorithm is used to solve the proposed model. Prices of feedstock and ethanol end-products are modeled as a mean reversion (MR). The results for both hedging and non-hedging are compared for profit realizations, and the hedging is better as compared to non-hedging for smaller profits. Further sensitivity analyses are conducted to provide managerial insights. Finally, sustainability concepts, which include economic, environmental, and social sustainability, are incorporated in the HGBSC. A two-stage stochastic mixed integer linear programming approach is used, and the proposed HGBSC model is solved using the Lagrangean Relaxation (LR) and Sample Average Approximation (SAA). A representative case study in North Dakota is used for this study.
Modeling and Solving Multi-Product Multi-Layer Location-Routing Problems
(North Dakota State University, 2011) Hamidi, Mohsen
Distribution is a very important component of logistics and supply chain management. Location-Routing Problem (LRP) simultaneously takes into consideration location, allocation, and vehicle routing decisions to design an optimal distribution network. Multi-layer and multi-product LRP is even more complex as it deals with the decisions at multiple layers of a distribution network where multiple products are transported within and between layers of the network. This dissertation focuses on modeling and solving complicated four-layer and multi-product LRPs which have not been tackled yet. The four-layer LRP represents a multi-product distribution network consisting of plants, central depots, regional depots, and customers. The LRP integrates location, allocation, vehicle routing, and transshipment problems. Through the modeling phase, the structure, assumptions, and limitations of the distribution network are defined and the mathematical optimization programming model that can be used to obtain optimal solutions is developed. Since the mathematical model can obtain the optimal solution only for small-size problems, through the solving phase metaheuristic algorithms are developed to solve large-size problems. GRASP (Greedy Randomized Adaptive Search Procedure), probabilistic tabu search, local search techniques, the Clarke-Wright Savings algorithm, and a node ejection chains algorithm are combined to solve two versions of the four-layer LRP. Results show that the metaheuristic can solve the problem effectively in terms of computational time and solution quality. The presented four-layer LRP, which considers realistic assumptions and limitations such as producing multiple products, limited plant production capacity, limited depot and vehicle capacity, and limited traveling distances, enables companies to mimic the real world limitations and obtain realistic results. The main objective of this research is to develop solution algorithms that can solve large-size multi-product multi-layer LRPs and produce high-quality solutions in a reasonable amount of time.
Moisture Sensitivity of PLA/PBS Blends During Ultrasonic Welding and Fused Deposition Modeling
(North Dakota State University, 2021) Quader, Raihan
Moisture absorption into hygroscopic/hydrophilic materials used in fused deposition modeling (FDM) and ultrasonic welding (USW) can diminish desired mechanical properties. Sensitivity to moisture is dependent on material properties and environmental factors and needs characterization. In this thesis, moisture sensitivity of PLA filaments and PLA/PBS blended filaments was characterized in FDM printed ASTM test samples post-conditioning the filaments at different relative humidity levels. Tensile strength decreased with increase in moisture content. Parts made with PLA 4043D, PLA/PBS 75/25 filaments were most sensitive to moisture. Investigation of tensile properties of parts made with PLA filaments exposed to room temperature and humidity conditions for three months showed a more significant decrease. Moisture sensitivity of PLA, PBS, and PLA/PBS 25/75 blend characterized for USW using injection-molded industrial standard test parts (ISTeP) showed a downward trend in weld strength for 100% PLA and PLA/PBS 25/75 while 100% PBS was significantly affected at high moisture conditions.
Molecular Dynamics Simulation of Nano-Indentation Process of Silicon: Effects of Initial Temperature and Grain Size
(North Dakota State University, 2014) Wang, Yachao
In this study, a comprehensive investigation on nano-scale indentation of monocrystal and polycrystalline silicon is carried out by adopting molecular dynamic (MD) simulation. Five levels of initial temperature (30K, 100K, 300K, 500K and 700K) are configured in this study and the simulation results reveal the amount of bct-5 silicon atoms at the maximum indentation position is not significantly affected by the initial temperature, substantially less ß-silicon atoms are observed with higher temperatures. The temperature effect on the unloading process is also discussed. Meanwhile, indentation force curves for polycrystalline silicon (grain size ranging from 6.45 nm to 20.48 nm) and single crystalline silicon is compared. The result shows that the normal Hall-Petch effect is not seen in the nano-indentation process of silicon. The grain boundary increases local stress during the indentation process and results in less formation of ß-silicon phase, but it hardly affects the formation of bct-5 silicon.
Multi-Objective Optimal Phasor Measurement Units Placement in Power Systems
(North Dakota State University, 2014) Khiabani, Vahidhossein
The extensive development of power networks has increased the requirements for robust, reliable and secure monitoring and control techniques based on the concept of Wide Area Measurement System (WAMS). Phasor Measurement Units (PMUs) are key elements in WAMS based operations of power systems. Most existing algorithms consider the problem of optimal PMU placement where the main objective is to ensure observability. They consider cost and observability of buses ignoring the reliability aspect of both WAMS and PMUs. Given the twin and conflicting objectives of cost and reliability, this dissertation aims to model and solve a multi-objective optimization formulation that maintains full system observability with minimum cost while exceeding a pre-specified level of reliability of observability. No unique solution exists for these conflicting objectives, hence the model finds the best tradeoffs. Given that the reliability-based PMU placement model is Non-deterministic Polynomial time hard (NP-hard), the mathematical model can only address small problems. This research accomplishes the following: (a) modeling and solving the multi-objective PMU placement model for IEEE standard test systems and its observability, and (b) developing heuristic algorithms to increase the scalability of the model and solve large problems. In short, early consideration of the reliability of observability in the PMU placement problem provides a balanced approach which increases the reliability of the power system overall and reduces the cost of reliability. The findings are helpful to show and understand the effectiveness of the proposed models. However the increased cost associated with the increased reliability would be negligible when considering cost of blackouts to commerce, industry, and society as a whole.

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